diff --git a/.gitignore b/.gitignore index 398bd275..974c8f6e 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ build build-*/ test-results*/ .vs +.vscode .clangd test settings.json diff --git a/CHANGELOG.md b/CHANGELOG.md index 7ddb8902..627d0dcd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,29 @@ ## Changelog +### CUDA 13.4 +* Removed obsolete samples `simpleHyperQ`, `simpleOccupancy`, `mergeSort`, `simpleTemplates`, `template`, `p2pBandwidthLatencyTest`. +* Removed redundant `_nvrtc` sample variants (`clock_nvrtc`, `matrixMul_nvrtc`, `simpleAssert_nvrtc`, `simpleAtomicIntrinsics_nvrtc`, `inlinePTX_nvrtc`, `binomialOptions_nvrtc`, `BlackScholes_nvrtc`, `quasirandomGenerator_nvrtc`); `vectorAdd_nvrtc` remains as the canonical NVRTC example. +* Rewrote `cpp/0_Introduction/simpleMultiGPU` to replace the hand-written reduction with `cub::BlockReduce` and the SDK stopwatch timer with CUDA events, removed helper header dependencies and merged `simpleMultiGPU.h` into the `.cu` for a single-file sample, modernized the code to C++17 (`std::vector`, `constexpr`), and added a minimum two-GPU check; updated README accordingly. +* Modernized `cpp/3_CUDA_Features/simpleCudaGraphs`: dropped `helper_cuda.h`/`cooperative_groups.h`; used `cub::BlockReduce`; migrated to the unified `cudaGraphAddNode` API; split into `simpleCudaGraphs_explicit` and `simpleCudaGraphs_capture` sharing `simpleCudaGraphs.cuh`; added a graph-reuse demo (fresh input each launch); rewrote README. +* Rewrote `cpp/0_Introduction/simpleAtomicIntrinsics` to show race conditions by comparing non-atomic vs atomic kernels (`atomicAdd`, `atomicMax`, `atomicCAS`), removed helper header dependencies; updated README accordingly. +* Reorganized CMake build system for all C++ samples: centralized GPU architecture detection (`DetectCudaArch.cmake`) and common build setup (`CudaSampleCommon.cmake`) into shared modules. Samples with restricted arch support now declare a `SAMPLE_DISALLOW_ARCHS` list instead of hardcoding their own arch list, and are gracefully skipped at configure time when the requested arch is unsupported. +* Added `sm_103` to the default set of GPU architectures the C++ samples build for. +* Added `sm_107` to the default set of GPU architectures the C++ samples build for. +* Rewrote `cpp/0_Introduction/simpleCallback` to replace the deprecated `cudaStreamAddCallback` with `cudaLaunchHostFunc`, replaced the `multithreading.h/.cpp` helpers with standard C++ `std::thread`, and simplified to a single-GPU, single-workload C++17 example; updated README accordingly. +* Rewrote `cpp/0_Introduction/simpleStreams` to simplify code and remove helper header dependencies; updated README accordingly. +* Added `python/2_CoreConcepts/persistentProgramCache` to demonstrate persisting and reusing compiled CUDA artifacts with `cuda.core` and `FileStreamProgramCache`. +* Updated `cpp/0_Introduction/clock` to replace the manual shared-memory tree reduction with `cub::BlockReduce` min-reduction, removed dependency on `helper_cuda.h` and `helper_functions.h`, and updated README. +* Rewrote `cpp/0_Introduction/vectorAdd` to match the corresponding example in the CUDA Programming Guide. The sample now uses Unified Memory (`cudaMallocManaged`) instead of explicit `cudaMalloc`/`cudaMemcpy`. +* Restructured the `cpp/0_Introduction/simplePrintf` README with newer style and updated sample code by removing helper dependency. +* Updated the `cpp/0_Introduction/simpleAssert` README to newer style, simplified code for better understanding. +* Added `cpp/3_CUDA_Features/dmabufInterop` - a Linux-only CUDA dma-buf interoperability sample demonstrating three scenarios in a single binary: (1) same-process round-trip export/import, (2) cross-process IPC via `fork()` + `SCM_RIGHTS`, and (3) cross-GPU sharing across processes (self-skips on systems without a qualifying GPU pair). +* Removed the unnecessary `set(CMAKE_POSITION_INDEPENDENT_CODE ON)` from the C++ sample `CMakeLists.txt` files. +* Removed `CUDA_SEPARABLE_COMPILATION ON` from C++ samples that don't require relocatable device code (kept for the CDP samples). +* Removed 26 math library samples from `cpp/4_CUDA_Libraries/` that are maintained in the [CUDA Library Samples](https://github.com/NVIDIA/CUDALibrarySamples) repository: cuBLAS (`simpleCUBLAS`, `simpleCUBLAS_LU`, `simpleCUBLASXT`, `matrixMulCUBLAS`, `batchCUBLAS`), cuFFT (`simpleCUFFT`, `simpleCUFFT_MGPU`, `simpleCUFFT_2d_MGPU`, `simpleCUFFT_callback`), cuRAND (`MersenneTwisterGP11213`), cuSOLVER (`cuSolverDn_LinearSolver`, `cuSolverRf`, `cuSolverSp_LinearSolver`, `cuSolverSp_LowlevelCholesky`, `cuSolverSp_LowlevelQR`), cuSPARSE (`conjugateGradient`, `conjugateGradientPrecond`, `conjugateGradientUM`), NPP (`boxFilterNPP`, `cannyEdgeDetectorNPP`, `FilterBorderControlNPP`, `freeImageInteropNPP`, `histEqualizationNPP`, `watershedSegmentationNPP`), and nvJPEG (`nvJPEG`, `nvJPEG_encoder`). +* Lifted the LLVM 14 upper bound on `cpp/7_libNVVM/cuda-c-linking`, which now builds against LLVM 15 and newer. +* Added samples for locality domains in `cpp/3_CUDA_FEATURES/localityDomains` and `cpp/3_CUDA_FEATURES/localityDomainsDrv`. +* Added Windows on Arm support: `cpp/5_Domain_Specific/marchingCubes` now detects the `glew32` GLEW library name used on Windows on Arm, and `cpp/7_libNVVM` searches the `nvvm/lib/arm64` directory for libNVVM. + ### CUDA 13.3 * Added **CUDA Tile C++** samples under `cpp/9_CUDA_Tile`. * Added a set of **CCCL 3.3 feature samples** under `cpp/4_CUDA_Libraries/`, each built against CCCL fetched via CPM (pinned to v3.3.3, with an optional `CCCL_SOURCE_DIR` override): diff --git a/CMakeLists.txt b/CMakeLists.txt index bf040754..1fafbd80 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,33 +1,31 @@ cmake_minimum_required(VERSION 3.20) +include(cmake/CudaSampleArchs.cmake) + +# Default to all supported architectures if the user does not specify. +# Gated on CUDA_SAMPLES_ARCHS_DEFAULTED rather than CMAKE_CUDA_ARCHITECTURES so this +# decision is made once (on the first configure) and does not flip on reconfigure, +# when CMAKE_CUDA_ARCHITECTURES is already defined from our own cached default. +# The decision is revisited when the list itself moves, which the user can do with -D +# on any later configure of the same build directory. Keep the list each configure +# ends up with, so that such a change is told apart from our own cached default. +if(NOT DEFINED CUDA_SAMPLES_ARCHS_DEFAULTED) + if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + list(JOIN CUDA_SAMPLES_MASTER_ARCHS ";" _default_archs) + set(CMAKE_CUDA_ARCHITECTURES "${_default_archs}" CACHE STRING "CUDA architectures") + unset(_default_archs) + set(CUDA_SAMPLES_ARCHS_DEFAULTED TRUE CACHE INTERNAL "") + else() + set(CUDA_SAMPLES_ARCHS_DEFAULTED FALSE CACHE INTERNAL "") + endif() +elseif(NOT "${CMAKE_CUDA_ARCHITECTURES}" STREQUAL "${CUDA_SAMPLES_LAST_CONFIGURED_ARCHS}") + set(CUDA_SAMPLES_ARCHS_DEFAULTED FALSE CACHE INTERNAL "") +endif() + +set(CUDA_SAMPLES_LAST_CONFIGURED_ARCHS "${CMAKE_CUDA_ARCHITECTURES}" CACHE INTERNAL "") + project(cuda-samples LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CXX_STANDARD 17) -set(CMAKE_CXX_STANDARD_REQUIRED ON) - -set(CMAKE_CUDA_STANDARD 17) -set(CMAKE_CUDA_STANDARD_REQUIRED ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --extended-lambda") - -# Add MSVC-specific flags for standard-conforming preprocessor (required for CCCL) -if(MSVC) - add_compile_options($<$:-Xcompiler=/Zc:preprocessor>) -endif() - -# Include installation configuration before processing samples -include(cmake/InstallSamples.cmake) +include(cmake/CudaSampleCommon.cmake) add_subdirectory(cpp) diff --git a/Common/UtilNPP/Exceptions.h b/Common/UtilNPP/Exceptions.h deleted file mode 100644 index e0e542e0..00000000 --- a/Common/UtilNPP/Exceptions.h +++ /dev/null @@ -1,197 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_EXCEPTIONS_H -#define NV_UTIL_NPP_EXCEPTIONS_H - - -#include -#include -#include - -/// All npp related C++ classes are put into the npp namespace. -namespace npp -{ - - /// Exception base class. - /// This exception base class will be used for everything C++ throught - /// the NPP project. - /// The exception contains a string message, as well as data fields for a string - /// containing the name of the file as well as the line number where the exception was thrown. - /// The easiest way of throwing exceptions and providing filename and line number is - /// to use one of the ASSERT macros defined for that purpose. - class Exception - { - public: - /// Constructor. - /// \param rMessage A message with information as to why the exception was thrown. - /// \param rFileName The name of the file where the exception was thrown. - /// \param nLineNumber Line number in the file where the exception was thrown. - explicit - Exception(const std::string &rMessage = "", const std::string &rFileName = "", unsigned int nLineNumber = 0) - : sMessage_(rMessage), sFileName_(rFileName), nLineNumber_(nLineNumber) - { }; - - Exception(const Exception &rException) - : sMessage_(rException.sMessage_), sFileName_(rException.sFileName_), nLineNumber_(rException.nLineNumber_) - { }; - - virtual - ~Exception() - { }; - - /// Get the exception's message. - const - std::string & - message() - const - { - return sMessage_; - } - - /// Get the exception's file info. - const - std::string & - fileName() - const - { - return sFileName_; - } - - /// Get the exceptions's line info. - unsigned int - lineNumber() - const - { - return nLineNumber_; - } - - - /// Create a clone of this exception. - /// This creates a new Exception object on the heap. It is - /// the responsibility of the user of this function to free this memory - /// (delete x). - virtual - Exception * - clone() - const - { - return new Exception(*this); - } - - /// Create a single string with all the exceptions information. - /// The virtual toString() method is used by the operator<<() - /// so that all exceptions derived from this base-class can print - /// their full information correctly even if a reference to their - /// exact type is not had at the time of printing (i.e. the basic - /// operator<<() is used). - virtual - std::string - toString() - const - { - std::ostringstream oOutputString; - oOutputString << fileName() << ":" << lineNumber() << ": " << message(); - return oOutputString.str(); - } - - private: - std::string sMessage_; ///< Message regarding the cause of the exception. - std::string sFileName_; ///< Name of the file where the exception was thrown. - unsigned int nLineNumber_; ///< Line number in the file where the exception was thrown - }; - - /// Output stream inserter for Exception. - /// \param rOutputStream The stream the exception information is written to. - /// \param rException The exception that's being written. - /// \return Reference to the output stream being used. - std::ostream & - operator << (std::ostream &rOutputStream, const Exception &rException) - { - rOutputStream << rException.toString(); - return rOutputStream; - } - - /// Basic assert macro. - /// This macro should be used to enforce any kind of pre or post conditions. - /// Unlike the C-runtime assert macro, this macro does not abort execution, but throws - /// a C++ exception. The exception is automatically filled with information about the failing - /// condition, the filename and line number where the exception was thrown. - /// \note The macro is written in such a way that omitting a semicolon after its usage - /// causes a compiler error. The correct way to invoke this macro is: - /// NPP_ASSERT(n < MAX); -#define NPP_ASSERT(C) do {if (!(C)) throw npp::Exception(#C " assertion faild!", __FILE__, __LINE__);} while(false) - - // ASSERT macro. - // Same functionality as the basic assert macro with the added ability to pass - // a message M. M should be a string literal. - // Note: Never use code inside ASSERT() that causes a side-effect ASSERT macros may get compiled - // out in release mode. -#define NPP_ASSERT_MSG(C, M) do {if (!(C)) throw npp::Exception(#C " assertion faild! Message: " M, __FILE__, __LINE__);} while(false) - -#ifdef _DEBUG - /// Basic debug assert macro. - /// This macro is identical in every respect to NPP_ASSERT(C) but it does get compiled to a - /// no-op in release builds. It is therefor of utmost importance to not put statements into - /// this macro that cause side effects required for correct program execution. -#define NPP_DEBUG_ASSERT(C) do {if (!(C)) throw npp::Exception(#C " debug assertion faild!", __FILE__, __LINE__);} while(false) -#else -#define NPP_DEBUG_ASSERT(C) -#endif - - /// ASSERT for null-pointer test. - /// It is safe to put code with side effects into this macro. Also: This macro never - /// gets compiled to a no-op because resource allocation may fail based on external causes not under - /// control of a software developer. -#define NPP_ASSERT_NOT_NULL(P) do {if ((P) == 0) throw npp::Exception(#P " not null assertion faild!", __FILE__, __LINE__);} while(false) - - /// Macro for flagging methods as not implemented. - /// The macro throws an exception with a message that an implementation was missing -#define NPP_NOT_IMPLEMENTED() do {throw npp::Exception("Implementation missing!", __FILE__, __LINE__);} while(false) - - /// Macro for checking error return code of CUDA (runtime) calls. - /// This macro never gets disabled. -#define NPP_CHECK_CUDA(S) do {cudaError_t eCUDAResult; \ - eCUDAResult = S; \ - if (eCUDAResult != cudaSuccess) std::cout << "NPP_CHECK_CUDA - eCUDAResult = " << eCUDAResult << std::endl; \ - NPP_ASSERT(eCUDAResult == cudaSuccess);} while (false) - - /// Macro for checking error return code for NPP calls. -#define NPP_CHECK_NPP(S) do {NppStatus eStatusNPP; \ - eStatusNPP = S; \ - if (eStatusNPP != NPP_SUCCESS) std::cout << "NPP_CHECK_NPP - eStatusNPP = " << _cudaGetErrorEnum(eStatusNPP) << "("<< eStatusNPP << ")" << std::endl; \ - NPP_ASSERT(eStatusNPP == NPP_SUCCESS);} while (false) - - /// Macro for checking error return codes from cuFFT calls. -#define NPP_CHECK_CUFFT(S) do {cufftResult eCUFFTResult; \ - eCUFFTResult = S; \ - if (eCUFFTResult != NPP_SUCCESS) std::cout << "NPP_CHECK_CUFFT - eCUFFTResult = " << eCUFFTResult << std::endl; \ - NPP_ASSERT(eCUFFTResult == CUFFT_SUCCESS);} while (false) - -} // npp namespace - -#endif // NV_UTIL_NPP_EXCEPTIONS_H diff --git a/Common/UtilNPP/Image.h b/Common/UtilNPP/Image.h deleted file mode 100644 index a80630f7..00000000 --- a/Common/UtilNPP/Image.h +++ /dev/null @@ -1,155 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGE_H -#define NV_UTIL_NPP_IMAGE_H - -#include - -namespace npp -{ - - class Image - { - public: - struct Size - { - unsigned int nWidth; - unsigned int nHeight; - - Size() : nWidth(0), nHeight(0) - { }; - - Size(unsigned int nWidthNew, unsigned nHeightNew) : nWidth(nWidthNew), nHeight(nHeightNew) - { }; - - Size(const Size &rSize) : nWidth(rSize.nWidth), nHeight(rSize.nHeight) - { }; - - Size & - operator= (const Size &rSize) - { - if (&rSize == this) - { - return *this; - } - - nWidth = rSize.nWidth; - nHeight = rSize.nHeight; - - return *this; - } - - void - swap(Size &rSize) - { - unsigned int nTemp; - nTemp = nWidth; - nWidth = rSize.nWidth; - rSize.nWidth = nTemp; - - nTemp = nHeight; - nHeight = rSize.nHeight; - rSize.nHeight = nTemp; - } - }; - - Image() - { }; - - Image(unsigned int nWidth, unsigned int nHeight) : oSize_(nWidth, nHeight) - { }; - - Image(const Image::Size &rSize) : oSize_(rSize) - { }; - - Image(const Image &rImage) : oSize_(rImage.oSize_) - { }; - - virtual - ~Image() - { }; - - Image & - operator= (const Image &rImage) - { - if (&rImage == this) - { - return *this; - } - - oSize_ = rImage.oSize_; - return *this; - }; - - unsigned int - width() - const - { - return oSize_.nWidth; - } - - unsigned int - height() - const - { - return oSize_.nHeight; - } - - Size - size() - const - { - return oSize_; - } - - void - swap(Image &rImage) - { - oSize_.swap(rImage.oSize_); - } - - private: - Size oSize_; - }; - - bool - operator== (const Image::Size &rFirst, const Image::Size &rSecond) - { - return rFirst.nWidth == rSecond.nWidth && rFirst.nHeight == rSecond.nHeight; - } - - bool - operator!= (const Image::Size &rFirst, const Image::Size &rSecond) - { - return rFirst.nWidth != rSecond.nWidth || rFirst.nHeight != rSecond.nHeight; - } - -} // npp namespace - - -#endif // NV_UTIL_NPP_IMAGE_H diff --git a/Common/UtilNPP/ImageAllocatorsCPU.h b/Common/UtilNPP/ImageAllocatorsCPU.h deleted file mode 100644 index b5e73bc0..00000000 --- a/Common/UtilNPP/ImageAllocatorsCPU.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGE_ALLOCATORS_CPU_H -#define NV_UTIL_NPP_IMAGE_ALLOCATORS_CPU_H - -#include "Exceptions.h" - -namespace npp -{ - - template - class ImageAllocatorCPU - { - public: - static - D * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch) - { - NPP_ASSERT(nWidth * nHeight > 0); - - D *pResult = new D[nWidth * N * nHeight]; - *pPitch = nWidth * sizeof(D) * N; - - return pResult; - }; - - static - void - Free2D(D *pPixels) - { - delete[] pPixels; - }; - - static - void - Copy2D(D *pDst, size_t nDstPitch, const D *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - const void *pSrcLine = pSrc; - void *pDstLine = pDst; - - for (size_t iLine = 0; iLine < nHeight; ++iLine) - { - // copy one line worth of data - memcpy(pDst, pSrc, nWidth * N * sizeof(D)); - // move data pointers to next line - pDst += nDstPitch; - pSrc += nSrcPitch; - } - }; - - }; - -} // npp namespace - -#endif // NV_UTIL_NPP_IMAGE_ALLOCATORS_CPU_H diff --git a/Common/UtilNPP/ImageAllocatorsNPP.h b/Common/UtilNPP/ImageAllocatorsNPP.h deleted file mode 100644 index 6483e769..00000000 --- a/Common/UtilNPP/ImageAllocatorsNPP.h +++ /dev/null @@ -1,1139 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGE_ALLOCATORS_NPP_H -#define NV_UTIL_NPP_IMAGE_ALLOCATORS_NPP_H - -#include "Exceptions.h" - -#include -#include - -namespace npp -{ - template - D * - MallocTightCUDA(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch) - { - D *pResult; - *pPitch = nWidth * sizeof(D) * N; - NPP_CHECK_CUDA(cudaMalloc(&pResult, *pPitch * nHeight)); - NPP_ASSERT_NOT_NULL(pResult); - - return pResult; - } - - - template - class ImageAllocator - { - }; - - template<> - class ImageAllocator - { - public: - static - Npp8u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp8u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_8u_C1(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp8u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp8u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp8u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp8u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_8u_C2(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp8u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp8u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp8u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp8u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_8u_C3(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp8u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp8u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp8u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp8u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_8u_C4(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp8u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp8u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp8u *pDst, size_t nDstPitch, const Npp8u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp8u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16u_C1(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16u_C2(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - - template<> - class ImageAllocator - { - public: - static - Npp16u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16u_C3(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp16u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16u * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16u *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16u_C4(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16u *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16u), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16u *pDst, size_t nDstPitch, const Npp16u *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16u), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16s_C1(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16s_C2(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp16s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp16s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_16s_C4(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp16s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp16s *pDst, size_t nDstPitch, const Npp16s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp16s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32s_C1(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32s_C3(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32s * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32s *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32s_C4(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32s *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32s), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32s *pDst, size_t nDstPitch, const Npp32s *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32s), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32f * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32f *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32f_C1(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32f *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32f), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32f * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32f *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32f_C2(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32f *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp32f), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 2 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32f * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32f *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32f_C3(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32f *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32f), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 3 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class ImageAllocator - { - public: - static - Npp32f * - Malloc2D(unsigned int nWidth, unsigned int nHeight, unsigned int *pPitch, bool bTight = false) - { - NPP_ASSERT(nWidth * nHeight > 0); - - Npp32f *pResult = 0; - - if (bTight) - { - pResult = MallocTightCUDA(nWidth, nHeight, pPitch); - } - else - { - pResult = nppiMalloc_32f_C4(nWidth, nHeight, reinterpret_cast(pPitch)); - NPP_ASSERT(pResult != 0); - } - - return pResult; - }; - - static - void - Free2D(Npp32f *pPixels) - { - nppiFree(pPixels); - }; - - static - void - Copy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32f), nHeight, cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy2D(Npp32f *pDst, size_t nDstPitch, const Npp32f *pSrc, size_t nSrcPitch, size_t nWidth, size_t nHeight) - { - cudaError_t eResult; - eResult = cudaMemcpy2D(pDst, nDstPitch, pSrc, nSrcPitch, nWidth * 4 * sizeof(Npp32f), nHeight, cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - -} // npp namespace - -#endif // NV_UTIL_NPP_IMAGE_ALLOCATORS_NPP_H diff --git a/Common/UtilNPP/ImageIO.h b/Common/UtilNPP/ImageIO.h deleted file mode 100644 index 75e1e37c..00000000 --- a/Common/UtilNPP/ImageIO.h +++ /dev/null @@ -1,149 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGE_IO_H -#define NV_UTIL_NPP_IMAGE_IO_H - -#include "ImagesCPU.h" -#include "ImagesNPP.h" - -#include "FreeImage.h" -#include "Exceptions.h" - -#include -#include "string.h" - - -// Error handler for FreeImage library. -// In case this handler is invoked, it throws an NPP exception. -void -FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif, const char *zMessage) -{ - throw npp::Exception(zMessage); -} - -namespace npp -{ - // Load a gray-scale image from disk. - void - loadImage(const std::string &rFileName, ImageCPU_8u_C1 &rImage) - { - // set your own FreeImage error handler - FreeImage_SetOutputMessage(FreeImageErrorHandler); - - FREE_IMAGE_FORMAT eFormat = FreeImage_GetFileType(rFileName.c_str()); - - // no signature? try to guess the file format from the file extension - if (eFormat == FIF_UNKNOWN) - { - eFormat = FreeImage_GetFIFFromFilename(rFileName.c_str()); - } - - NPP_ASSERT(eFormat != FIF_UNKNOWN); - // check that the plugin has reading capabilities ... - FIBITMAP *pBitmap; - - if (FreeImage_FIFSupportsReading(eFormat)) - { - pBitmap = FreeImage_Load(eFormat, rFileName.c_str()); - } - - NPP_ASSERT(pBitmap != 0); - // make sure this is an 8-bit single channel image - NPP_ASSERT(FreeImage_GetColorType(pBitmap) == FIC_MINISBLACK); - NPP_ASSERT(FreeImage_GetBPP(pBitmap) == 8); - - // create an ImageCPU to receive the loaded image data - ImageCPU_8u_C1 oImage(FreeImage_GetWidth(pBitmap), FreeImage_GetHeight(pBitmap)); - - // Copy the FreeImage data into the new ImageCPU - unsigned int nSrcPitch = FreeImage_GetPitch(pBitmap); - const Npp8u *pSrcLine = FreeImage_GetBits(pBitmap) + nSrcPitch * (FreeImage_GetHeight(pBitmap) -1); - Npp8u *pDstLine = oImage.data(); - unsigned int nDstPitch = oImage.pitch(); - - for (size_t iLine = 0; iLine < oImage.height(); ++iLine) - { - memcpy(pDstLine, pSrcLine, oImage.width() * sizeof(Npp8u)); - pSrcLine -= nSrcPitch; - pDstLine += nDstPitch; - } - - // swap the user given image with our result image, effecively - // moving our newly loaded image data into the user provided shell - oImage.swap(rImage); - } - - // Save an gray-scale image to disk. - void - saveImage(const std::string &rFileName, const ImageCPU_8u_C1 &rImage) - { - // create the result image storage using FreeImage so we can easily - // save - FIBITMAP *pResultBitmap = FreeImage_Allocate(rImage.width(), rImage.height(), 8 /* bits per pixel */); - NPP_ASSERT_NOT_NULL(pResultBitmap); - unsigned int nDstPitch = FreeImage_GetPitch(pResultBitmap); - Npp8u *pDstLine = FreeImage_GetBits(pResultBitmap) + nDstPitch * (rImage.height()-1); - const Npp8u *pSrcLine = rImage.data(); - unsigned int nSrcPitch = rImage.pitch(); - - for (size_t iLine = 0; iLine < rImage.height(); ++iLine) - { - memcpy(pDstLine, pSrcLine, rImage.width() * sizeof(Npp8u)); - pSrcLine += nSrcPitch; - pDstLine -= nDstPitch; - } - - // now save the result image - bool bSuccess; - bSuccess = FreeImage_Save(FIF_PGM, pResultBitmap, rFileName.c_str(), 0) == TRUE; - NPP_ASSERT_MSG(bSuccess, "Failed to save result image."); - } - - // Load a gray-scale image from disk. - void - loadImage(const std::string &rFileName, ImageNPP_8u_C1 &rImage) - { - ImageCPU_8u_C1 oImage; - loadImage(rFileName, oImage); - ImageNPP_8u_C1 oResult(oImage); - rImage.swap(oResult); - } - - // Save an gray-scale image to disk. - void - saveImage(const std::string &rFileName, const ImageNPP_8u_C1 &rImage) - { - ImageCPU_8u_C1 oHostImage(rImage.size()); - // copy the device result data - rImage.copyTo(oHostImage.data(), oHostImage.pitch()); - saveImage(rFileName, oHostImage); - } -} - - -#endif // NV_UTIL_NPP_IMAGE_IO_H diff --git a/Common/UtilNPP/ImagePacked.h b/Common/UtilNPP/ImagePacked.h deleted file mode 100644 index ee4c1f95..00000000 --- a/Common/UtilNPP/ImagePacked.h +++ /dev/null @@ -1,171 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGE_PACKED_H -#define NV_UTIL_NPP_IMAGE_PACKED_H - -#include "Image.h" -#include "Pixel.h" - -namespace npp -{ - template - class ImagePacked: public npp::Image - { - public: - typedef npp::Pixel tPixel; - typedef D tData; - static const size_t gnChannels = N; - typedef npp::Image::Size tSize; - - ImagePacked(): aPixels_(0) - , nPitch_(0) - { - ; - } - - ImagePacked(unsigned int nWidth, unsigned int nHeight): Image(nWidth, nHeight) - , aPixels_(0) - , nPitch_(0) - { - aPixels_ = A::Malloc2D(width(), height(), &nPitch_); - } - - ImagePacked(unsigned int nWidth, unsigned int nHeight, bool bTight): Image(nWidth, nHeight) - , aPixels_(0) - , nPitch_(0) - { - aPixels_ = A::Malloc2D(width(), height(), &nPitch_, bTight); - } - - ImagePacked(const tSize &rSize): Image(rSize) - , aPixels_(0) - , nPitch_(0) - { - aPixels_ = A::Malloc2D(width(), height(), &nPitch_); - } - - ImagePacked(const ImagePacked &rImage): Image(rImage) - , aPixels_(0) - , nPitch_(rImage.pitch()) - { - aPixels_ = A::Malloc2D(width(), height(), &nPitch_); - A::Copy2D(aPixels_, nPitch_, rImage.pixels(), rImage.pitch(), width(), height()); - } - - virtual - ~ImagePacked() - { - A::Free2D(aPixels_); - } - - ImagePacked & - operator= (const ImagePacked &rImage) - { - // in case of self-assignment - if (&rImage == this) - { - return *this; - } - - A::Free2D(aPixels_); - aPixels_ = 0; - nPitch_ = 0; - - // assign parent class's data fields (width, height) - Image::operator =(rImage); - - aPixels_ = A::Malloc2D(width(), height(), &nPitch_); - A::Copy2D(aPixels_, nPitch_, rImage.data(), rImage.pitch(), width(), height()); - - return *this; - } - - unsigned int - pitch() - const - { - return nPitch_; - } - - /// Get a pointer to the pixel array. - /// The result pointer can be offset to pixel at position (x, y) and - /// even negative offsets are allowed. - /// \param nX Horizontal pointer/array offset. - /// \param nY Vertical pointer/array offset. - /// \return Pointer to the pixel array (or first pixel in array with coordinates (nX, nY). - tPixel * - pixels(int nX = 0, int nY = 0) - { - return reinterpret_cast(reinterpret_cast(aPixels_) + nY * pitch() + nX * gnChannels * sizeof(D)); - } - - const - tPixel * - pixels(int nX = 0, int nY = 0) - const - { - return reinterpret_cast(reinterpret_cast(aPixels_) + nY * pitch() + nX * gnChannels * sizeof(D)); - } - - D * - data(int nX = 0, int nY = 0) - { - return reinterpret_cast(pixels(nX, nY)); - } - - const - D * - data(int nX = 0, int nY = 0) - const - { - return reinterpret_cast(pixels(nX, nY)); - } - - void - swap(ImagePacked &rImage) - { - Image::swap(rImage); - - tData *aTemp = aPixels_; - aPixels_ = rImage.aPixels_; - rImage.aPixels_ = aTemp; - - unsigned int nTemp = nPitch_; - nPitch_ = rImage.nPitch_; - rImage.nPitch_ = nTemp; - } - - private: - D *aPixels_; - unsigned int nPitch_; - }; - -} // npp namespace - - -#endif // NV_IMAGE_IPP_H diff --git a/Common/UtilNPP/ImagesCPU.h b/Common/UtilNPP/ImagesCPU.h deleted file mode 100644 index d55e0955..00000000 --- a/Common/UtilNPP/ImagesCPU.h +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef NV_UTIL_NPP_IMAGES_CPU_H -#define NV_UTIL_NPP_IMAGES_CPU_H - -#include "ImagePacked.h" - -#include "ImageAllocatorsCPU.h" -#include "Exceptions.h" - -#include - - -namespace npp -{ - - template - class ImageCPU: public npp::ImagePacked - { - public: - - ImageCPU() - { - ; - } - - ImageCPU(unsigned int nWidth, unsigned int nHeight): ImagePacked(nWidth, nHeight) - { - ; - } - - explicit - ImageCPU(const npp::Image::Size &rSize): ImagePacked(rSize) - { - ; - } - - ImageCPU(const ImageCPU &rImage): Image(rImage) - { - ; - } - - virtual - ~ImageCPU() - { - ; - } - - ImageCPU & - operator= (const ImageCPU &rImage) - { - ImagePacked::operator= (rImage); - - return *this; - } - - npp::Pixel & - operator()(unsigned int iX, unsigned int iY) - { - return *ImagePacked::pixels(iX, iY); - } - - npp::Pixel - operator()(unsigned int iX, unsigned int iY) - const - { - return *ImagePacked::pixels(iX, iY); - } - - }; - - - typedef ImageCPU > ImageCPU_8u_C1; - typedef ImageCPU > ImageCPU_8u_C2; - typedef ImageCPU > ImageCPU_8u_C3; - typedef ImageCPU > ImageCPU_8u_C4; - - typedef ImageCPU > ImageCPU_16u_C1; - typedef ImageCPU > ImageCPU_16u_C3; - typedef ImageCPU > ImageCPU_16u_C4; - - typedef ImageCPU > ImageCPU_16s_C1; - typedef ImageCPU > ImageCPU_16s_C3; - typedef ImageCPU > ImageCPU_16s_C4; - - typedef ImageCPU > ImageCPU_32s_C1; - typedef ImageCPU > ImageCPU_32s_C3; - typedef ImageCPU > ImageCPU_32s_C4; - - typedef ImageCPU > ImageCPU_32f_C1; - typedef ImageCPU > ImageCPU_32f_C3; - typedef ImageCPU > ImageCPU_32f_C4; - -} // npp namespace - -#endif // NV_IMAGE_IPP_H diff --git a/Common/UtilNPP/ImagesNPP.h b/Common/UtilNPP/ImagesNPP.h deleted file mode 100644 index 2cdb18ad..00000000 --- a/Common/UtilNPP/ImagesNPP.h +++ /dev/null @@ -1,149 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_IMAGES_NPP_H -#define NV_UTIL_NPP_IMAGES_NPP_H - -#include "Exceptions.h" -#include "ImagePacked.h" - -#include "ImageAllocatorsNPP.h" -#include - -namespace npp -{ - // forward declaration - template class ImageCPU; - - template - class ImageNPP: public npp::ImagePacked > - { - public: - ImageNPP() - { - ; - } - - ImageNPP(unsigned int nWidth, unsigned int nHeight, bool bTight = false): ImagePacked >(nWidth, nHeight, bTight) - { - ; - } - - ImageNPP(const npp::Image::Size &rSize): ImagePacked >(rSize) - { - ; - } - - ImageNPP(const ImageNPP &rImage): Image(rImage) - { - ; - } - - template - explicit - ImageNPP(const ImageCPU &rImage, bool bTight = false): ImagePacked >(rImage.width(), rImage.height(), bTight) - { - npp::ImageAllocator::HostToDeviceCopy2D(ImagePacked >::data(), - ImagePacked >::pitch(), - rImage.data(), - rImage.pitch(), - ImagePacked >::width(), - ImagePacked >::height()); - } - - virtual - ~ImageNPP() - { - ; - } - - ImageNPP & - operator= (const ImageNPP &rImage) - { - ImagePacked >::operator= (rImage); - - return *this; - } - - void - copyTo(D *pData, unsigned int nPitch) - const - { - NPP_ASSERT((ImagePacked >::width() * sizeof(npp::Pixel) <= nPitch)); - npp::ImageAllocator::DeviceToHostCopy2D(pData, - nPitch, - ImagePacked >::data(), - ImagePacked >::pitch(), - ImagePacked >::width(), - ImagePacked >::height()); - } - - void - copyFrom(D *pData, unsigned int nPitch) - { - NPP_ASSERT((ImagePacked >::width() * sizeof(npp::Pixel) <= nPitch)); - npp::ImageAllocator::HostToDeviceCopy2D(ImagePacked >::data(), - ImagePacked >::pitch(), - pData, - nPitch, - ImagePacked >::width(), - ImagePacked >::height()); - } - }; - - typedef ImageNPP ImageNPP_8u_C1; - typedef ImageNPP ImageNPP_8u_C2; - typedef ImageNPP ImageNPP_8u_C3; - typedef ImageNPP ImageNPP_8u_C4; - - typedef ImageNPP ImageNPP_16u_C1; - typedef ImageNPP ImageNPP_16u_C2; - typedef ImageNPP ImageNPP_16u_C3; - typedef ImageNPP ImageNPP_16u_C4; - - typedef ImageNPP ImageNPP_16s_C1; - typedef ImageNPP ImageNPP_16s_C3; - typedef ImageNPP ImageNPP_16s_C4; - - typedef ImageNPP ImageNPP_32s_C1; - typedef ImageNPP ImageNPP_32s_C3; - typedef ImageNPP ImageNPP_32s_C4; - - typedef ImageNPP ImageNPP_32f_C1; - typedef ImageNPP ImageNPP_32f_C2; - typedef ImageNPP ImageNPP_32f_C3; - typedef ImageNPP ImageNPP_32f_C4; - - typedef ImageNPP ImageNPP_64f_C1; - typedef ImageNPP ImageNPP_64f_C2; - typedef ImageNPP ImageNPP_64f_C3; - typedef ImageNPP ImageNPP_64f_C4; - -} // npp namespace - -#endif // NV_UTIL_NPP_IMAGES_NPP_H diff --git a/Common/UtilNPP/Pixel.h b/Common/UtilNPP/Pixel.h deleted file mode 100644 index 69f9ca6d..00000000 --- a/Common/UtilNPP/Pixel.h +++ /dev/null @@ -1,126 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_PIXEL_H -#define NV_UTIL_PIXEL_H - -#include "Exceptions.h" - -namespace npp -{ - template - struct Pixel - { }; - - template - struct Pixel - { - D x; - - const D & - operator[](size_t iChannel) - const - { - NPP_ASSERT(iChannel < 1); - return (&x)[iChannel]; - } - - D & - operator[](size_t iChannel) - { - NPP_ASSERT(iChannel < 1); - return (&x)[iChannel]; - } - }; - - template - struct Pixel - { - D x,y; - - const D & - operator[](size_t iChannel) - const - { - NPP_ASSERT(iChannel < 2); - return (&x)[iChannel]; - } - - D & - operator[](size_t iChannel) - { - NPP_ASSERT(iChannel < 2); - return (&x)[iChannel]; - } - }; - - template - struct Pixel - { - D x,y,z; - - const D & - operator[](size_t iChannel) - const - { - NPP_ASSERT(iChannel < 3); - return (&x)[iChannel]; - } - - D & - operator[](size_t iChannel) - { - NPP_ASSERT(iChannel < 3); - return (&x)[iChannel]; - } - }; - - template - struct Pixel - { - D x, y, z, w; - - const D & - operator[](size_t iChannel) - const - { - NPP_ASSERT(iChannel < 4); - return (&x)[iChannel]; - } - - D & - operator[](size_t iChannel) - { - NPP_ASSERT(iChannel < 4); - return (&x)[iChannel]; - } - }; - -} // npp namespace - -#endif // NV_UTIL_PIXEL_H diff --git a/Common/UtilNPP/Signal.h b/Common/UtilNPP/Signal.h deleted file mode 100644 index f45fd07b..00000000 --- a/Common/UtilNPP/Signal.h +++ /dev/null @@ -1,168 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_SIGNAL_H -#define NV_UTIL_NPP_SIGNAL_H - -#include - -namespace npp -{ - class Signal - { - public: - Signal() : nSize_(0) - { }; - - explicit - Signal(size_t nSize) : nSize_(nSize) - { }; - - Signal(const Signal &rSignal) : nSize_(rSignal.nSize_) - { }; - - virtual - ~Signal() - { } - - Signal & - operator= (const Signal &rSignal) - { - nSize_ = rSignal.nSize_; - return *this; - } - - size_t - size() - const - { - return nSize_; - } - - void - swap(Signal &rSignal) - { - size_t nTemp = nSize_; - nSize_ = rSignal.nSize_; - rSignal.nSize_ = nTemp; - } - - - private: - size_t nSize_; - }; - - template - class SignalTemplate: public Signal - { - public: - typedef D tData; - - SignalTemplate(): aValues_(0) - { - ; - } - - SignalTemplate(size_t nSize): Signal(nSize) - , aValues_(0) - { - aValues_ = A::Malloc1D(size()); - } - - SignalTemplate(const SignalTemplate &rSignal): Signal(rSignal) - , aValues_(0) - { - aValues_ = A::Malloc1D(size()); - A::Copy1D(aValues_, rSignal.values(), size()); - } - - virtual - ~SignalTemplate() - { - A::Free1D(aValues_); - } - - SignalTemplate & - operator= (const SignalTemplate &rSignal) - { - // in case of self-assignment - if (&rSignal == this) - { - return *this; - } - - A::Free1D(aValues_); - this->aPixels_ = 0; - - // assign parent class's data fields (width, height) - Signal::operator =(rSignal); - - aValues_ = A::Malloc1D(size()); - A::Copy1D(aValues_, rSignal.value(), size()); - - return *this; - } - - /// Get a pointer to the pixel array. - /// The result pointer can be offset to pixel at position (x, y) and - /// even negative offsets are allowed. - /// \param nX Horizontal pointer/array offset. - /// \param nY Vertical pointer/array offset. - /// \return Pointer to the pixel array (or first pixel in array with coordinates (nX, nY). - tData * - values(int i = 0) - { - return aValues_ + i; - } - - const - tData * - values(int i = 0) - const - { - return aValues_ + i; - } - - void - swap(SignalTemplate &rSignal) - { - Signal::swap(rSignal); - - tData *aTemp = this->aValues_; - this->aValues_ = rSignal.aValues_; - rSignal.aValues_ = aTemp; - } - - private: - D *aValues_; - }; - -} // npp namespace - - -#endif // NV_UTIL_NPP_SIGNAL_H diff --git a/Common/UtilNPP/SignalAllocatorsCPU.h b/Common/UtilNPP/SignalAllocatorsCPU.h deleted file mode 100644 index 56751f26..00000000 --- a/Common/UtilNPP/SignalAllocatorsCPU.h +++ /dev/null @@ -1,66 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_SIGNAL_ALLOCATORS_CPU_H -#define NV_UTIL_NPP_SIGNAL_ALLOCATORS_CPU_H - -#include "Exceptions.h" - -namespace npp -{ - - template - class SignalAllocatorCPU - { - public: - static - D * - Malloc1D(unsigned int nSize) - { - return new D[nSize];; - }; - - static - void - Free1D(D *pPixels) - { - delete[] pPixels; - }; - - static - void - Copy1D(D *pDst, const D *pSrc, size_t nSize) - { - memcpy(pDst, pSrc, nSize * sizeof(D)); - }; - - }; - -} // npp namespace - -#endif // NV_UTIL_NPP_SIGNAL_ALLOCATORS_CPU_H diff --git a/Common/UtilNPP/SignalAllocatorsNPP.h b/Common/UtilNPP/SignalAllocatorsNPP.h deleted file mode 100644 index 2f5d332e..00000000 --- a/Common/UtilNPP/SignalAllocatorsNPP.h +++ /dev/null @@ -1,684 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_SIGNAL_ALLOCATORS_NPP_H -#define NV_UTIL_NPP_SIGNAL_ALLOCATORS_NPP_H - - -#include "Exceptions.h" - -#include -#include - -namespace npp -{ - - template - class SignalAllocator - { - }; - - template<> - class SignalAllocator - { - public: - static - Npp8u * - Malloc1D(size_t nSize) - { - Npp8u *pResult = nppsMalloc_8u(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp8u *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp8u *pDst, const Npp8u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp8u),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp8u *pDst, const Npp8u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp8u), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp8u *pDst, const Npp8u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp8u), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp16s * - Malloc1D(size_t nSize) - { - Npp16s *pResult = nppsMalloc_16s(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp16s *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp16s *pDst, const Npp16s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16s),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp16s *pDst, const Npp16s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16s), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp16s *pDst, const Npp16s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16s), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp16u * - Malloc1D(size_t nSize) - { - Npp16u *pResult = nppsMalloc_16u(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp16u *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp16u *pDst, const Npp16u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16u),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp16u *pDst, const Npp16u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16u), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp16u *pDst, const Npp16u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16u), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp16sc * - Malloc1D(size_t nSize) - { - Npp16sc *pResult = nppsMalloc_16sc(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp16sc *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp16sc *pDst, const Npp16sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16sc),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp16sc *pDst, const Npp16sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16sc), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp16sc *pDst, const Npp16sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp16sc), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp32u * - Malloc1D(size_t nSize) - { - Npp32u *pResult = nppsMalloc_32u(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp32u *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp32u *pDst, const Npp32u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32u),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp32u *pDst, const Npp32u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32u), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp32u *pDst, const Npp32u *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32u), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp32s * - Malloc1D(size_t nSize) - { - Npp32s *pResult = nppsMalloc_32s(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp32s *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp32s *pDst, const Npp32s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32s),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp32s *pDst, const Npp32s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32s), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp32s *pDst, const Npp32s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32s), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp32sc * - Malloc1D(size_t nSize) - { - Npp32sc *pResult = nppsMalloc_32sc(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp32sc *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp32sc *pDst, const Npp32sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32sc),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp32sc *pDst, const Npp32sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32sc), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp32sc *pDst, const Npp32sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32sc), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp32f * - Malloc1D(size_t nSize) - { - Npp32f *pResult = nppsMalloc_32f(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp32f *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp32f *pDst, const Npp32f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32f),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp32f *pDst, const Npp32f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32f), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp32f *pDst, const Npp32f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32f), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp32fc * - Malloc1D(size_t nSize) - { - Npp32fc *pResult = nppsMalloc_32fc(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp32fc *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp32fc *pDst, const Npp32fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32fc),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp32fc *pDst, const Npp32fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32fc), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp32fc *pDst, const Npp32fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp32fc), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp64s * - Malloc1D(size_t nSize) - { - Npp64s *pResult = nppsMalloc_64s(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp64s *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp64s *pDst, const Npp64s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64s),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp64s *pDst, const Npp64s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64s), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp64s *pDst, const Npp64s *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64s), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp64sc * - Malloc1D(size_t nSize) - { - Npp64sc *pResult = nppsMalloc_64sc(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp64sc *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp64sc *pDst, const Npp64sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64sc),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp64sc *pDst, const Npp64sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64sc), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp64sc *pDst, const Npp64sc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64sc), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp64f * - Malloc1D(size_t nSize) - { - Npp64f *pResult = nppsMalloc_64f(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp64f *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp64f *pDst, const Npp64f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64f),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp64f *pDst, const Npp64f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64f), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp64f *pDst, const Npp64f *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64f), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; - - template<> - class SignalAllocator - { - public: - static - Npp64fc * - Malloc1D(size_t nSize) - { - Npp64fc *pResult = nppsMalloc_64fc(static_cast(nSize)); - NPP_ASSERT(pResult != 0); - - return pResult; - }; - - static - void - Free1D(Npp64fc *pValues) - { - nppsFree(pValues); - }; - - static - void - Copy1D(Npp64fc *pDst, const Npp64fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64fc),cudaMemcpyDeviceToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - HostToDeviceCopy1D(Npp64fc *pDst, const Npp64fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64fc), cudaMemcpyHostToDevice); - NPP_ASSERT(cudaSuccess == eResult); - }; - - static - void - DeviceToHostCopy1D(Npp64fc *pDst, const Npp64fc *pSrc, size_t nSize) - { - cudaError_t eResult; - eResult = cudaMemcpy(pDst, pSrc, nSize * sizeof(Npp64fc), cudaMemcpyDeviceToHost); - NPP_ASSERT(cudaSuccess == eResult); - }; - }; -} // npp namespace - -#endif // NV_UTIL_NPP_SIGNAL_ALLOCATORS_NPP_H diff --git a/Common/UtilNPP/SignalsCPU.h b/Common/UtilNPP/SignalsCPU.h deleted file mode 100644 index 7b38d550..00000000 --- a/Common/UtilNPP/SignalsCPU.h +++ /dev/null @@ -1,107 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_SIGNALS_CPU_H -#define NV_UTIL_NPP_SIGNALS_CPU_H - -#include "Signal.h" - -#include "SignalAllocatorsCPU.h" -#include "Exceptions.h" - -#include - - -namespace npp -{ - - template - class SignalCPU: public npp::SignalTemplate - { - public: - typedef typename npp::SignalTemplate::tData tData; - - SignalCPU() - { - ; - } - - SignalCPU(size_t nSize): SignalTemplate(nSize) - { - ; - } - - SignalCPU(const SignalCPU &rSignal): SignalTemplate(rSignal) - { - ; - } - - virtual - ~SignalCPU() - { - ; - } - - SignalCPU & - operator= (const SignalCPU &rSignal) - { - SignalTemplate::operator= (rSignal); - - return *this; - } - - tData & - operator [](unsigned int i) - { - return *SignalTemplate::values(i); - } - - tData - operator [](unsigned int i) - const - { - return *SignalTemplate::values(i); - } - - }; - - typedef SignalCPU > SignalCPU_8u; - typedef SignalCPU > SignalCPU_32s; - typedef SignalCPU > SignalCPU_16s; - typedef SignalCPU > SignalCPU_16sc; - typedef SignalCPU > SignalCPU_32sc; - typedef SignalCPU > SignalCPU_32f; - typedef SignalCPU > SignalCPU_32fc; - typedef SignalCPU > SignalCPU_64s; - typedef SignalCPU > SignalCPU_64sc; - typedef SignalCPU > SignalCPU_64f; - typedef SignalCPU > SignalCPU_64fc; - -} // npp namespace - -#endif // NV_UTIL_NPP_SIGNALS_CPU_H diff --git a/Common/UtilNPP/SignalsNPP.h b/Common/UtilNPP/SignalsNPP.h deleted file mode 100644 index c56b91ae..00000000 --- a/Common/UtilNPP/SignalsNPP.h +++ /dev/null @@ -1,113 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#ifndef NV_UTIL_NPP_SIGNALS_NPP_H -#define NV_UTIL_NPP_SIGNALS_NPP_H - -#include "Exceptions.h" -#include "Signal.h" - -#include "SignalAllocatorsNPP.h" -#include - -namespace npp -{ - // forward declaration - template class SignalCPU; - - template - class SignalNPP: public npp::SignalTemplate > - { - public: - SignalNPP() - { - ; - } - - explicit - SignalNPP(size_t nSize): SignalTemplate >(nSize) - { - ; - } - - SignalNPP(const SignalNPP &rSignal): SignalTemplate >(rSignal) - { - ; - } - - template - explicit - SignalNPP(const SignalCPU &rSignal): SignalTemplate >(rSignal.size()) - { - npp::SignalAllocator::HostToDeviceCopy1D(SignalTemplate >::values(), - rSignal.values(), SignalTemplate >::size()); - } - - virtual - ~SignalNPP() - { - ; - } - - SignalNPP & - operator= (const SignalNPP &rSignal) - { - SignalTemplate >::operator= (rSignal); - - return *this; - } - - void - copyTo(D *pValues) - const - { - npp::SignalAllocator::DeviceToHostCopy1D(pValues, SignalTemplate >::values(), SignalTemplate >::size()); - } - - void - copyFrom(D *pValues) - { - npp::SignalAllocator::HostToDeviceCopy1D(SignalTemplate >::values(), pValues, SignalTemplate >::size()); - } - }; - - typedef SignalNPP SignalNPP_8u; - typedef SignalNPP SignalNPP_16s; - typedef SignalNPP SignalNPP_16sc; - typedef SignalNPP SignalNPP_32s; - typedef SignalNPP SignalNPP_32sc; - typedef SignalNPP SignalNPP_32f; - typedef SignalNPP SignalNPP_32fc; - typedef SignalNPP SignalNPP_64s; - typedef SignalNPP SignalNPP_64sc; - typedef SignalNPP SignalNPP_64f; - typedef SignalNPP SignalNPP_64fc; - -} // npp namespace - -#endif // NV_UTIL_NPP_SIGNALS_NPP_H diff --git a/Common/helper_cuda.h b/Common/helper_cuda.h index 9440235e..67f86e33 100644 --- a/Common/helper_cuda.h +++ b/Common/helper_cuda.h @@ -674,6 +674,7 @@ inline int _ConvertSMVer2Cores(int major, int minor) { {0xa0, 128}, {0xa1, 128}, {0xa3, 128}, + {0xa7, 128}, {0xb0, 128}, {0xc0, 128}, {0xc1, 128}, @@ -729,6 +730,7 @@ inline const char* _ConvertSMVer2ArchName(int major, int minor) { {0xa0, "Blackwell"}, {0xa1, "Blackwell"}, {0xa3, "Blackwell"}, + {0xa7, "Rubin"}, {0xb0, "Blackwell"}, {0xc0, "Blackwell"}, {0xc1, "Blackwell"}, diff --git a/Common/helper_cuda_drvapi.h b/Common/helper_cuda_drvapi.h index 2ded503e..893c874f 100644 --- a/Common/helper_cuda_drvapi.h +++ b/Common/helper_cuda_drvapi.h @@ -119,6 +119,7 @@ inline int _ConvertSMVer2CoresDRV(int major, int minor) { {0xa0, 128}, {0xa1, 128}, {0xa3, 128}, + {0xa7, 128}, {0xb0, 128}, {0xc0, 128}, {0xc1, 128}, diff --git a/Common/helper_math.h b/Common/helper_math.h index 6d2f63ae..36281913 100644 --- a/Common/helper_math.h +++ b/Common/helper_math.h @@ -1143,10 +1143,16 @@ inline __host__ __device__ uint4 max(uint4 a, uint4 b) // - linear interpolation between a and b, based on value t in [0, 1] range //////////////////////////////////////////////////////////////////////////////// +// std::lerp (C++20) is pulled into the global namespace by GCC's ; +// only define our own scalar overload when std::lerp is unavailable, to avoid +// a redeclaration / ambiguous-overload clash. (The float2/3/4 overloads below +// take distinct argument types and never conflict.) +#if !defined(__cplusplus) || (__cplusplus < 202002L) inline __device__ __host__ float lerp(float a, float b, float t) { return a + t*(b-a); } +#endif inline __device__ __host__ float2 lerp(float2 a, float2 b, float t) { return a + t*(b-a); diff --git a/Common/helper_multiprocess.cpp b/Common/helper_multiprocess.cpp index ee1618aa..91b3aa55 100644 --- a/Common/helper_multiprocess.cpp +++ b/Common/helper_multiprocess.cpp @@ -185,22 +185,24 @@ int ipcCreateSocket(ipcHandle *&handle, const char *name, return -1; } - char path_name[50]; + char path_name[sizeof(servaddr.sun_path)]; // Create unique name for the socket with path if SOCK_FOLDER is set. - sprintf(path_name, "%s/%u", getSocketFolder().c_str(), getpid()); + int written = snprintf(path_name, sizeof(path_name), "%s/%u", + getSocketFolder().c_str(), getpid()); + if (written < 0 || (size_t)written >= sizeof(path_name)) { + perror("IPC failure: Cannot bind provided name to socket. Name too large"); + close(server_fd); + delete handle; + handle = NULL; + return -1; + } unlink(path_name); memset(&servaddr, 0, sizeof(servaddr)); servaddr.sun_family = AF_UNIX; - size_t len = strlen(path_name); - if (len > (sizeof(servaddr.sun_path) - 1)) { - perror("IPC failure: Cannot bind provided name to socket. Name too large"); - return -1; - } - - strncpy(servaddr.sun_path, path_name, len); + strncpy(servaddr.sun_path, path_name, sizeof(servaddr.sun_path) - 1); if (bind(server_fd, (struct sockaddr *)&servaddr, SUN_LEN(&servaddr)) < 0) { perror("IPC failure: Binding socket failed"); @@ -227,10 +229,18 @@ int ipcOpenSocket(ipcHandle *&handle) { memset(&cliaddr, 0, sizeof(cliaddr)); cliaddr.sun_family = AF_UNIX; - char temp[50]; + char temp[sizeof(cliaddr.sun_path)]; // Create unique name for the socket with path if SOCK_FOLDER is set. - sprintf(temp, "%s/%u", getSocketFolder().c_str(), getpid()); + int written = snprintf(temp, sizeof(temp), "%s/%u", getSocketFolder().c_str(), + getpid()); + if (written < 0 || (size_t)written >= sizeof(temp)) { + perror("IPC failure: Cannot bind provided name to socket. Name too large"); + close(sock); + delete handle; + handle = NULL; + return -1; + } strcpy(cliaddr.sun_path, temp); if (bind(sock, (struct sockaddr *)&cliaddr, sizeof(cliaddr)) < 0) { @@ -366,8 +376,14 @@ int ipcSendShareableHandle(ipcHandle *handle, // Construct client address to send this SHareable handle to memset(&cliaddr, 0, sizeof(cliaddr)); cliaddr.sun_family = AF_UNIX; - char temp[20]; - sprintf(temp, "%s/%u", getSocketFolder().c_str(), process); + char temp[sizeof(cliaddr.sun_path)]; + int written = + snprintf(temp, sizeof(temp), "%s/%u", getSocketFolder().c_str(), process); + if (written < 0 || (size_t)written >= sizeof(temp)) { + perror("IPC failure: Cannot address client socket. Name too large"); + free(control_un.control); + return -1; + } strcpy(cliaddr.sun_path, temp); len = sizeof(cliaddr); diff --git a/README.md b/README.md index 106d3f73..8e35d2e7 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # CUDA Samples -Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports [CUDA Toolkit 13.3](https://developer.nvidia.com/cuda-downloads). +Samples for CUDA Developers which demonstrates features in CUDA Toolkit. This version supports [CUDA Toolkit 13.4](https://developer.nvidia.com/cuda-downloads). ## Release Notes @@ -28,8 +28,6 @@ Without using git the easiest way to use these samples is to download the zip fi ## Building CUDA Samples -### Building CUDA Samples - The CUDA Samples are built using CMake. Follow the instructions below for building on Linux, Windows, and for cross-compilation to Tegra devices. ### Linux @@ -51,7 +49,27 @@ Build the samples: ``` make -j$(nproc) ``` -Run the samples from their respective directories in the build folder. You can also follow this process from and subdirectory of the samples repo, or from within any individual sample. + +By default, samples are compiled for all GPU architectures supported by this release. If you only need to target a specific GPU, you can override this to build for a single architecture and reduce build time considerably: +``` +cmake -DCMAKE_CUDA_ARCHITECTURES= .. +``` +Replace `` with your GPU's SM version (e.g. `90` for sm_90). + +Run the samples from their respective directories in the build folder. + +### Building a Single Sample + +To build just one sample, configure CMake from within the sample's directory. You must explicitly specify a GPU architecture — standalone builds have no top-level default: + +```bash +cd cpp// +mkdir -p build && cd build +cmake -DCMAKE_CUDA_ARCHITECTURES= .. +make +``` + +Replace `` with your GPU's SM version (e.g. `90` for sm_90). ### Windows @@ -170,23 +188,66 @@ $ make -j$(nproc) --ignore-errors # or --keep-going ### QNX -Cross-compilation for QNX with CMake is supported in the CUDA 13.0 samples release and newer. An example build for -the Tegra Thor QNX platform might look like this: +Cross-compilation for QNX with CMake is supported in the CUDA 13.0 samples release and newer. +Set up the QNX SDP paths: + +```bash +export QNX_HOST=/path/to/qnx/host +export QNX_TARGET=/path/to/qnx/target ``` -$ mkdir build -$ cd build -QNX_HOST=/path/to/qnx/host \ -QNX_TARGET=/path/to/qnx/target \ +Build the samples for the Tegra Thor QNX platform: + +```bash +mkdir -p build && cd build cmake .. \ --DBUILD_TEGRA=True \ --DCMAKE_CUDA_COMPILER=/usr/local/cuda-13.3/bin/nvcc \ --DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/toolchain-aarch64-qnx.cmake \ --DCMAKE_LIBRARY_PATH=/usr/local/cuda-13.3/thor/targets/aarch64-qnx/lib/stubs/ \ --DCMAKE_INCLUDE_PATH=/usr/local/cuda-13.3/thor/targets/aarch64-qnx/include/ + -DBUILD_TEGRA=True \ + -DCMAKE_CUDA_COMPILER=/usr/local/cuda-13.3/bin/nvcc \ + -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/toolchain-aarch64-qnx.cmake \ + -DTARGET_FS=/path/to/qnx/targetfs \ + -DCMAKE_LIBRARY_PATH=/usr/local/cuda-13.3/thor/targets/aarch64-qnx/lib/stubs/ \ + -DCMAKE_INCLUDE_PATH=/usr/local/cuda-13.3/thor/targets/aarch64-qnx/include/ +cmake --build . ``` +`TARGET_FS` is the QNX target filesystem of your board. The `cudaNvSci` sample needs it, because the NvSci headers and libraries ship with the target filesystem and not with the CUDA toolkit. Without `TARGET_FS` the build reports `NvSCI not found` and skips the sample. + +The target filesystem is part of the NVIDIA DRIVE OS QNX SDK, which registered users download from [NVONLINE](https://partners.nvidia.com). After the SDK is installed, the filesystem is in the DRIVE OS workspace: `/drive-qnx` for the standard SDK and `/drive-qnx-safety` for the safety SDK. See the [DRIVE OS documentation](https://docs.nvidia.com/drive/) for the installation guides. + +For both QNX toolchains the build looks for `nvscibuf.h` in `/include`, `/../include` and `/usr/include`, and for `libnvscibuf.so` in `/lib-target`, `/usr/libnvidia` and `/usr/lib`. If your filesystem uses a different layout, set `NVSCIBUF_INCLUDE_DIR`, `NVSCISYNC_INCLUDE_DIR`, `NVSCIBUF_LIBRARY` and `NVSCISYNC_LIBRARY` on the cmake command line instead. + +### QNX Safety (CUDA Safe toolkit) + +Cross-compilation for **QNX Safety** uses the CUDA Safe toolkit (for example `/usr/local/cuda-13.3-safe`). + +Set up the QNX SDP and Safe toolkit paths: + +```bash +export QNX_HOST=/path/to/qnx/host +export QNX_TARGET=/path/to/qnx/target +export CUDA_PATH=/usr/local/cuda-13.3-safe +export PATH=$CUDA_PATH/nvvm/bin:$PATH +``` + +Build a single sample (standalone configure from the sample directory). + +```bash +mkdir -p build && cd build +cmake .. \ + -DCMAKE_TOOLCHAIN_FILE=../../../cmake/toolchains/toolchain-aarch64-qnx-safe.cmake \ + -DCMAKE_CUDA_COMPILER=$CUDA_PATH/bin/nvcc \ + -DCMAKE_CUDA_ARCHITECTURES=110 \ + -DTARGET_FS=/path/to/qnx/safety/targetfs +cmake --build . +``` + +The safe toolchain sets `-safety-compat`, links shared `libcudart` and `libcuda`, and uses QNX `q++` as the CUDA host compiler (`CMAKE_CUDA_HOST_COMPILER`). Set `CMAKE_CUDA_ARCHITECTURES` to match your GPU (`87` for Orin, `110` for Thor). + +Supported QNX Safety samples: `matrixMul`, `cudaNvSci`. + +`TARGET_FS` is only needed for `cudaNvSci`, which links the NvSci libraries from the safety target filesystem (`/drive-qnx-safety`, part of the DRIVE OS QNX Safety SDK). `matrixMul` builds without it. The NvSci search paths are the same as for the standard QNX build above. + ### Forward Compatibility To build samples with new CUDA Toolkit(CUDA 13.0 or later) and UMD(Version 580 or later) and old KMD(Version 550 or earlier)，you need to set the `CMAKE_PREFIX_PATH` for using new driver library, the command might like this: diff --git a/cmake/CudaSampleArchs.cmake b/cmake/CudaSampleArchs.cmake new file mode 100644 index 00000000..01ded949 --- /dev/null +++ b/cmake/CudaSampleArchs.cmake @@ -0,0 +1 @@ +set(CUDA_SAMPLES_MASTER_ARCHS 75 80 86 87 89 90 100 103 107 110 120) diff --git a/cmake/CudaSampleCommon.cmake b/cmake/CudaSampleCommon.cmake new file mode 100644 index 00000000..8ca24b1c --- /dev/null +++ b/cmake/CudaSampleCommon.cmake @@ -0,0 +1,19 @@ +include_guard(GLOBAL) + +get_filename_component(_cuda_sample_cmake_dir "${CMAKE_CURRENT_LIST_FILE}" DIRECTORY) + +list(APPEND CMAKE_MODULE_PATH "${_cuda_sample_cmake_dir}") +list(APPEND CMAKE_MODULE_PATH "${_cuda_sample_cmake_dir}/Modules") + +set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") +if(ENABLE_CUDA_DEBUG) + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") +else() + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") +endif() + +if(MSVC) + add_compile_options($<$:-Xcompiler=/Zc:preprocessor>) +endif() + +unset(_cuda_sample_cmake_dir) diff --git a/cmake/DetectCudaArch.cmake b/cmake/DetectCudaArch.cmake new file mode 100644 index 00000000..280b4ab8 --- /dev/null +++ b/cmake/DetectCudaArch.cmake @@ -0,0 +1,66 @@ +if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + message(FATAL_ERROR + "CMAKE_CUDA_ARCHITECTURES is not set.\n" + "For standalone sample builds, specify a target architecture explicitly:\n" + " cmake -DCMAKE_CUDA_ARCHITECTURES= \n" + "Example: cmake -DCMAKE_CUDA_ARCHITECTURES=90 .") +endif() + +include("${CMAKE_CURRENT_LIST_DIR}/CudaSampleArchs.cmake") + +# Master list of all CUDA architectures supported by this repo and CUDA 13.x. +# Each sample may define SAMPLE_DISALLOW_ARCHS to exclude architectures it does not support. +set(_master_archs ${CUDA_SAMPLES_MASTER_ARCHS}) + +# Compute this sample's effective arch list: master minus any disallowed archs. +set(_effective_archs ${_master_archs}) +if(DEFINED SAMPLE_DISALLOW_ARCHS) + foreach(_arch ${SAMPLE_DISALLOW_ARCHS}) + list(REMOVE_ITEM _effective_archs "${_arch}") + endforeach() +endif() + +get_filename_component(_sample_dir "${CMAKE_CURRENT_SOURCE_DIR}" NAME) + +# CMAKE_CUDA_ARCHITECTURES is set by the top-level CMakeLists.txt (defaulting to the +# full master list if the user did not specify). Build only for the intersection with +# the effective list; skip the sample if no requested arch is supported. +# CMake special keywords (all, all-major, native) are passed through as-is. +set(_passthrough_values "all" "all-major" "native") +if(CMAKE_CUDA_ARCHITECTURES IN_LIST _passthrough_values) + set(_passthrough TRUE) +else() + set(_passthrough FALSE) +endif() + +if(NOT _passthrough) + set(_build_archs) + foreach(_arch ${CMAKE_CUDA_ARCHITECTURES}) + if(_arch IN_LIST _effective_archs) + list(APPEND _build_archs "${_arch}") + endif() + endforeach() + + if(NOT _build_archs) + if(NOT CUDA_SAMPLES_ARCHS_DEFAULTED) + message(WARNING + "Sample in directory '${_sample_dir}' skipped: " + "user specified '${CMAKE_CUDA_ARCHITECTURES}' but this sample only supports '${_effective_archs}'") + endif() + set(SAMPLE_SKIP_BUILD TRUE) + elseif(NOT "${_build_archs}" STREQUAL "${CMAKE_CUDA_ARCHITECTURES}" AND NOT CUDA_SAMPLES_ARCHS_DEFAULTED) + message(WARNING + "Sample in directory '${_sample_dir}': user requested some architecture(s) that are not supported. " + "Only building for '${_build_archs}'") + set(CMAKE_CUDA_ARCHITECTURES "${_build_archs}") + else() + set(CMAKE_CUDA_ARCHITECTURES "${_build_archs}") + endif() + unset(_build_archs) +endif() + +unset(_master_archs) +unset(_effective_archs) +unset(_sample_dir) +unset(_passthrough) +unset(_passthrough_values) diff --git a/cmake/Modules/FindFreeImage.cmake b/cmake/Modules/FindFreeImage.cmake deleted file mode 100644 index 8e6ade45..00000000 --- a/cmake/Modules/FindFreeImage.cmake +++ /dev/null @@ -1,17 +0,0 @@ -find_path(FreeImage_INCLUDE_DIR - NAMES freeimage.h FreeImage.h - PATHS /usr/include /usr/local/include -) - -find_library(FreeImage_LIBRARY - NAMES freeimage FreeImage - PATHS /usr/lib /usr/local/lib -) - -include(FindPackageHandleStandardArgs) -find_package_handle_standard_args(FreeImage DEFAULT_MSG FreeImage_LIBRARY FreeImage_INCLUDE_DIR) - -if(FreeImage_FOUND) - set(FreeImage_LIBRARIES ${FreeImage_LIBRARY}) - set(FreeImage_INCLUDE_DIRS ${FreeImage_INCLUDE_DIR}) -endif() diff --git a/cmake/toolchains/toolchain-aarch64-qnx-safe.cmake b/cmake/toolchains/toolchain-aarch64-qnx-safe.cmake new file mode 100644 index 00000000..df3d90a4 --- /dev/null +++ b/cmake/toolchains/toolchain-aarch64-qnx-safe.cmake @@ -0,0 +1,136 @@ +# Toolchain for QNX Safety builds with the CUDA Safe toolkit (QNX SAFE stack). +# Use this file instead of toolchain-aarch64-qnx.cmake for safe-toolkit installs. + +set(CMAKE_SYSTEM_NAME QNX) +set(CMAKE_SYSTEM_PROCESSOR aarch64) + +set(QNX_HOST $ENV{QNX_HOST}) +set(QNX_TARGET $ENV{QNX_TARGET}) + +message(STATUS "QNX_HOST = ${QNX_HOST}") +message(STATUS "QNX_TARGET = ${QNX_TARGET}") + +find_program(QNX_QCC NAMES qcc PATHS "${QNX_HOST}/usr/bin") +find_program(QNX_QPLUS NAMES q++ PATHS "${QNX_HOST}/usr/bin") + +if(NOT QNX_QCC OR NOT QNX_QPLUS) + message(FATAL_ERROR "Could not find qcc or q++ in QNX_HOST=${QNX_HOST}/usr/bin") +endif() + +set(CMAKE_C_COMPILER ${QNX_QCC}) +set(CMAKE_CXX_COMPILER ${QNX_QPLUS}) + +set(CMAKE_C_COMPILER_TARGET aarch64) +set(CMAKE_CXX_COMPILER_TARGET aarch64) + +# Toolkit root: required so CUDA compiler ID test does not expand empty CUDA_ROOT into -I/include, etc. +if(CMAKE_CUDA_COMPILER) + get_filename_component(_cuda_nvcc_dir "${CMAKE_CUDA_COMPILER}" DIRECTORY) + get_filename_component(CUDA_TOOLKIT_ROOT "${_cuda_nvcc_dir}" DIRECTORY) +elseif(DEFINED ENV{CUDA_PATH}) + set(CUDA_TOOLKIT_ROOT "$ENV{CUDA_PATH}") +endif() +if(CUDA_TOOLKIT_ROOT) + set(CUDA_TOOLKIT_ROOT "${CUDA_TOOLKIT_ROOT}" CACHE PATH "CUDA Toolkit root") + set(CUDA_ROOT "${CUDA_TOOLKIT_ROOT}" CACHE PATH "CUDA Toolkit root (nvcc / legacy CMake)" FORCE) +endif() + +if(NOT __qnx_gcc_ver) + set(__qnx_gcc_ver "12.2.0" CACHE STRING "QNX qcc/q++ toolchain version for -V / --qpp-config (match your SDP)") +endif() + +set(CMAKE_CUDA_HOST_COMPILER ${CMAKE_CXX_COMPILER} CACHE STRING "" FORCE) + +set(_cuda_safe_target "aarch64-qnx") + +# CUDA Safe toolkit flags: -safety-compat keeps the kernel-launch ABI consistent; the safe toolkit +# ships only a shared cudart (no static/cudadevrt); -I/-L point at its headers/libs; +# --unresolved-symbols defers libcudart's rootfs-resident deps (nvdvms_*, NvOs*) to runtime. +if(CUDA_TOOLKIT_ROOT) + set(_cuda_safe_inc "${CUDA_TOOLKIT_ROOT}/targets/${_cuda_safe_target}/include") + set(_cuda_safe_lib "${CUDA_TOOLKIT_ROOT}/targets/${_cuda_safe_target}/lib") + set(_cuda_safe_stubs "${CUDA_TOOLKIT_ROOT}/targets/${_cuda_safe_target}/lib/stubs") +endif() + +set(_cuda_safe_flags "-safety-compat --cudart=shared --cudadevrt=none -target-dir ${_cuda_safe_target} --qpp-config=${__qnx_gcc_ver},gcc_ntoaarch64le") +if(CUDA_TOOLKIT_ROOT) + string(APPEND _cuda_safe_flags " -I${_cuda_safe_inc} -L${_cuda_safe_lib} -L${_cuda_safe_stubs}") +endif() +string(APPEND _cuda_safe_flags " -Xlinker --unresolved-symbols=ignore-in-shared-libs") + +set(CMAKE_C_FLAGS " \"-V${__qnx_gcc_ver},gcc_ntoaarch64le\"") +set(CMAKE_CXX_FLAGS " \"-V${__qnx_gcc_ver},gcc_ntoaarch64le\"") +# Host .cpp TUs are built by q++ directly, so give them the CUDA include too. +if(CUDA_TOOLKIT_ROOT) + string(APPEND CMAKE_C_FLAGS " -I${_cuda_safe_inc}") + string(APPEND CMAKE_CXX_FLAGS " -I${_cuda_safe_inc}") +endif() +set(CMAKE_CUDA_FLAGS " ${_cuda_safe_flags}") +separate_arguments(_automagic_nvcc_flags UNIX_COMMAND "${_cuda_safe_flags}") +set(AUTOMAGIC_NVCC_FLAGS ${_automagic_nvcc_flags} + CACHE STRING "automagic feature detection flags for QNX Safe cross build") + +# Safe toolkit has no cudadevrt/static cudart, so disable CMake's implicit runtime libs and link the +# shared cudart ourselves. Also link the driver (-lcuda): the shared libcudart.so references safety- +# stack symbols (nvdvms_*, NvOs*) that are transitive deps of libcuda.so.1, so the exe must pull in +# libcuda for them to resolve/load on the target. STANDARD_LIBRARIES_INIT covers ABI checks; the +# HOST_LINK options cover real targets. +set(CMAKE_CUDA_RUNTIME_LIBRARY "None" CACHE STRING "CUDA runtime library (safe toolkit: linked manually)") +set(CMAKE_CUDA_STANDARD_LIBRARIES_INIT "-lcudart -lcuda") +if(CUDA_TOOLKIT_ROOT) + add_link_options("$" "$" + "$" "$") +endif() + +# -V on the host link only; nvcc would comma-split it at the device link (which uses --qpp-config). +add_link_options("$gcc_ntoaarch64le>") +# Defer libcudart's rootfs-resident deps to runtime. +add_link_options("-Wl,--unresolved-symbols=ignore-in-shared-libs") + +set(CROSS_COMPILE_FOR_QNX ON CACHE BOOL "Cross compiling for QNX platforms") +string(APPEND CMAKE_CXX_FLAGS " -D_QNX_SOURCE") +string(APPEND CMAKE_CUDA_FLAGS " -D_QNX_SOURCE") + +# cudaNvSci needs NvSci headers/libs from the QNX safety rootfs; point TARGET_FS at it. This +# pre-seeds FindNVSCI's cache vars and auto-detects the header/lib dirs across rootfs layouts. +if(DEFINED TARGET_FS) + # Expand a leading ~ so paths reach the linker absolute. + if(TARGET_FS MATCHES "^~") + string(REGEX REPLACE "^~" "$ENV{HOME}" TARGET_FS "${TARGET_FS}") + endif() + get_filename_component(_nvsci_sdk_root "${TARGET_FS}" DIRECTORY) + + # Header dir varies by rootfs layout. + foreach(_inc "${TARGET_FS}/include" "${_nvsci_sdk_root}/include" "${TARGET_FS}/usr/include") + if(NOT _nvsci_inc AND EXISTS "${_inc}/nvscibuf.h") + set(_nvsci_inc "${_inc}") + endif() + endforeach() + + # Lib dir varies by rootfs layout. + foreach(_lib "${TARGET_FS}/lib-target" "${TARGET_FS}/usr/libnvidia" "${TARGET_FS}/usr/lib") + if(NOT _nvsci_lib AND EXISTS "${_lib}/libnvscibuf.so") + set(_nvsci_lib "${_lib}") + endif() + endforeach() + + if(NOT _nvsci_inc OR NOT _nvsci_lib) + message(FATAL_ERROR + "Could not locate NvSci headers/libs from TARGET_FS='${TARGET_FS}'. " + "Checked include roots for nvscibuf.h and lib roots for libnvscibuf.so. " + "Please set TARGET_FS (or NVSCIBUF/NVSCISYNC cache vars) to a valid QNX safety rootfs.") + endif() + + set(NVSCIBUF_INCLUDE_DIR "${_nvsci_inc}" CACHE PATH "NvSciBuf headers (QNX safe)") + set(NVSCISYNC_INCLUDE_DIR "${_nvsci_inc}" CACHE PATH "NvSciSync headers (QNX safe)") + set(NVSCIBUF_LIBRARY "${_nvsci_lib}/libnvscibuf.so" CACHE FILEPATH "NvSciBuf library (QNX safe)") + set(NVSCISYNC_LIBRARY "${_nvsci_lib}/libnvscisync.so" CACHE FILEPATH "NvSciSync library (QNX safe)") + + # rpath-link the rootfs lib dirs so NvSci's transitive deps resolve at link time. + foreach(_dir "${_nvsci_lib}" "${TARGET_FS}/lib-target" "${TARGET_FS}/usr/libnvidia" "${TARGET_FS}/usr/lib") + if(IS_DIRECTORY "${_dir}") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,-rpath-link,${_dir}") + set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -Wl,-rpath-link,${_dir}") + endif() + endforeach() +endif() diff --git a/cmake/toolchains/toolchain-aarch64-qnx.cmake b/cmake/toolchains/toolchain-aarch64-qnx.cmake index df9839b8..b1f2bfc4 100644 --- a/cmake/toolchains/toolchain-aarch64-qnx.cmake +++ b/cmake/toolchains/toolchain-aarch64-qnx.cmake @@ -35,3 +35,50 @@ add_link_options("-V${__qnx_gcc_ver},gcc_ntoaarch64le") set(CROSS_COMPILE_FOR_QNX ON CACHE BOOL "Cross compiling for QNX platforms") string(APPEND CMAKE_CXX_FLAGS " -D_QNX_SOURCE") string(APPEND CMAKE_CUDA_FLAGS " -D_QNX_SOURCE") + +# cudaNvSci needs NvSci headers/libs from the QNX rootfs; point TARGET_FS at it. This pre-seeds +# FindNVSCI's cache vars and auto-detects the header/lib dirs across rootfs layouts. +if(DEFINED TARGET_FS) + # Expand a leading ~ so paths reach the linker absolute. + if(TARGET_FS MATCHES "^~") + string(REGEX REPLACE "^~" "$ENV{HOME}" TARGET_FS "${TARGET_FS}") + endif() + get_filename_component(_nvsci_sdk_root "${TARGET_FS}" DIRECTORY) + + # Header dir varies by rootfs layout. Both headers are required, so accept a dir only if it + # holds both. + foreach(_inc "${TARGET_FS}/include" "${_nvsci_sdk_root}/include" "${TARGET_FS}/usr/include") + if(NOT _nvsci_inc AND EXISTS "${_inc}/nvscibuf.h" AND EXISTS "${_inc}/nvscisync.h") + set(_nvsci_inc "${_inc}") + endif() + endforeach() + + # Lib dir varies by rootfs layout. FindNVSCI needs both libraries, so accept a dir only if it + # holds both. + foreach(_lib "${TARGET_FS}/lib-target" "${TARGET_FS}/usr/libnvidia" "${TARGET_FS}/usr/lib") + if(NOT _nvsci_lib AND EXISTS "${_lib}/libnvscibuf.so" AND EXISTS "${_lib}/libnvscisync.so") + set(_nvsci_lib "${_lib}") + endif() + endforeach() + + # A rootfs without NvSci is not an error: FindNVSCI then reports NvSci as missing and only the + # NvSci samples are skipped. + if(_nvsci_inc AND _nvsci_lib) + set(NVSCIBUF_INCLUDE_DIR "${_nvsci_inc}" CACHE PATH "NvSciBuf headers") + set(NVSCISYNC_INCLUDE_DIR "${_nvsci_inc}" CACHE PATH "NvSciSync headers") + set(NVSCIBUF_LIBRARY "${_nvsci_lib}/libnvscibuf.so" CACHE FILEPATH "NvSciBuf library") + set(NVSCISYNC_LIBRARY "${_nvsci_lib}/libnvscisync.so" CACHE FILEPATH "NvSciSync library") + + # rpath-link the rootfs lib dirs so NvSci's transitive deps resolve at link time. + # NvSci pulls in NvRm/NvOs/NvSciIpc, which can sit in a lib dir of their own. + foreach(_dir "${_nvsci_lib}" "${TARGET_FS}/lib-target" "${_nvsci_sdk_root}/lib-target" + "${TARGET_FS}/usr/libnvidia" "${TARGET_FS}/usr/lib") + if(IS_DIRECTORY "${_dir}") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -Wl,-rpath-link,${_dir}") + set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} -Wl,-rpath-link,${_dir}") + endif() + endforeach() + else() + message(STATUS "NvSci headers/libs not found in TARGET_FS='${TARGET_FS}'") + endif() +endif() diff --git a/cpp/0_Introduction/CMakeLists.txt b/cpp/0_Introduction/CMakeLists.txt index 74534758..92fd8f86 100644 --- a/cpp/0_Introduction/CMakeLists.txt +++ b/cpp/0_Introduction/CMakeLists.txt @@ -1,44 +1,35 @@ add_subdirectory(UnifiedMemoryStreams) add_subdirectory(asyncAPI) add_subdirectory(clock) -add_subdirectory(clock_nvrtc) add_subdirectory(cudaOpenMP) add_subdirectory(fp16ScalarProduct) add_subdirectory(matrixMul) add_subdirectory(matrixMulDrv) add_subdirectory(matrixMulDynlinkJIT) -add_subdirectory(matrixMul_nvrtc) -add_subdirectory(mergeSort) add_subdirectory(simpleAWBarrier) add_subdirectory(simpleAssert) -add_subdirectory(simpleAssert_nvrtc) add_subdirectory(simpleAtomicIntrinsics) -add_subdirectory(simpleAtomicIntrinsics_nvrtc) add_subdirectory(simpleAttributes) add_subdirectory(simpleCUDA2GL) add_subdirectory(simpleCallback) add_subdirectory(simpleCooperativeGroups) add_subdirectory(simpleCubemapTexture) add_subdirectory(simpleDrvRuntime) -add_subdirectory(simpleHyperQ) add_subdirectory(simpleIPC) add_subdirectory(simpleLayeredTexture) add_subdirectory(simpleMPI) add_subdirectory(simpleMultiCopy) add_subdirectory(simpleMultiGPU) -add_subdirectory(simpleOccupancy) add_subdirectory(simpleP2P) add_subdirectory(simplePitchLinearTexture) add_subdirectory(simplePrintf) add_subdirectory(simpleStreams) add_subdirectory(simpleSurfaceWrite) -add_subdirectory(simpleTemplates) add_subdirectory(simpleTexture) add_subdirectory(simpleTexture3D) add_subdirectory(simpleTextureDrv) add_subdirectory(simpleVoteIntrinsics) add_subdirectory(simpleZeroCopy) -add_subdirectory(template) add_subdirectory(systemWideAtomics) add_subdirectory(vectorAdd) add_subdirectory(vectorAddDrv) diff --git a/cpp/0_Introduction/README.md b/cpp/0_Introduction/README.md index d32566f1..06626f9c 100644 --- a/cpp/0_Introduction/README.md +++ b/cpp/0_Introduction/README.md @@ -7,9 +7,6 @@ This sample illustrates the usage of CUDA events for both GPU timing and overlap ### [clock](./clock) This example shows how to use the clock function to measure the performance of block of threads of a kernel accurately. -### [clock_nvrtc](./clock_nvrtc) -This example shows how to use the clock function using libNVRTC to measure the performance of block of threads of a kernel accurately. - ### [cudaOpenMP](./cudaOpenMP) This sample demonstrates how to use OpenMP API to write an application for multiple GPUs. @@ -19,30 +16,18 @@ Calculates scalar product of two vectors of FP16 numbers. ### [matrixMul](./matrixMul) This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication. -### [matrixMul_nvrtc](./matrixMul_nvrtc) -This sample implements matrix multiplication and is exactly the same as Chapter 6 of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication. - ### [matrixMulDrv](./matrixMulDrv) This sample implements matrix multiplication and uses the new CUDA 4.0 kernel launch Driver API. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. CUBLAS provides high-performance matrix multiplication. ### [matrixMulDynlinkJIT](./matrixMulDynlinkJIT) This sample revisits matrix multiplication using the CUDA driver API. It demonstrates how to link to CUDA driver at runtime and how to use JIT (just-in-time) compilation from PTX code. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. CUBLAS provides high-performance matrix multiplication. -### [mergeSort](./mergeSort) -This sample implements a merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient on large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), may be the algorithms of choice for sorting batches of short- to mid-sized (key, value) array pairs. Refer to the excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm - ### [simpleAssert](./simpleAssert) This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 . -### [simpleAssert_nvrtc](./simpleAssert_nvrtc) -This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 . - ### [simpleAtomicIntrinsics](./simpleAtomicIntrinsics) A simple demonstration of global memory atomic instructions. -### [simpleAtomicIntrinsics_nvrtc](./simpleAtomicIntrinsics_nvrtc) -A simple demonstration of global memory atomic instructions.This sample makes use of NVRTC for Runtime Compilation. - ### [simpleAttributes](./simpleAttributes) This CUDA Runtime API sample is a very basic example that implements how to use the stream attributes that affect L2 locality. Performance improvement due to use of L2 access policy window can only be noticed on Compute capability 8.0 or higher. @@ -64,9 +49,6 @@ This sample shows how to copy CUDA image back to OpenGL using the most efficient ### [simpleDrvRuntime](./simpleDrvRuntime) A simple example which demonstrates how CUDA Driver and Runtime APIs can work together to load cuda fatbinary of vector add kernel and performing vector addition. -### [simpleHyperQ](./simpleHyperQ) -This sample demonstrates the use of CUDA streams for concurrent execution of several kernels on devices which provide HyperQ (SM 3.5). Devices without HyperQ (SM 2.0 and SM 3.0) will run a maximum of two kernels concurrently. - ### [simpleIPC](./simpleIPC) This CUDA Runtime API sample is a very basic sample that demonstrates Inter Process Communication with one process per GPU for computation. Requires Compute Capability 3.0 or higher and a Linux Operating System, or a Windows Operating System with TCC enabled GPUs @@ -82,9 +64,6 @@ Supported in GPUs with Compute Capability 1.1, overlapping compute with one memc ### [simpleMultiGPU](./simpleMultiGPU) This application demonstrates how to use the new CUDA 4.0 API for CUDA context management and multi-threaded access to run CUDA kernels on multiple-GPUs. -### [simpleOccupancy](./simpleOccupancy) -This sample demonstrates the basic usage of the CUDA occupancy calculator and occupancy-based launch configurator APIs by launching a kernel with the launch configurator, and measures the utilization difference against a manually configured launch. - ### [simpleP2P](./simpleP2P) This application demonstrates CUDA APIs that support Peer-To-Peer (P2P) copies, Peer-To-Peer (P2P) addressing, and Unified Virtual Memory Addressing (UVA) between multiple GPUs. In general, P2P is supported between two same GPUs with some exceptions, such as some Tesla and Quadro GPUs. @@ -100,9 +79,6 @@ This sample uses CUDA streams to overlap kernel executions with memory copies be ### [simpleSurfaceWrite](./simpleSurfaceWrite) Simple example that demonstrates the use of 2D surface references (Write-to-Texture) -### [simpleTemplates](./simpleTemplates) -This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays. - ### [simpleTexture](./simpleTexture) Simple example that demonstrates use of Textures in CUDA. @@ -121,9 +97,6 @@ This sample illustrates how to use Zero MemCopy, kernels can read and write dire ### [systemWideAtomics](./systemWideAtomics) A simple demonstration of system wide atomic instructions. -### [template](./template) -A trivial template project that can be used as a starting point to create new CUDA projects. - ### [UnifiedMemoryStreams](./UnifiedMemoryStreams) This sample demonstrates the use of OpenMP and streams with Unified Memory on a single GPU. diff --git a/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/c_cpp_properties.json b/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/extensions.json b/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/UnifiedMemoryStreams/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/UnifiedMemoryStreams/CMakeLists.txt b/cpp/0_Introduction/UnifiedMemoryStreams/CMakeLists.txt index 2fd880d0..9822a65a 100644 --- a/cpp/0_Introduction/UnifiedMemoryStreams/CMakeLists.txt +++ b/cpp/0_Introduction/UnifiedMemoryStreams/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(UnifiedMemoryStreams LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -31,23 +27,15 @@ endif() find_package(OpenMP COMPONENTS CXX) if(OpenMP_CXX_FOUND) - # Add target for UnifiedMemoryStreams add_executable(UnifiedMemoryStreams UnifiedMemoryStreams.cu) - -target_compile_options(UnifiedMemoryStreams PRIVATE $<$:--extended-lambda>) - -target_compile_features(UnifiedMemoryStreams PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(UnifiedMemoryStreams PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(UnifiedMemoryStreams PRIVATE $<$:--extended-lambda>) + target_compile_features(UnifiedMemoryStreams PRIVATE cxx_std_17 cuda_std_17) target_link_libraries(UnifiedMemoryStreams PUBLIC CUDA::cublas OpenMP::OpenMP_CXX ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "OpenMP not found - will not build sample 'UnifiedMemoryStreams'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/asyncAPI/.vscode/c_cpp_properties.json b/cpp/0_Introduction/asyncAPI/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/asyncAPI/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/asyncAPI/.vscode/extensions.json b/cpp/0_Introduction/asyncAPI/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/asyncAPI/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/asyncAPI/CMakeLists.txt b/cpp/0_Introduction/asyncAPI/CMakeLists.txt index ec50945b..a059f561 100644 --- a/cpp/0_Introduction/asyncAPI/CMakeLists.txt +++ b/cpp/0_Introduction/asyncAPI/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(asyncAPI LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for asyncAPI add_executable(asyncAPI asyncAPI.cu) target_compile_options(asyncAPI PRIVATE $<$:--extended-lambda>) target_compile_features(asyncAPI PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(asyncAPI PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/clock/.vscode/c_cpp_properties.json b/cpp/0_Introduction/clock/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/clock/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/clock/.vscode/extensions.json b/cpp/0_Introduction/clock/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/clock/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/clock/CMakeLists.txt b/cpp/0_Introduction/clock/CMakeLists.txt index b9d71781..ad472fd6 100644 --- a/cpp/0_Introduction/clock/CMakeLists.txt +++ b/cpp/0_Introduction/clock/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(clock LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for asyncAPI add_executable(clock clock.cu) target_compile_options(clock PRIVATE $<$:--extended-lambda>) target_compile_features(clock PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(clock PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/clock/README.md b/cpp/0_Introduction/clock/README.md index adf55b8c..fd1d4914 100644 --- a/cpp/0_Introduction/clock/README.md +++ b/cpp/0_Introduction/clock/README.md @@ -1,32 +1,79 @@ -# clock - Clock +# clock - Per-Block Kernel Timing with CUB Reduction ## Description -This example shows how to use the clock function to measure the performance of block of threads of a kernel accurately. +A CUDA sample that demonstrates how to use the `clock()` function to accurately measure kernel execution time on a per-block basis. Each of the 64 blocks records its own start and end SM cycle counter, then performs a block-wide parallel **min-reduction** over 512 float elements using CUB's `BlockReduce`. The host collects all timestamps and computes the average elapsed clock cycles across all blocks. + +Because blocks execute in parallel and out of order with no cross-block synchronization, each block independently measures its own execution time — this is the correct way to time GPU work at block granularity. + +## What You'll Learn + +- Using `clock()` inside a CUDA kernel to capture per-block SM cycle counts +- Performing a block-wide parallel reduction with `cub::BlockReduce` using a custom binary operator +- Loading multiple elements per thread (`ITEMS_PER_THREAD = 2`) for CUB reductions +- Understanding why only thread 0 holds the valid aggregate after a `BlockReduce` +- Querying device properties (`cudaDeviceGetAttribute`) without helper libraries +- Computing average elapsed clocks on the host from per-block timestamps ## Key Concepts -Performance Strategies +- **SM Clock Counter** — `clock()` reads the streaming multiprocessor's cycle counter; difference between two samples gives elapsed cycles for that block +- **CUB BlockReduce** — warp-shuffle-based block-scope reduction; default constructor allocates shared memory internally via `PrivateStorage()`, no explicit `TempStorage` needed +- **Items per thread** — each thread owns 2 elements; CUB's array overload of `Reduce` combines them before the cross-thread reduction +- **Per-block timing** — since blocks run independently, each block times itself; the host averages results across all blocks -## Supported SM Architectures +## Key APIs -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +### CUDA Runtime +- `cudaSetDevice` — select the active GPU for all subsequent CUDA calls +- `cudaDeviceGetAttribute` — query device properties (compute capability, SM count) without `cudaGetDeviceProperties` +- `cudaMalloc` / `cudaFree` — allocate and release device memory +- `cudaMemcpy` — transfer data between host and device +- `clock()` — device-side SM cycle counter (returns `clock_t`) -## Supported OSes +### CUB +- `cub::BlockReduce` — block-scope reduction template +- `BlockReduce::Reduce(T (&input)[ITEMS_PER_THREAD], ReductionOp op)` — reduce multiple items per thread with a custom binary operator -Linux, Windows +## Requirements -## Supported CPU Architecture +### Hardware +- NVIDIA GPU with Compute Capability 7.5 or higher -x86_64, armv7l +### Software +- CMake 3.20 or newer +- A C++17-capable host compiler -## CUDA APIs involved +## How to Build -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaMemcpy, cudaFree +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. -## Prerequisites +## How to Run -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +```bash +./clock +``` -## References (for more details) +No command-line arguments are required. The sample always runs on device 0. + +## Expected Output + +```text +CUDA Clock sample +GPU Device 0: with compute capability 8.9 and Number of SMs 142 + +Average clocks/block = 1239.640625 +``` + +The average clocks value varies by GPU and reflects how many SM cycles each block takes to complete the reduction. Blocks scheduled later on a busy GPU will show higher elapsed times. + +## Files + +- `clock.cu` — kernel implementation and host driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUB BlockReduce documentation](https://nvidia.github.io/cccl/unstable/cub/developer/block_scope.html) +- [CUDA C++ Programming Guide — clock()](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-support.html#clock-and-clock64) diff --git a/cpp/0_Introduction/clock/clock.cu b/cpp/0_Introduction/clock/clock.cu index f9fa5ae5..f46a349b 100644 --- a/cpp/0_Introduction/clock/clock.cu +++ b/cpp/0_Introduction/clock/clock.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -41,45 +41,36 @@ // CUDA runtime #include -// helper functions and utilities to work with CUDA -#include -#include +// CUB for block-scope reduction +#include + +#define NUM_BLOCKS 64 +#define NUM_THREADS 256 // This kernel computes a standard parallel reduction and evaluates the // time it takes to do that for each block. The timing results are stored // in device memory. __global__ static void timedReduction(const float *input, float *output, clock_t *timer) { - // __shared__ float shared[2 * blockDim.x]; - extern __shared__ float shared[]; - const int tid = threadIdx.x; const int bid = blockIdx.x; if (tid == 0) timer[bid] = clock(); - // Copy input. - shared[tid] = input[tid]; - shared[tid + blockDim.x] = input[tid + blockDim.x]; + // Each thread loads 2 elements and reduces them to a local min. + float thread_data[2]; + thread_data[0] = input[tid]; + thread_data[1] = input[tid + blockDim.x]; - // Perform reduction to find minimum. - for (int d = blockDim.x; d > 0; d /= 2) { - __syncthreads(); + // Block-wide min-reduction using CUB. Default constructor allocates + // shared memory internally via PrivateStorage(). + using BlockReduce = cub::BlockReduce; + float block_min = BlockReduce().Reduce(thread_data, [] __device__(float a, float b) { return fminf(a, b); }); - if (tid < d) { - float f0 = shared[tid]; - float f1 = shared[tid + d]; - - if (f1 < f0) { - shared[tid] = f1; - } - } - } - - // Write result. + // Only thread 0 holds the valid aggregate. if (tid == 0) - output[bid] = shared[0]; + output[bid] = block_min; __syncthreads(); @@ -87,33 +78,25 @@ __global__ static void timedReduction(const float *input, float *output, clock_t timer[bid + gridDim.x] = clock(); } -#define NUM_BLOCKS 64 -#define NUM_THREADS 256 - -// It's interesting to change the number of blocks and the number of threads to -// understand how to keep the hardware busy. -// -// Here are some numbers I get on my G80: -// blocks - clocks -// 1 - 3096 -// 8 - 3232 -// 16 - 3364 -// 32 - 4615 -// 64 - 9981 -// -// With less than 16 blocks some of the multiprocessors of the device are idle. -// With more than 16 you are using all the multiprocessors, but there's only one -// block per multiprocessor and that doesn't allow you to hide the latency of -// the memory. With more than 32 the speed scales linearly. - // Start the main CUDA Sample here int main(int argc, char **argv) { printf("CUDA Clock sample\n"); - // This will pick the best possible CUDA capable device - int dev = findCudaDevice(argc, (const char **)argv); + // Select device 0 as the active GPU + int devID = 0; + cudaSetDevice(devID); + // Query compute capability (major.minor) and number of SMs on the device + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); + + // Print device info + printf("GPU Device %d: with compute capability %d.%d and Number of SMs %d\n\n", devID, major, minor, smCount); + + // Device pointers for input data, per-block minimum output, and clock timestamps float *dinput = NULL; float *doutput = NULL; clock_t *dtimer = NULL; @@ -125,19 +108,19 @@ int main(int argc, char **argv) input[i] = (float)i; } - checkCudaErrors(cudaMalloc((void **)&dinput, sizeof(float) * NUM_THREADS * 2)); - checkCudaErrors(cudaMalloc((void **)&doutput, sizeof(float) * NUM_BLOCKS)); - checkCudaErrors(cudaMalloc((void **)&dtimer, sizeof(clock_t) * NUM_BLOCKS * 2)); + cudaMalloc((void **)&dinput, sizeof(float) * NUM_THREADS * 2); + cudaMalloc((void **)&doutput, sizeof(float) * NUM_BLOCKS); + cudaMalloc((void **)&dtimer, sizeof(clock_t) * NUM_BLOCKS * 2); - checkCudaErrors(cudaMemcpy(dinput, input, sizeof(float) * NUM_THREADS * 2, cudaMemcpyHostToDevice)); + cudaMemcpy(dinput, input, sizeof(float) * NUM_THREADS * 2, cudaMemcpyHostToDevice); - timedReduction<<>>(dinput, doutput, dtimer); + timedReduction<<>>(dinput, doutput, dtimer); - checkCudaErrors(cudaMemcpy(timer, dtimer, sizeof(clock_t) * NUM_BLOCKS * 2, cudaMemcpyDeviceToHost)); + cudaMemcpy(timer, dtimer, sizeof(clock_t) * NUM_BLOCKS * 2, cudaMemcpyDeviceToHost); - checkCudaErrors(cudaFree(dinput)); - checkCudaErrors(cudaFree(doutput)); - checkCudaErrors(cudaFree(dtimer)); + cudaFree(dinput); + cudaFree(doutput); + cudaFree(dtimer); long double avgElapsedClocks = 0; diff --git a/cpp/0_Introduction/clock_nvrtc/.vscode/c_cpp_properties.json b/cpp/0_Introduction/clock_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/clock_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/clock_nvrtc/.vscode/extensions.json b/cpp/0_Introduction/clock_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/clock_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/clock_nvrtc/CMakeLists.txt b/cpp/0_Introduction/clock_nvrtc/CMakeLists.txt deleted file mode 100644 index 9d5abe4c..00000000 --- a/cpp/0_Introduction/clock_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(clock_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add sample target executable -add_executable(clock_nvrtc clock.cpp) - -target_compile_options(clock_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(clock_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -target_link_libraries(clock_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy clock_kernel.cu to the output directory -add_custom_command(TARGET clock_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/clock_kernel.cu ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/clock_nvrtc/README.md b/cpp/0_Introduction/clock_nvrtc/README.md deleted file mode 100644 index d26eb3a7..00000000 --- a/cpp/0_Introduction/clock_nvrtc/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# clock_nvrtc - Clock libNVRTC - -## Description - -This example shows how to use the clock function using libNVRTC to measure the performance of block of threads of a kernel accurately. - -## Key Concepts - -Performance Strategies, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuMemcpyHtoD, cuCtxSynchronize, cuMemAlloc, cuMemFree, cuModuleGetFunction - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaBlockSize, cudaGridSize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/0_Introduction/clock_nvrtc/clock.cpp b/cpp/0_Introduction/clock_nvrtc/clock.cpp deleted file mode 100644 index d301703c..00000000 --- a/cpp/0_Introduction/clock_nvrtc/clock.cpp +++ /dev/null @@ -1,137 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This example shows how to use the clock function to measure the performance - * of block of threads of a kernel accurately. Blocks are executed in parallel - * and out of order. Since there's no synchronization mechanism between blocks, - * we measure the clock once for each block. The clock samples are written to - * device memory. - */ - -// System includes -#include -#include -#include -#include -#include - -// helper functions and utilities to work with CUDA -#include - -#define NUM_BLOCKS 64 - -#define NUM_THREADS 256 - -// It's interesting to change the number of blocks and the number of threads to -// understand how to keep the hardware busy. -// - -// Here are some numbers I get on my G80: -// blocks - clocks -// 1 - 3096 -// 8 - 3232 -// 16 - 3364 -// 32 - 4615 -// 64 - 9981 - -// -// With less than 16 blocks some of the multiprocessors of the device are idle. -// With -// more than 16 you are using all the multiprocessors, but there's only one -// block per -// multiprocessor and that doesn't allow you to hide the latency of the memory. -// With -// more than 32 the speed scales linearly. - -// Start the main CUDA Sample here - -int main(int argc, char **argv) -{ - printf("CUDA Clock sample\n"); - - typedef long clock_t; - - clock_t timer[NUM_BLOCKS * 2]; - - float input[NUM_THREADS * 2]; - - for (int i = 0; i < NUM_THREADS * 2; i++) { - input[i] = (float)i; - } - - char *cubin, *kernel_file; - size_t cubinSize; - - kernel_file = sdkFindFilePath("clock_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - - CUmodule module = loadCUBIN(cubin, argc, argv); - CUfunction kernel_addr; - - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "timedReduction")); - - dim3 cudaBlockSize(NUM_THREADS, 1, 1); - dim3 cudaGridSize(NUM_BLOCKS, 1, 1); - - CUdeviceptr dinput, doutput, dtimer; - checkCudaErrors(cuMemAlloc(&dinput, sizeof(float) * NUM_THREADS * 2)); - checkCudaErrors(cuMemAlloc(&doutput, sizeof(float) * NUM_BLOCKS)); - checkCudaErrors(cuMemAlloc(&dtimer, sizeof(clock_t) * NUM_BLOCKS * 2)); - checkCudaErrors(cuMemcpyHtoD(dinput, input, sizeof(float) * NUM_THREADS * 2)); - - void *arr[] = {(void *)&dinput, (void *)&doutput, (void *)&dtimer}; - - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - cudaBlockSize.x, - cudaBlockSize.y, - cudaBlockSize.z, /* block dim */ - sizeof(float) * 2 * NUM_THREADS, - 0, /* shared mem, stream */ - &arr[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); - checkCudaErrors(cuMemcpyDtoH(timer, dtimer, sizeof(clock_t) * NUM_BLOCKS * 2)); - checkCudaErrors(cuMemFree(dinput)); - checkCudaErrors(cuMemFree(doutput)); - checkCudaErrors(cuMemFree(dtimer)); - - long double avgElapsedClocks = 0; - - for (int i = 0; i < NUM_BLOCKS; i++) { - avgElapsedClocks += (long double)(timer[i + NUM_BLOCKS] - timer[i]); - } - - avgElapsedClocks = avgElapsedClocks / NUM_BLOCKS; - printf("Average clocks/block = %Lf\n", avgElapsedClocks); - - return EXIT_SUCCESS; -} diff --git a/cpp/0_Introduction/clock_nvrtc/clock_kernel.cu b/cpp/0_Introduction/clock_nvrtc/clock_kernel.cu deleted file mode 100644 index 4bc854b9..00000000 --- a/cpp/0_Introduction/clock_nvrtc/clock_kernel.cu +++ /dev/null @@ -1,77 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This example shows how to use the clock function to measure the performance - * of block of threads of a kernel accurately. Blocks are executed in parallel - * and out of order. Since there's no synchronization mechanism between blocks, - * we measure the clock once for each block. The clock samples are written to - * device memory. - */ - -// This kernel computes a standard parallel reduction and evaluates the -// time it takes to do that for each block. The timing results are stored -// in device memory. - -extern "C" __global__ void timedReduction(const float *input, float *output, clock_t *timer) -{ - // __shared__ float shared[2 * blockDim.x]; - extern __shared__ float shared[]; - - const int tid = threadIdx.x; - const int bid = blockIdx.x; - - if (tid == 0) - timer[bid] = clock(); - - // Copy input. - shared[tid] = input[tid]; - shared[tid + blockDim.x] = input[tid + blockDim.x]; - - // Perform reduction to find minimum. - for (int d = blockDim.x; d > 0; d /= 2) { - __syncthreads(); - - if (tid < d) { - float f0 = shared[tid]; - float f1 = shared[tid + d]; - - if (f1 < f0) { - shared[tid] = f1; - } - } - } - - // Write result. - if (tid == 0) - output[bid] = shared[0]; - - __syncthreads(); - - if (tid == 0) - timer[bid + gridDim.x] = clock(); -} diff --git a/cpp/0_Introduction/cudaOpenMP/.vscode/c_cpp_properties.json b/cpp/0_Introduction/cudaOpenMP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/cudaOpenMP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/cudaOpenMP/.vscode/extensions.json b/cpp/0_Introduction/cudaOpenMP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/cudaOpenMP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/cudaOpenMP/CMakeLists.txt b/cpp/0_Introduction/cudaOpenMP/CMakeLists.txt index 991936c1..b7bb5899 100644 --- a/cpp/0_Introduction/cudaOpenMP/CMakeLists.txt +++ b/cpp/0_Introduction/cudaOpenMP/CMakeLists.txt @@ -1,43 +1,35 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaOpenMP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(OpenMP) -# Source file if(OpenMP_CXX_FOUND) -# Add target for asyncAPI add_executable(cudaOpenMP cudaOpenMP.cu) -target_compile_options(cudaOpenMP PRIVATE $<$:--extended-lambda>) + target_compile_options(cudaOpenMP PRIVATE $<$:--extended-lambda>) -target_compile_features(cudaOpenMP PRIVATE cxx_std_17 cuda_std_17) + target_compile_features(cudaOpenMP PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(cudaOpenMP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) target_link_libraries(cudaOpenMP PUBLIC OpenMP::OpenMP_CXX ) + + include(InstallSamples) + setup_samples_install() else() message(STATUS "OpenMP not found - will not build sample 'cudaOpenMP'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/fp16ScalarProduct/.vscode/c_cpp_properties.json b/cpp/0_Introduction/fp16ScalarProduct/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/fp16ScalarProduct/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/fp16ScalarProduct/.vscode/extensions.json b/cpp/0_Introduction/fp16ScalarProduct/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/fp16ScalarProduct/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/fp16ScalarProduct/CMakeLists.txt b/cpp/0_Introduction/fp16ScalarProduct/CMakeLists.txt index 3f2a3d34..17e81190 100644 --- a/cpp/0_Introduction/fp16ScalarProduct/CMakeLists.txt +++ b/cpp/0_Introduction/fp16ScalarProduct/CMakeLists.txt @@ -1,33 +1,31 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(fp16ScalarProduct LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) if(ENABLE_CUDA_DEBUG) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for asyncAPI add_executable(fp16ScalarProduct fp16ScalarProduct.cu) target_compile_options(fp16ScalarProduct PRIVATE $<$:--extended-lambda>) target_compile_features(fp16ScalarProduct PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(fp16ScalarProduct PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/matrixMul/.vscode/c_cpp_properties.json b/cpp/0_Introduction/matrixMul/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/matrixMul/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/matrixMul/.vscode/extensions.json b/cpp/0_Introduction/matrixMul/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/matrixMul/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/matrixMul/CMakeLists.txt b/cpp/0_Introduction/matrixMul/CMakeLists.txt index 9293df3f..e38ad491 100644 --- a/cpp/0_Introduction/matrixMul/CMakeLists.txt +++ b/cpp/0_Introduction/matrixMul/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(matrixMul LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for asyncAPI add_executable(matrixMul matrixMul.cu) target_compile_options(matrixMul PRIVATE $<$:--extended-lambda>) target_compile_features(matrixMul PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(matrixMul PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/matrixMul/README.md b/cpp/0_Introduction/matrixMul/README.md index b7d9c7fb..5f6b5cd6 100644 --- a/cpp/0_Introduction/matrixMul/README.md +++ b/cpp/0_Introduction/matrixMul/README.md @@ -2,7 +2,7 @@ ## Description -This sample implements matrix multiplication and is exactly the same as the second example of the [Shared Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#shared-memory) section of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the CUDA 4.0+ interface for cuBLAS to demonstrate high-performance performance for matrix multiplication. +This sample implements matrix multiplication. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the CUDA 4.0+ interface for cuBLAS to demonstrate high-performance matrix multiplication. ## Key Concepts @@ -14,7 +14,7 @@ CUDA Runtime API, Linear Algebra ## Supported OSes -Linux, Windows +Linux, Windows, QNX (QNX Safety cross-build with CUDA Safe toolkit) ## Supported CPU Architecture diff --git a/cpp/0_Introduction/matrixMul/matrixMul.cu b/cpp/0_Introduction/matrixMul/matrixMul.cu index 957be0f7..2e00c4ce 100644 --- a/cpp/0_Introduction/matrixMul/matrixMul.cu +++ b/cpp/0_Introduction/matrixMul/matrixMul.cu @@ -44,8 +44,12 @@ #include // CUDA runtime -#include #include +// The CUDA Safe runtime does not ship the profiler API; gate it on header availability. +#if defined(__has_include) && __has_include() +#include +#define HAVE_CUDA_PROFILER_API 1 +#endif // Helper functions and utilities to work with CUDA #include @@ -141,11 +145,11 @@ int MatrixMultiply(int argc, char **argv, int block_size, const dim3 &dimsA, con unsigned int size_A = dimsA.x * dimsA.y; unsigned int mem_size_A = sizeof(float) * size_A; float *h_A; - checkCudaErrors(cudaMallocHost(&h_A, mem_size_A)); + checkCudaErrors(cudaMallocHost(&h_A, mem_size_A, 0)); unsigned int size_B = dimsB.x * dimsB.y; unsigned int mem_size_B = sizeof(float) * size_B; float *h_B; - checkCudaErrors(cudaMallocHost(&h_B, mem_size_B)); + checkCudaErrors(cudaMallocHost(&h_B, mem_size_B, 0)); cudaStream_t stream; // Initialize host memory @@ -160,7 +164,7 @@ int MatrixMultiply(int argc, char **argv, int block_size, const dim3 &dimsA, con dim3 dimsC(dimsB.x, dimsA.y, 1); unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float); float *h_C; - checkCudaErrors(cudaMallocHost(&h_C, mem_size_C)); + checkCudaErrors(cudaMallocHost(&h_C, mem_size_C, 0)); if (h_C == NULL) { fprintf(stderr, "Failed to allocate host matrix C!\n"); @@ -334,9 +338,13 @@ int main(int argc, char **argv) printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y); +#ifdef HAVE_CUDA_PROFILER_API checkCudaErrors(cudaProfilerStart()); +#endif int matrix_result = MatrixMultiply(argc, argv, block_size, dimsA, dimsB); +#ifdef HAVE_CUDA_PROFILER_API checkCudaErrors(cudaProfilerStop()); +#endif exit(matrix_result); } diff --git a/cpp/0_Introduction/matrixMulDrv/.vscode/c_cpp_properties.json b/cpp/0_Introduction/matrixMulDrv/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/matrixMulDrv/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/matrixMulDrv/.vscode/extensions.json b/cpp/0_Introduction/matrixMulDrv/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/matrixMulDrv/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/matrixMulDrv/CMakeLists.txt b/cpp/0_Introduction/matrixMulDrv/CMakeLists.txt index 735cbe62..39d7d95f 100644 --- a/cpp/0_Introduction/matrixMulDrv/CMakeLists.txt +++ b/cpp/0_Introduction/matrixMulDrv/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(matrixMulDrv LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for matrixMulDrv add_executable(matrixMulDrv matrixMulDrv.cpp) target_compile_options(matrixMulDrv PRIVATE $<$:--extended-lambda>) target_compile_features(matrixMulDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(matrixMulDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(matrixMulDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -56,7 +48,7 @@ endif() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -67,6 +59,5 @@ add_custom_target(generate_fatbin_matmulDrv ALL DEPENDS ${CUDA_FATBIN_FILE}) # Ensure matrixMulDrv depends on the fatbin add_dependencies(matrixMulDrv generate_fatbin_matmulDrv) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/c_cpp_properties.json b/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/extensions.json b/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/matrixMulDynlinkJIT/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/matrixMulDynlinkJIT/CMakeLists.txt b/cpp/0_Introduction/matrixMulDynlinkJIT/CMakeLists.txt index 6cd324cd..fe545bac 100644 --- a/cpp/0_Introduction/matrixMulDynlinkJIT/CMakeLists.txt +++ b/cpp/0_Introduction/matrixMulDynlinkJIT/CMakeLists.txt @@ -1,26 +1,20 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(matrixMulDynlinkJIT LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for matrixMulDynlinkJIT add_executable(matrixMulDynlinkJIT cuda_drvapi_dynlink.c matrixMulDynlinkJIT.cpp matrixMul_gold.cpp matrixMul_kernel_32_ptxdump.c matrixMul_kernel_64_ptxdump.c) target_compile_options(matrixMulDynlinkJIT PRIVATE $<$:--extended-lambda>) @@ -28,7 +22,6 @@ target_compile_options(matrixMulDynlinkJIT PRIVATE $<$:-- target_compile_features(matrixMulDynlinkJIT PRIVATE cxx_std_17 cuda_std_17) set_target_properties(matrixMulDynlinkJIT PROPERTIES - CUDA_SEPARABLE_COMPILATION ON POSITION_INDEPENDENT_CODE OFF ) @@ -46,6 +39,5 @@ if(${CMAKE_SYSTEM_NAME} STREQUAL "Linux") target_link_libraries(matrixMulDynlinkJIT PUBLIC dl) endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/matrixMul_nvrtc/.vscode/c_cpp_properties.json b/cpp/0_Introduction/matrixMul_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/matrixMul_nvrtc/.vscode/extensions.json b/cpp/0_Introduction/matrixMul_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/matrixMul_nvrtc/CMakeLists.txt b/cpp/0_Introduction/matrixMul_nvrtc/CMakeLists.txt deleted file mode 100644 index adaa1d48..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,61 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(matrixMul_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add sample target executable -add_executable(matrixMul_nvrtc matrixMul.cpp) - -target_compile_options(matrixMul_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(matrixMul_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -target_link_libraries(matrixMul_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# The primary directory of CUDAToolkit_INCLUDE_DIRS is the CUDA Toolkit's include directory for finding the header files. -list(GET CUDAToolkit_INCLUDE_DIRS 0 CUDA_INCLUDE_DIR) - -# Copy clock_kernel.cu to the output directory -add_custom_command(TARGET matrixMul_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/matrixMul_kernel.cu ${CUDA_INCLUDE_DIR}/cooperative_groups.h ${CMAKE_CURRENT_BINARY_DIR} -) - -add_custom_command(TARGET matrixMul_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CUDA_INCLUDE_DIR}/cooperative_groups ${CMAKE_CURRENT_BINARY_DIR}/cooperative_groups -) - -add_custom_command(TARGET matrixMul_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CUDA_INCLUDE_DIR}/cccl/nv ${CMAKE_CURRENT_BINARY_DIR}/nv -) - -add_custom_command(TARGET matrixMul_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CUDA_INCLUDE_DIR}/cccl/cuda ${CMAKE_CURRENT_BINARY_DIR}/cuda -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/matrixMul_nvrtc/README.md b/cpp/0_Introduction/matrixMul_nvrtc/README.md deleted file mode 100644 index ee7b4c20..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# matrixMul_nvrtc - Matrix Multiplication with libNVRTC - -## Description - -This sample implements matrix multiplication and is exactly the same as the second example of the [Shared Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#shared-memory) section of the programming guide. It has been written for clarity of exposition to illustrate various CUDA programming principles, not with the goal of providing the most performant generic kernel for matrix multiplication. To illustrate GPU performance for matrix multiply, this sample also shows how to use the CUDA 4.0+ interface for cuBLAS to demonstrate high-performance performance for matrix multiplication. - -## Key Concepts - -CUDA Runtime API, Linear Algebra, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuMemcpyHtoD, cuCtxSynchronize, cuMemAlloc, cuMemFree, cuModuleGetFunction - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/0_Introduction/matrixMul_nvrtc/matrixMul.cpp b/cpp/0_Introduction/matrixMul_nvrtc/matrixMul.cpp deleted file mode 100644 index b54eee1f..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/matrixMul.cpp +++ /dev/null @@ -1,249 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/** - * Matrix multiplication: C = A * B. - * Host code. - * - * This sample implements matrix multiplication as described in Chapter 3 - * of the programming guide. - * It has been written for clarity of exposition to illustrate various CUDA - * programming principles, not with the goal of providing the most - * performant generic kernel for matrix multiplication. - * - * See also: - * V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra," - * in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08), - * Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11. - */ - -// System includes -#include -#include - -// CUDA runtime -#include - -#include "nvrtc_helper.h" - -// Helper functions and utilities to work with CUDA -#include - -void constantInit(float *data, int size, float val) -{ - for (int i = 0; i < size; ++i) { - data[i] = val; - } -} - -/** - * Run a simple test of matrix multiplication using CUDA - */ -int matrixMultiply(int argc, char **argv, int block_size, dim3 &dimsA, dim3 &dimsB) -{ - // Allocate host memory for matrices A and B - unsigned int size_A = dimsA.x * dimsA.y; - unsigned int mem_size_A = sizeof(float) * size_A; - float *h_A = (float *)malloc(mem_size_A); - unsigned int size_B = dimsB.x * dimsB.y; - unsigned int mem_size_B = sizeof(float) * size_B; - float *h_B = (float *)malloc(mem_size_B); - - // Initialize host memory - const float valB = 0.01f; - constantInit(h_A, size_A, 1.0f); - constantInit(h_B, size_B, valB); - - // Allocate device memory - CUdeviceptr d_A, d_B, d_C; - - char *cubin, *kernel_file; - size_t cubinSize; - - kernel_file = sdkFindFilePath("matrixMul_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 1); - - CUmodule module = loadCUBIN(cubin, argc, argv); - - // Allocate host matrix C - dim3 dimsC(dimsB.x, dimsA.y, 1); - unsigned int mem_size_C = dimsC.x * dimsC.y * sizeof(float); - float *h_C = (float *)malloc(mem_size_C); - - if (h_C == NULL) { - fprintf(stderr, "Failed to allocate host matrix C!\n"); - exit(EXIT_FAILURE); - } - - checkCudaErrors(cuMemAlloc(&d_A, mem_size_A)); - checkCudaErrors(cuMemAlloc(&d_B, mem_size_B)); - checkCudaErrors(cuMemAlloc(&d_C, mem_size_C)); - - // copy host memory to device - checkCudaErrors(cuMemcpyHtoD(d_A, h_A, mem_size_A)); - checkCudaErrors(cuMemcpyHtoD(d_B, h_B, mem_size_B)); - - // Setup execution parameters - dim3 threads(block_size, block_size); - dim3 grid(dimsB.x / threads.x, dimsA.y / threads.y); - - // Create and start timer - printf("Computing result using CUDA Kernel...\n"); - - CUfunction kernel_addr; - if (block_size == 16) { - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "matrixMulCUDA_block16")); - } - else { - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "matrixMulCUDA_block32")); - } - - void *arr[] = {(void *)&d_C, (void *)&d_A, (void *)&d_B, (void *)&dimsA.x, (void *)&dimsB.x}; - - // Execute the kernel - int nIter = 300; - - for (int j = 0; j < nIter; j++) { - checkCudaErrors(cuLaunchKernel(kernel_addr, - grid.x, - grid.y, - grid.z, /* grid dim */ - threads.x, - threads.y, - threads.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &arr[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); - } - - // Copy result from device to host - checkCudaErrors(cuMemcpyDtoH(h_C, d_C, mem_size_C)); - - printf("Checking computed result for correctness: "); - - bool correct = true; - - // test relative error by the formula - // |_cpu - _gpu|/<|x|, |y|> < eps - - double eps = 1.e-6; // machine zero - - for (int i = 0; i < (int)(dimsC.x * dimsC.y); i++) { - double abs_err = fabs(h_C[i] - (dimsA.x * valB)); - double dot_length = dimsA.x; - double abs_val = fabs(h_C[i]); - double rel_err = abs_err / abs_val / dot_length; - - if (rel_err > eps) { - printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, h_C[i], dimsA.x * valB, eps); - correct = false; - } - } - - printf("%s\n", correct ? "Result = PASS" : "Result = FAIL"); - - printf("\nNOTE: The CUDA Samples are not meant for performance measurements. " - "Results may vary when GPU Boost is enabled.\n"); - - // Clean up memory - free(h_A); - free(h_B); - free(h_C); - - checkCudaErrors(cuMemFree(d_A)); - checkCudaErrors(cuMemFree(d_B)); - checkCudaErrors(cuMemFree(d_C)); - - if (correct) { - return EXIT_SUCCESS; - } - else { - return EXIT_FAILURE; - } -} - -/** - * Program main - */ - -int main(int argc, char **argv) -{ - printf("[Matrix Multiply Using CUDA] - Starting...\n"); - - if (checkCmdLineFlag(argc, (const char **)argv, "help") || checkCmdLineFlag(argc, (const char **)argv, "?")) { - printf("Usage -device=n (n >= 0 for deviceID)\n"); - printf(" -wA=WidthA -hA=HeightA (Width x Height of Matrix A)\n"); - printf(" -wB=WidthB -hB=HeightB (Width x Height of Matrix B)\n"); - printf(" Note: Outer matrix dimensions of A & B matrices must be equal.\n"); - - exit(EXIT_SUCCESS); - } - - int block_size = 32; - - // original: - dim3 dimsA(5 * 2 * block_size, 5 * 2 * block_size, 1); - dim3 dimsB(5 * 4 * block_size, 5 * 2 * block_size, 1); - - // reduce sizes to avoid running out of memory - // dim3 dimsA(32,32, 1); - // dim3 dimsB(32,32,1); - - // width of Matrix A - if (checkCmdLineFlag(argc, (const char **)argv, "wA")) { - dimsA.x = getCmdLineArgumentInt(argc, (const char **)argv, "wA"); - } - - // height of Matrix A - if (checkCmdLineFlag(argc, (const char **)argv, "hA")) { - dimsA.y = getCmdLineArgumentInt(argc, (const char **)argv, "hA"); - } - - // width of Matrix B - if (checkCmdLineFlag(argc, (const char **)argv, "wB")) { - dimsB.x = getCmdLineArgumentInt(argc, (const char **)argv, "wB"); - } - - // height of Matrix B - if (checkCmdLineFlag(argc, (const char **)argv, "hB")) { - dimsB.y = getCmdLineArgumentInt(argc, (const char **)argv, "hB"); - } - - if (dimsA.x != dimsB.y) { - printf("Error: outer matrix dimensions must be equal. (%d != %d)\n", dimsA.x, dimsB.y); - exit(EXIT_FAILURE); - } - - printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", dimsA.x, dimsA.y, dimsB.x, dimsB.y); - - int matrix_result = matrixMultiply(argc, argv, block_size, dimsA, dimsB); - - exit(matrix_result); -} diff --git a/cpp/0_Introduction/matrixMul_nvrtc/matrixMul_kernel.cu b/cpp/0_Introduction/matrixMul_nvrtc/matrixMul_kernel.cu deleted file mode 100644 index 0b8ce226..00000000 --- a/cpp/0_Introduction/matrixMul_nvrtc/matrixMul_kernel.cu +++ /dev/null @@ -1,130 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/** - * Matrix multiplication: C = A * B. - * Host code. - * - * This sample implements matrix multiplication as described in Chapter 3 - * of the programming guide. - * It has been written for clarity of exposition to illustrate various CUDA - * programming principles, not with the goal of providing the most - * performant generic kernel for matrix multiplication. - * - * See also: - * V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra," - * in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08), - * Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11. - */ - -/** - * Matrix multiplication (CUDA Kernel) on the device: C = A * B - * wA is A's width and wB is B's width - */ - -#include - -template __device__ void matrixMulCUDA(float *C, float *A, float *B, int wA, int wB) -{ - // Handle to thread block group - cooperative_groups::thread_block cta = cooperative_groups::this_thread_block(); - // Block index - int bx = blockIdx.x; - int by = blockIdx.y; - - // Thread index - int tx = threadIdx.x; - int ty = threadIdx.y; - - // Index of the first sub-matrix of A processed by the block - int aBegin = wA * BLOCK_SIZE * by; - - // Index of the last sub-matrix of A processed by the block - int aEnd = aBegin + wA - 1; - - // Step size used to iterate through the sub-matrices of A - int aStep = BLOCK_SIZE; - - // Index of the first sub-matrix of B processed by the block - int bBegin = BLOCK_SIZE * bx; - - // Step size used to iterate through the sub-matrices of B - int bStep = BLOCK_SIZE * wB; - - // Csub is used to store the element of the block sub-matrix - // that is computed by the thread - float Csub = 0; - - // Loop over all the sub-matrices of A and B - // required to compute the block sub-matrix - for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) { - // Declaration of the shared memory array As used to - // store the sub-matrix of A - __shared__ float As[BLOCK_SIZE][BLOCK_SIZE]; - - // Declaration of the shared memory array Bs used to - // store the sub-matrix of B - __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE]; - - // Load the matrices from device memory - // to shared memory; each thread loads - // one element of each matrix - As[ty][tx] = A[a + wA * ty + tx]; - Bs[ty][tx] = B[b + wB * ty + tx]; - - // Synchronize to make sure the matrices are loaded - cooperative_groups::sync(cta); - -// Multiply the two matrices together; -// each thread computes one element -// of the block sub-matrix -#pragma unroll - for (int k = 0; k < BLOCK_SIZE; ++k) { - Csub += As[ty][k] * Bs[k][tx]; - } - - // Synchronize to make sure that the preceding - // computation is done before loading two new - // sub-matrices of A and B in the next iteration - cooperative_groups::sync(cta); - } - - // Write the block sub-matrix to device memory; - // each thread writes one element - int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx; - C[c + wB * ty + tx] = Csub; -} - -extern "C" __global__ void matrixMulCUDA_block16(float *C, float *A, float *B, int wA, int wB) -{ - matrixMulCUDA<16>(C, A, B, wA, wB); -} - -extern "C" __global__ void matrixMulCUDA_block32(float *C, float *A, float *B, int wA, int wB) -{ - matrixMulCUDA<32>(C, A, B, wA, wB); -} diff --git a/cpp/0_Introduction/mergeSort/.vscode/c_cpp_properties.json b/cpp/0_Introduction/mergeSort/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/mergeSort/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/mergeSort/.vscode/extensions.json b/cpp/0_Introduction/mergeSort/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/mergeSort/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/mergeSort/README.md b/cpp/0_Introduction/mergeSort/README.md deleted file mode 100644 index 9c95b53b..00000000 --- a/cpp/0_Introduction/mergeSort/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# mergeSort - Merge Sort - -## Description - -This sample implements a merge sort (also known as Batcher's sort), algorithms belonging to the class of sorting networks. While generally subefficient on large sequences compared to algorithms with better asymptotic algorithmic complexity (i.e. merge sort or radix sort), may be the algorithms of choice for sorting batches of short- to mid-sized (key, value) array pairs. Refer to the excellent tutorial by H. W. Lang http://www.iti.fh-flensburg.de/lang/algorithmen/sortieren/networks/indexen.htm - -## Key Concepts - -Data-Parallel Algorithms - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaDeviceSynchronize, cudaMemcpy, cudaFree - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) diff --git a/cpp/0_Introduction/mergeSort/bitonic.cu b/cpp/0_Introduction/mergeSort/bitonic.cu deleted file mode 100644 index 144e55e4..00000000 --- a/cpp/0_Introduction/mergeSort/bitonic.cu +++ /dev/null @@ -1,281 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include - -namespace cg = cooperative_groups; -#include -#include - -#include "mergeSort_common.h" - -inline __device__ void Comparator(uint &keyA, uint &valA, uint &keyB, uint &valB, uint arrowDir) -{ - uint t; - - if ((keyA > keyB) == arrowDir) { - t = keyA; - keyA = keyB; - keyB = t; - t = valA; - valA = valB; - valB = t; - } -} - -__global__ void -bitonicSortSharedKernel(uint *d_DstKey, uint *d_DstVal, uint *d_SrcKey, uint *d_SrcVal, uint arrayLength, uint sortDir) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - // Shared memory storage for one or more short vectors - __shared__ uint s_key[SHARED_SIZE_LIMIT]; - __shared__ uint s_val[SHARED_SIZE_LIMIT]; - - // Offset to the beginning of subbatch and load data - d_SrcKey += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_SrcVal += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_DstKey += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_DstVal += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - s_key[threadIdx.x + 0] = d_SrcKey[0]; - s_val[threadIdx.x + 0] = d_SrcVal[0]; - s_key[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = d_SrcKey[(SHARED_SIZE_LIMIT / 2)]; - s_val[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = d_SrcVal[(SHARED_SIZE_LIMIT / 2)]; - - for (uint size = 2; size < arrayLength; size <<= 1) { - // Bitonic merge - uint dir = (threadIdx.x & (size / 2)) != 0; - - for (uint stride = size / 2; stride > 0; stride >>= 1) { - cg::sync(cta); - uint pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); - Comparator(s_key[pos + 0], s_val[pos + 0], s_key[pos + stride], s_val[pos + stride], dir); - } - } - - // ddd == sortDir for the last bitonic merge step - { - for (uint stride = arrayLength / 2; stride > 0; stride >>= 1) { - cg::sync(cta); - uint pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); - Comparator(s_key[pos + 0], s_val[pos + 0], s_key[pos + stride], s_val[pos + stride], sortDir); - } - } - - cg::sync(cta); - d_DstKey[0] = s_key[threadIdx.x + 0]; - d_DstVal[0] = s_val[threadIdx.x + 0]; - d_DstKey[(SHARED_SIZE_LIMIT / 2)] = s_key[threadIdx.x + (SHARED_SIZE_LIMIT / 2)]; - d_DstVal[(SHARED_SIZE_LIMIT / 2)] = s_val[threadIdx.x + (SHARED_SIZE_LIMIT / 2)]; -} - -// Helper function (also used by odd-even merge sort) -extern "C" uint factorRadix2(uint *log2L, uint L) -{ - if (!L) { - *log2L = 0; - return 0; - } - else { - for (*log2L = 0; (L & 1) == 0; L >>= 1, *log2L++) - ; - - return L; - } -} - -extern "C" void bitonicSortShared(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint batchSize, - uint arrayLength, - uint sortDir) -{ - // Nothing to sort - if (arrayLength < 2) { - return; - } - - // Only power-of-two array lengths are supported by this implementation - uint log2L; - uint factorizationRemainder = factorRadix2(&log2L, arrayLength); - assert(factorizationRemainder == 1); - - uint blockCount = batchSize * arrayLength / SHARED_SIZE_LIMIT; - uint threadCount = SHARED_SIZE_LIMIT / 2; - - assert(arrayLength <= SHARED_SIZE_LIMIT); - assert((batchSize * arrayLength) % SHARED_SIZE_LIMIT == 0); - - bitonicSortSharedKernel<<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, arrayLength, sortDir); - getLastCudaError("bitonicSortSharedKernel<<<>>> failed!\n"); -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 3: merge elementary intervals -//////////////////////////////////////////////////////////////////////////////// -static inline __host__ __device__ uint iDivUp(uint a, uint b) { return ((a % b) == 0) ? (a / b) : (a / b + 1); } - -static inline __host__ __device__ uint getSampleCount(uint dividend) { return iDivUp(dividend, SAMPLE_STRIDE); } - -template -static inline __device__ void -ComparatorExtended(uint &keyA, uint &valA, uint &flagA, uint &keyB, uint &valB, uint &flagB, uint arrowDir) -{ - uint t; - - if ((!(flagA || flagB) && ((keyA > keyB) == arrowDir)) || ((arrowDir == sortDir) && (flagA == 1)) - || ((arrowDir != sortDir) && (flagB == 1))) { - t = keyA; - keyA = keyB; - keyB = t; - t = valA; - valA = valB; - valB = t; - t = flagA; - flagA = flagB; - flagB = t; - } -} - -template -__global__ void bitonicMergeElementaryIntervalsKernel(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint *d_LimitsA, - uint *d_LimitsB, - uint stride, - uint N) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - __shared__ uint s_key[2 * SAMPLE_STRIDE]; - __shared__ uint s_val[2 * SAMPLE_STRIDE]; - __shared__ uint s_inf[2 * SAMPLE_STRIDE]; - - const uint intervalI = blockIdx.x & ((2 * stride) / SAMPLE_STRIDE - 1); - const uint segmentBase = (blockIdx.x - intervalI) * SAMPLE_STRIDE; - d_SrcKey += segmentBase; - d_SrcVal += segmentBase; - d_DstKey += segmentBase; - d_DstVal += segmentBase; - - // Set up threadblock-wide parameters - __shared__ uint startSrcA, lenSrcA, startSrcB, lenSrcB, startDst; - - if (threadIdx.x == 0) { - uint segmentElementsA = stride; - uint segmentElementsB = umin(stride, N - segmentBase - stride); - uint segmentSamplesA = stride / SAMPLE_STRIDE; - uint segmentSamplesB = getSampleCount(segmentElementsB); - uint segmentSamples = segmentSamplesA + segmentSamplesB; - - startSrcA = d_LimitsA[blockIdx.x]; - startSrcB = d_LimitsB[blockIdx.x]; - startDst = startSrcA + startSrcB; - - uint endSrcA = (intervalI + 1 < segmentSamples) ? d_LimitsA[blockIdx.x + 1] : segmentElementsA; - uint endSrcB = (intervalI + 1 < segmentSamples) ? d_LimitsB[blockIdx.x + 1] : segmentElementsB; - lenSrcA = endSrcA - startSrcA; - lenSrcB = endSrcB - startSrcB; - } - - s_inf[threadIdx.x + 0] = 1; - s_inf[threadIdx.x + SAMPLE_STRIDE] = 1; - - // Load input data - cg::sync(cta); - - if (threadIdx.x < lenSrcA) { - s_key[threadIdx.x] = d_SrcKey[0 + startSrcA + threadIdx.x]; - s_val[threadIdx.x] = d_SrcVal[0 + startSrcA + threadIdx.x]; - s_inf[threadIdx.x] = 0; - } - - // Prepare for bitonic merge by inversing the ordering - if (threadIdx.x < lenSrcB) { - s_key[2 * SAMPLE_STRIDE - 1 - threadIdx.x] = d_SrcKey[stride + startSrcB + threadIdx.x]; - s_val[2 * SAMPLE_STRIDE - 1 - threadIdx.x] = d_SrcVal[stride + startSrcB + threadIdx.x]; - s_inf[2 * SAMPLE_STRIDE - 1 - threadIdx.x] = 0; - } - - //"Extended" bitonic merge - for (uint stride = SAMPLE_STRIDE; stride > 0; stride >>= 1) { - cg::sync(cta); - uint pos = 2 * threadIdx.x - (threadIdx.x & (stride - 1)); - ComparatorExtended(s_key[pos + 0], - s_val[pos + 0], - s_inf[pos + 0], - s_key[pos + stride], - s_val[pos + stride], - s_inf[pos + stride], - sortDir); - } - - // Store sorted data - cg::sync(cta); - d_DstKey += startDst; - d_DstVal += startDst; - - if (threadIdx.x < lenSrcA) { - d_DstKey[threadIdx.x] = s_key[threadIdx.x]; - d_DstVal[threadIdx.x] = s_val[threadIdx.x]; - } - - if (threadIdx.x < lenSrcB) { - d_DstKey[lenSrcA + threadIdx.x] = s_key[lenSrcA + threadIdx.x]; - d_DstVal[lenSrcA + threadIdx.x] = s_val[lenSrcA + threadIdx.x]; - } -} - -extern "C" void bitonicMergeElementaryIntervals(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint *d_LimitsA, - uint *d_LimitsB, - uint stride, - uint N, - uint sortDir) -{ - uint lastSegmentElements = N % (2 * stride); - - uint mergePairs = (lastSegmentElements > stride) ? getSampleCount(N) : (N - lastSegmentElements) / SAMPLE_STRIDE; - - if (sortDir) { - bitonicMergeElementaryIntervalsKernel<1U> - <<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, d_LimitsA, d_LimitsB, stride, N); - getLastCudaError("mergeElementaryIntervalsKernel<1> failed\n"); - } - else { - bitonicMergeElementaryIntervalsKernel<0U> - <<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, d_LimitsA, d_LimitsB, stride, N); - getLastCudaError("mergeElementaryIntervalsKernel<0> failed\n"); - } -} diff --git a/cpp/0_Introduction/mergeSort/main.cpp b/cpp/0_Introduction/mergeSort/main.cpp deleted file mode 100644 index 8eb50184..00000000 --- a/cpp/0_Introduction/mergeSort/main.cpp +++ /dev/null @@ -1,119 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include -#include -#include - -#include "mergeSort_common.h" - -//////////////////////////////////////////////////////////////////////////////// -// Test driver -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - uint *h_SrcKey, *h_SrcVal, *h_DstKey, *h_DstVal; - uint *d_SrcKey, *d_SrcVal, *d_BufKey, *d_BufVal, *d_DstKey, *d_DstVal; - StopWatchInterface *hTimer = NULL; - - const uint N = 4 * 1048576; - const uint DIR = 1; - const uint numValues = 65536; - - printf("%s Starting...\n\n", argv[0]); - - int dev = findCudaDevice(argc, (const char **)argv); - - if (dev == -1) { - return EXIT_FAILURE; - } - - printf("Allocating and initializing host arrays...\n\n"); - sdkCreateTimer(&hTimer); - h_SrcKey = (uint *)malloc(N * sizeof(uint)); - h_SrcVal = (uint *)malloc(N * sizeof(uint)); - h_DstKey = (uint *)malloc(N * sizeof(uint)); - h_DstVal = (uint *)malloc(N * sizeof(uint)); - - srand(2009); - - for (uint i = 0; i < N; i++) { - h_SrcKey[i] = rand() % numValues; - } - - fillValues(h_SrcVal, N); - - printf("Allocating and initializing CUDA arrays...\n\n"); - checkCudaErrors(cudaMalloc((void **)&d_DstKey, N * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_DstVal, N * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_BufKey, N * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_BufVal, N * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_SrcKey, N * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_SrcVal, N * sizeof(uint))); - checkCudaErrors(cudaMemcpy(d_SrcKey, h_SrcKey, N * sizeof(uint), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_SrcVal, h_SrcVal, N * sizeof(uint), cudaMemcpyHostToDevice)); - - printf("Initializing GPU merge sort...\n"); - initMergeSort(); - - printf("Running GPU merge sort...\n"); - checkCudaErrors(cudaDeviceSynchronize()); - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - mergeSort(d_DstKey, d_DstVal, d_BufKey, d_BufVal, d_SrcKey, d_SrcVal, N, DIR); - checkCudaErrors(cudaDeviceSynchronize()); - sdkStopTimer(&hTimer); - printf("Time: %f ms\n", sdkGetTimerValue(&hTimer)); - - printf("Reading back GPU merge sort results...\n"); - checkCudaErrors(cudaMemcpy(h_DstKey, d_DstKey, N * sizeof(uint), cudaMemcpyDeviceToHost)); - checkCudaErrors(cudaMemcpy(h_DstVal, d_DstVal, N * sizeof(uint), cudaMemcpyDeviceToHost)); - - printf("Inspecting the results...\n"); - uint keysFlag = validateSortedKeys(h_DstKey, h_SrcKey, 1, N, numValues, DIR); - - uint valuesFlag = validateSortedValues(h_DstKey, h_DstVal, h_SrcKey, 1, N); - - printf("Shutting down...\n"); - closeMergeSort(); - sdkDeleteTimer(&hTimer); - checkCudaErrors(cudaFree(d_SrcVal)); - checkCudaErrors(cudaFree(d_SrcKey)); - checkCudaErrors(cudaFree(d_BufVal)); - checkCudaErrors(cudaFree(d_BufKey)); - checkCudaErrors(cudaFree(d_DstVal)); - checkCudaErrors(cudaFree(d_DstKey)); - free(h_DstVal); - free(h_DstKey); - free(h_SrcVal); - free(h_SrcKey); - - exit((keysFlag && valuesFlag) ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/0_Introduction/mergeSort/mergeSort.cu b/cpp/0_Introduction/mergeSort/mergeSort.cu deleted file mode 100644 index d2048a17..00000000 --- a/cpp/0_Introduction/mergeSort/mergeSort.cu +++ /dev/null @@ -1,537 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * Based on "Designing efficient sorting algorithms for manycore GPUs" - * by Nadathur Satish, Mark Harris, and Michael Garland - * http://mgarland.org/files/papers/gpusort-ipdps09.pdf - * - * Victor Podlozhnyuk 09/24/2009 - */ - -#include -#include - -namespace cg = cooperative_groups; - -#include - -#include "mergeSort_common.h" - -//////////////////////////////////////////////////////////////////////////////// -// Helper functions -//////////////////////////////////////////////////////////////////////////////// -static inline __host__ __device__ uint iDivUp(uint a, uint b) { return ((a % b) == 0) ? (a / b) : (a / b + 1); } - -static inline __host__ __device__ uint getSampleCount(uint dividend) { return iDivUp(dividend, SAMPLE_STRIDE); } - -#define W (sizeof(uint) * 8) -static inline __device__ uint nextPowerOfTwo(uint x) -{ - /* - --x; - x |= x >> 1; - x |= x >> 2; - x |= x >> 4; - x |= x >> 8; - x |= x >> 16; - return ++x; - */ - return 1U << (W - __clz(x - 1)); -} - -template static inline __device__ uint binarySearchInclusive(uint val, uint *data, uint L, uint stride) -{ - if (L == 0) { - return 0; - } - - uint pos = 0; - - for (; stride > 0; stride >>= 1) { - uint newPos = umin(pos + stride, L); - - if ((sortDir && (data[newPos - 1] <= val)) || (!sortDir && (data[newPos - 1] >= val))) { - pos = newPos; - } - } - - return pos; -} - -template static inline __device__ uint binarySearchExclusive(uint val, uint *data, uint L, uint stride) -{ - if (L == 0) { - return 0; - } - - uint pos = 0; - - for (; stride > 0; stride >>= 1) { - uint newPos = umin(pos + stride, L); - - if ((sortDir && (data[newPos - 1] < val)) || (!sortDir && (data[newPos - 1] > val))) { - pos = newPos; - } - } - - return pos; -} - -//////////////////////////////////////////////////////////////////////////////// -// Bottom-level merge sort (binary search-based) -//////////////////////////////////////////////////////////////////////////////// -template -__global__ void mergeSortSharedKernel(uint *d_DstKey, uint *d_DstVal, uint *d_SrcKey, uint *d_SrcVal, uint arrayLength) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - __shared__ uint s_key[SHARED_SIZE_LIMIT]; - __shared__ uint s_val[SHARED_SIZE_LIMIT]; - - d_SrcKey += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_SrcVal += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_DstKey += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - d_DstVal += blockIdx.x * SHARED_SIZE_LIMIT + threadIdx.x; - s_key[threadIdx.x + 0] = d_SrcKey[0]; - s_val[threadIdx.x + 0] = d_SrcVal[0]; - s_key[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = d_SrcKey[(SHARED_SIZE_LIMIT / 2)]; - s_val[threadIdx.x + (SHARED_SIZE_LIMIT / 2)] = d_SrcVal[(SHARED_SIZE_LIMIT / 2)]; - - for (uint stride = 1; stride < arrayLength; stride <<= 1) { - uint lPos = threadIdx.x & (stride - 1); - uint *baseKey = s_key + 2 * (threadIdx.x - lPos); - uint *baseVal = s_val + 2 * (threadIdx.x - lPos); - - cg::sync(cta); - uint keyA = baseKey[lPos + 0]; - uint valA = baseVal[lPos + 0]; - uint keyB = baseKey[lPos + stride]; - uint valB = baseVal[lPos + stride]; - uint posA = binarySearchExclusive(keyA, baseKey + stride, stride, stride) + lPos; - uint posB = binarySearchInclusive(keyB, baseKey + 0, stride, stride) + lPos; - - cg::sync(cta); - baseKey[posA] = keyA; - baseVal[posA] = valA; - baseKey[posB] = keyB; - baseVal[posB] = valB; - } - - cg::sync(cta); - d_DstKey[0] = s_key[threadIdx.x + 0]; - d_DstVal[0] = s_val[threadIdx.x + 0]; - d_DstKey[(SHARED_SIZE_LIMIT / 2)] = s_key[threadIdx.x + (SHARED_SIZE_LIMIT / 2)]; - d_DstVal[(SHARED_SIZE_LIMIT / 2)] = s_val[threadIdx.x + (SHARED_SIZE_LIMIT / 2)]; -} - -static void mergeSortShared(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint batchSize, - uint arrayLength, - uint sortDir) -{ - if (arrayLength < 2) { - return; - } - - assert(SHARED_SIZE_LIMIT % arrayLength == 0); - assert(((batchSize * arrayLength) % SHARED_SIZE_LIMIT) == 0); - uint blockCount = batchSize * arrayLength / SHARED_SIZE_LIMIT; - uint threadCount = SHARED_SIZE_LIMIT / 2; - - if (sortDir) { - mergeSortSharedKernel<1U><<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, arrayLength); - getLastCudaError("mergeSortShared<1><<<>>> failed\n"); - } - else { - mergeSortSharedKernel<0U><<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, arrayLength); - getLastCudaError("mergeSortShared<0><<<>>> failed\n"); - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 1: generate sample ranks -//////////////////////////////////////////////////////////////////////////////// -template -__global__ void -generateSampleRanksKernel(uint *d_RanksA, uint *d_RanksB, uint *d_SrcKey, uint stride, uint N, uint threadCount) -{ - uint pos = blockIdx.x * blockDim.x + threadIdx.x; - - if (pos >= threadCount) { - return; - } - - const uint i = pos & ((stride / SAMPLE_STRIDE) - 1); - const uint segmentBase = (pos - i) * (2 * SAMPLE_STRIDE); - d_SrcKey += segmentBase; - d_RanksA += segmentBase / SAMPLE_STRIDE; - d_RanksB += segmentBase / SAMPLE_STRIDE; - - const uint segmentElementsA = stride; - const uint segmentElementsB = umin(stride, N - segmentBase - stride); - const uint segmentSamplesA = getSampleCount(segmentElementsA); - const uint segmentSamplesB = getSampleCount(segmentElementsB); - - if (i < segmentSamplesA) { - d_RanksA[i] = i * SAMPLE_STRIDE; - d_RanksB[i] = binarySearchExclusive( - d_SrcKey[i * SAMPLE_STRIDE], d_SrcKey + stride, segmentElementsB, nextPowerOfTwo(segmentElementsB)); - } - - if (i < segmentSamplesB) { - d_RanksB[(stride / SAMPLE_STRIDE) + i] = i * SAMPLE_STRIDE; - d_RanksA[(stride / SAMPLE_STRIDE) + i] = binarySearchInclusive( - d_SrcKey[stride + i * SAMPLE_STRIDE], d_SrcKey + 0, segmentElementsA, nextPowerOfTwo(segmentElementsA)); - } -} - -static void generateSampleRanks(uint *d_RanksA, uint *d_RanksB, uint *d_SrcKey, uint stride, uint N, uint sortDir) -{ - uint lastSegmentElements = N % (2 * stride); - uint threadCount = (lastSegmentElements > stride) ? (N + 2 * stride - lastSegmentElements) / (2 * SAMPLE_STRIDE) - : (N - lastSegmentElements) / (2 * SAMPLE_STRIDE); - - if (sortDir) { - generateSampleRanksKernel<1U> - <<>>(d_RanksA, d_RanksB, d_SrcKey, stride, N, threadCount); - getLastCudaError("generateSampleRanksKernel<1U><<<>>> failed\n"); - } - else { - generateSampleRanksKernel<0U> - <<>>(d_RanksA, d_RanksB, d_SrcKey, stride, N, threadCount); - getLastCudaError("generateSampleRanksKernel<0U><<<>>> failed\n"); - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 2: generate sample ranks and indices -//////////////////////////////////////////////////////////////////////////////// -__global__ void mergeRanksAndIndicesKernel(uint *d_Limits, uint *d_Ranks, uint stride, uint N, uint threadCount) -{ - uint pos = blockIdx.x * blockDim.x + threadIdx.x; - - if (pos >= threadCount) { - return; - } - - const uint i = pos & ((stride / SAMPLE_STRIDE) - 1); - const uint segmentBase = (pos - i) * (2 * SAMPLE_STRIDE); - d_Ranks += (pos - i) * 2; - d_Limits += (pos - i) * 2; - - const uint segmentElementsA = stride; - const uint segmentElementsB = umin(stride, N - segmentBase - stride); - const uint segmentSamplesA = getSampleCount(segmentElementsA); - const uint segmentSamplesB = getSampleCount(segmentElementsB); - - if (i < segmentSamplesA) { - uint dstPos = binarySearchExclusive<1U>( - d_Ranks[i], d_Ranks + segmentSamplesA, segmentSamplesB, nextPowerOfTwo(segmentSamplesB)) - + i; - d_Limits[dstPos] = d_Ranks[i]; - } - - if (i < segmentSamplesB) { - uint dstPos = binarySearchInclusive<1U>( - d_Ranks[segmentSamplesA + i], d_Ranks, segmentSamplesA, nextPowerOfTwo(segmentSamplesA)) - + i; - d_Limits[dstPos] = d_Ranks[segmentSamplesA + i]; - } -} - -static void mergeRanksAndIndices(uint *d_LimitsA, uint *d_LimitsB, uint *d_RanksA, uint *d_RanksB, uint stride, uint N) -{ - uint lastSegmentElements = N % (2 * stride); - uint threadCount = (lastSegmentElements > stride) ? (N + 2 * stride - lastSegmentElements) / (2 * SAMPLE_STRIDE) - : (N - lastSegmentElements) / (2 * SAMPLE_STRIDE); - - mergeRanksAndIndicesKernel<<>>(d_LimitsA, d_RanksA, stride, N, threadCount); - getLastCudaError("mergeRanksAndIndicesKernel(A)<<<>>> failed\n"); - - mergeRanksAndIndicesKernel<<>>(d_LimitsB, d_RanksB, stride, N, threadCount); - getLastCudaError("mergeRanksAndIndicesKernel(B)<<<>>> failed\n"); -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 3: merge elementary intervals -//////////////////////////////////////////////////////////////////////////////// -template -inline __device__ void merge(uint *dstKey, - uint *dstVal, - uint *srcAKey, - uint *srcAVal, - uint *srcBKey, - uint *srcBVal, - uint lenA, - uint nPowTwoLenA, - uint lenB, - uint nPowTwoLenB, - cg::thread_block cta) -{ - uint keyA, valA, keyB, valB, dstPosA, dstPosB; - - if (threadIdx.x < lenA) { - keyA = srcAKey[threadIdx.x]; - valA = srcAVal[threadIdx.x]; - dstPosA = binarySearchExclusive(keyA, srcBKey, lenB, nPowTwoLenB) + threadIdx.x; - } - - if (threadIdx.x < lenB) { - keyB = srcBKey[threadIdx.x]; - valB = srcBVal[threadIdx.x]; - dstPosB = binarySearchInclusive(keyB, srcAKey, lenA, nPowTwoLenA) + threadIdx.x; - } - - cg::sync(cta); - - if (threadIdx.x < lenA) { - dstKey[dstPosA] = keyA; - dstVal[dstPosA] = valA; - } - - if (threadIdx.x < lenB) { - dstKey[dstPosB] = keyB; - dstVal[dstPosB] = valB; - } -} - -template -__global__ void mergeElementaryIntervalsKernel(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint *d_LimitsA, - uint *d_LimitsB, - uint stride, - uint N) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - __shared__ uint s_key[2 * SAMPLE_STRIDE]; - __shared__ uint s_val[2 * SAMPLE_STRIDE]; - - const uint intervalI = blockIdx.x & ((2 * stride) / SAMPLE_STRIDE - 1); - const uint segmentBase = (blockIdx.x - intervalI) * SAMPLE_STRIDE; - d_SrcKey += segmentBase; - d_SrcVal += segmentBase; - d_DstKey += segmentBase; - d_DstVal += segmentBase; - - // Set up threadblock-wide parameters - __shared__ uint startSrcA, startSrcB, lenSrcA, lenSrcB, startDstA, startDstB; - - if (threadIdx.x == 0) { - uint segmentElementsA = stride; - uint segmentElementsB = umin(stride, N - segmentBase - stride); - uint segmentSamplesA = getSampleCount(segmentElementsA); - uint segmentSamplesB = getSampleCount(segmentElementsB); - uint segmentSamples = segmentSamplesA + segmentSamplesB; - - startSrcA = d_LimitsA[blockIdx.x]; - startSrcB = d_LimitsB[blockIdx.x]; - uint endSrcA = (intervalI + 1 < segmentSamples) ? d_LimitsA[blockIdx.x + 1] : segmentElementsA; - uint endSrcB = (intervalI + 1 < segmentSamples) ? d_LimitsB[blockIdx.x + 1] : segmentElementsB; - lenSrcA = endSrcA - startSrcA; - lenSrcB = endSrcB - startSrcB; - startDstA = startSrcA + startSrcB; - startDstB = startDstA + lenSrcA; - } - - // Load main input data - cg::sync(cta); - - if (threadIdx.x < lenSrcA) { - s_key[threadIdx.x + 0] = d_SrcKey[0 + startSrcA + threadIdx.x]; - s_val[threadIdx.x + 0] = d_SrcVal[0 + startSrcA + threadIdx.x]; - } - - if (threadIdx.x < lenSrcB) { - s_key[threadIdx.x + SAMPLE_STRIDE] = d_SrcKey[stride + startSrcB + threadIdx.x]; - s_val[threadIdx.x + SAMPLE_STRIDE] = d_SrcVal[stride + startSrcB + threadIdx.x]; - } - - // Merge data in shared memory - cg::sync(cta); - merge(s_key, - s_val, - s_key + 0, - s_val + 0, - s_key + SAMPLE_STRIDE, - s_val + SAMPLE_STRIDE, - lenSrcA, - SAMPLE_STRIDE, - lenSrcB, - SAMPLE_STRIDE, - cta); - - // Store merged data - cg::sync(cta); - - if (threadIdx.x < lenSrcA) { - d_DstKey[startDstA + threadIdx.x] = s_key[threadIdx.x]; - d_DstVal[startDstA + threadIdx.x] = s_val[threadIdx.x]; - } - - if (threadIdx.x < lenSrcB) { - d_DstKey[startDstB + threadIdx.x] = s_key[lenSrcA + threadIdx.x]; - d_DstVal[startDstB + threadIdx.x] = s_val[lenSrcA + threadIdx.x]; - } -} - -static void mergeElementaryIntervals(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint *d_LimitsA, - uint *d_LimitsB, - uint stride, - uint N, - uint sortDir) -{ - uint lastSegmentElements = N % (2 * stride); - uint mergePairs = (lastSegmentElements > stride) ? getSampleCount(N) : (N - lastSegmentElements) / SAMPLE_STRIDE; - - if (sortDir) { - mergeElementaryIntervalsKernel<1U> - <<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, d_LimitsA, d_LimitsB, stride, N); - getLastCudaError("mergeElementaryIntervalsKernel<1> failed\n"); - } - else { - mergeElementaryIntervalsKernel<0U> - <<>>(d_DstKey, d_DstVal, d_SrcKey, d_SrcVal, d_LimitsA, d_LimitsB, stride, N); - getLastCudaError("mergeElementaryIntervalsKernel<0> failed\n"); - } -} - -extern "C" void bitonicSortShared(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint batchSize, - uint arrayLength, - uint sortDir); - -extern "C" void bitonicMergeElementaryIntervals(uint *d_DstKey, - uint *d_DstVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint *d_LimitsA, - uint *d_LimitsB, - uint stride, - uint N, - uint sortDir); - -static uint *d_RanksA, *d_RanksB, *d_LimitsA, *d_LimitsB; -static const uint MAX_SAMPLE_COUNT = 32768; - -extern "C" void initMergeSort(void) -{ - checkCudaErrors(cudaMalloc((void **)&d_RanksA, MAX_SAMPLE_COUNT * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_RanksB, MAX_SAMPLE_COUNT * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_LimitsA, MAX_SAMPLE_COUNT * sizeof(uint))); - checkCudaErrors(cudaMalloc((void **)&d_LimitsB, MAX_SAMPLE_COUNT * sizeof(uint))); -} - -extern "C" void closeMergeSort(void) -{ - checkCudaErrors(cudaFree(d_RanksA)); - checkCudaErrors(cudaFree(d_RanksB)); - checkCudaErrors(cudaFree(d_LimitsB)); - checkCudaErrors(cudaFree(d_LimitsA)); -} - -extern "C" void mergeSort(uint *d_DstKey, - uint *d_DstVal, - uint *d_BufKey, - uint *d_BufVal, - uint *d_SrcKey, - uint *d_SrcVal, - uint N, - uint sortDir) -{ - uint stageCount = 0; - - for (uint stride = SHARED_SIZE_LIMIT; stride < N; stride <<= 1, stageCount++) - ; - - uint *ikey, *ival, *okey, *oval; - - if (stageCount & 1) { - ikey = d_BufKey; - ival = d_BufVal; - okey = d_DstKey; - oval = d_DstVal; - } - else { - ikey = d_DstKey; - ival = d_DstVal; - okey = d_BufKey; - oval = d_BufVal; - } - - assert(N <= (SAMPLE_STRIDE * MAX_SAMPLE_COUNT)); - assert(N % SHARED_SIZE_LIMIT == 0); - mergeSortShared(ikey, ival, d_SrcKey, d_SrcVal, N / SHARED_SIZE_LIMIT, SHARED_SIZE_LIMIT, sortDir); - - for (uint stride = SHARED_SIZE_LIMIT; stride < N; stride <<= 1) { - uint lastSegmentElements = N % (2 * stride); - - // Find sample ranks and prepare for limiters merge - generateSampleRanks(d_RanksA, d_RanksB, ikey, stride, N, sortDir); - - // Merge ranks and indices - mergeRanksAndIndices(d_LimitsA, d_LimitsB, d_RanksA, d_RanksB, stride, N); - - // Merge elementary intervals - mergeElementaryIntervals(okey, oval, ikey, ival, d_LimitsA, d_LimitsB, stride, N, sortDir); - - if (lastSegmentElements <= stride) { - // Last merge segment consists of a single array which just needs to be - // passed through - checkCudaErrors(cudaMemcpy(okey + (N - lastSegmentElements), - ikey + (N - lastSegmentElements), - lastSegmentElements * sizeof(uint), - cudaMemcpyDeviceToDevice)); - checkCudaErrors(cudaMemcpy(oval + (N - lastSegmentElements), - ival + (N - lastSegmentElements), - lastSegmentElements * sizeof(uint), - cudaMemcpyDeviceToDevice)); - } - - uint *t; - t = ikey; - ikey = okey; - okey = t; - t = ival; - ival = oval; - oval = t; - } -} diff --git a/cpp/0_Introduction/mergeSort/mergeSort_common.h b/cpp/0_Introduction/mergeSort/mergeSort_common.h deleted file mode 100644 index 10835ff4..00000000 --- a/cpp/0_Introduction/mergeSort/mergeSort_common.h +++ /dev/null @@ -1,60 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -//////////////////////////////////////////////////////////////////////////////// -// Shortcut definitions -//////////////////////////////////////////////////////////////////////////////// -typedef unsigned int uint; - -#define SHARED_SIZE_LIMIT 1024U -#define SAMPLE_STRIDE 128 - -//////////////////////////////////////////////////////////////////////////////// -// Extensive sort validation routine -//////////////////////////////////////////////////////////////////////////////// -extern "C" uint -validateSortedKeys(uint *resKey, uint *srcKey, uint batchSize, uint arrayLength, uint numValues, uint sortDir); - -extern "C" void fillValues(uint *val, uint N); - -extern "C" int validateSortedValues(uint *resKey, uint *resVal, uint *srcKey, uint batchSize, uint arrayLength); - -//////////////////////////////////////////////////////////////////////////////// -// CUDA merge sort -//////////////////////////////////////////////////////////////////////////////// -extern "C" void initMergeSort(void); - -extern "C" void closeMergeSort(void); - -extern "C" void -mergeSort(uint *dstKey, uint *dstVal, uint *bufKey, uint *bufVal, uint *srcKey, uint *srcVal, uint N, uint sortDir); - -//////////////////////////////////////////////////////////////////////////////// -// CPU "emulation" -//////////////////////////////////////////////////////////////////////////////// -extern "C" void -mergeSortHost(uint *dstKey, uint *dstVal, uint *bufKey, uint *bufVal, uint *srcKey, uint *srcVal, uint N, uint sortDir); diff --git a/cpp/0_Introduction/mergeSort/mergeSort_host.cpp b/cpp/0_Introduction/mergeSort/mergeSort_host.cpp deleted file mode 100644 index 4a322f14..00000000 --- a/cpp/0_Introduction/mergeSort/mergeSort_host.cpp +++ /dev/null @@ -1,363 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -#include "mergeSort_common.h" - -//////////////////////////////////////////////////////////////////////////////// -// Helper functions -//////////////////////////////////////////////////////////////////////////////// -static void checkOrder(uint *data, uint N, uint sortDir) -{ - if (N <= 1) { - return; - } - - for (uint i = 0; i < N - 1; i++) - if ((sortDir && (data[i] > data[i + 1])) || (!sortDir && (data[i] < data[i + 1]))) { - fprintf(stderr, "checkOrder() failed!!!\n"); - exit(EXIT_FAILURE); - } -} - -static uint umin(uint a, uint b) { return (a <= b) ? a : b; } - -static uint getSampleCount(uint dividend) -{ - return ((dividend % SAMPLE_STRIDE) != 0) ? (dividend / SAMPLE_STRIDE + 1) : (dividend / SAMPLE_STRIDE); -} - -static uint nextPowerOfTwo(uint x) -{ - --x; - x |= x >> 1; - x |= x >> 2; - x |= x >> 4; - x |= x >> 8; - x |= x >> 16; - return ++x; -} - -static uint binarySearchInclusive(uint val, uint *data, uint L, uint sortDir) -{ - if (L == 0) { - return 0; - } - - uint pos = 0; - - for (uint stride = nextPowerOfTwo(L); stride > 0; stride >>= 1) { - uint newPos = umin(pos + stride, L); - - if ((sortDir && (data[newPos - 1] <= val)) || (!sortDir && (data[newPos - 1] >= val))) { - pos = newPos; - } - } - - return pos; -} - -static uint binarySearchExclusive(uint val, uint *data, uint L, uint sortDir) -{ - if (L == 0) { - return 0; - } - - uint pos = 0; - - for (uint stride = nextPowerOfTwo(L); stride > 0; stride >>= 1) { - uint newPos = umin(pos + stride, L); - - if ((sortDir && (data[newPos - 1] < val)) || (!sortDir && (data[newPos - 1] > val))) { - pos = newPos; - } - } - - return pos; -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 1: find sample ranks in each segment -//////////////////////////////////////////////////////////////////////////////// -static void generateSampleRanks(uint *ranksA, uint *ranksB, uint *srcKey, uint stride, uint N, uint sortDir) -{ - uint lastSegmentElements = N % (2 * stride); - uint sampleCount = (lastSegmentElements > stride) ? (N + 2 * stride - lastSegmentElements) / (2 * SAMPLE_STRIDE) - : (N - lastSegmentElements) / (2 * SAMPLE_STRIDE); - - for (uint pos = 0; pos < sampleCount; pos++) { - const uint i = pos & ((stride / SAMPLE_STRIDE) - 1); - const uint segmentBase = (pos - i) * (2 * SAMPLE_STRIDE); - - const uint lenA = stride; - const uint lenB = umin(stride, N - segmentBase - stride); - const uint nA = stride / SAMPLE_STRIDE; - const uint nB = getSampleCount(lenB); - - if (i < nA) { - ranksA[(segmentBase + 0) / SAMPLE_STRIDE + i] = i * SAMPLE_STRIDE; - ranksB[(segmentBase + 0) / SAMPLE_STRIDE + i] = binarySearchExclusive( - srcKey[segmentBase + i * SAMPLE_STRIDE], srcKey + segmentBase + stride, lenB, sortDir); - } - - if (i < nB) { - ranksB[(segmentBase + stride) / SAMPLE_STRIDE + i] = i * SAMPLE_STRIDE; - ranksA[(segmentBase + stride) / SAMPLE_STRIDE + i] = binarySearchInclusive( - srcKey[segmentBase + stride + i * SAMPLE_STRIDE], srcKey + segmentBase, lenA, sortDir); - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 2: merge ranks and indices to derive elementary intervals -//////////////////////////////////////////////////////////////////////////////// -static void mergeRanksAndIndices(uint *limits, uint *ranks, uint stride, uint N) -{ - uint lastSegmentElements = N % (2 * stride); - uint sampleCount = (lastSegmentElements > stride) ? (N + 2 * stride - lastSegmentElements) / (2 * SAMPLE_STRIDE) - : (N - lastSegmentElements) / (2 * SAMPLE_STRIDE); - - for (uint pos = 0; pos < sampleCount; pos++) { - const uint i = pos & ((stride / SAMPLE_STRIDE) - 1); - const uint segmentBase = (pos - i) * (2 * SAMPLE_STRIDE); - - const uint lenA = stride; - const uint lenB = umin(stride, N - segmentBase - stride); - const uint nA = stride / SAMPLE_STRIDE; - const uint nB = getSampleCount(lenB); - - if (i < nA) { - uint dstPosA = - binarySearchExclusive( - ranks[(segmentBase + 0) / SAMPLE_STRIDE + i], ranks + (segmentBase + stride) / SAMPLE_STRIDE, nB, 1) - + i; - assert(dstPosA < nA + nB); - limits[(segmentBase / SAMPLE_STRIDE) + dstPosA] = ranks[(segmentBase + 0) / SAMPLE_STRIDE + i]; - } - - if (i < nB) { - uint dstPosA = - binarySearchInclusive( - ranks[(segmentBase + stride) / SAMPLE_STRIDE + i], ranks + (segmentBase + 0) / SAMPLE_STRIDE, nA, 1) - + i; - assert(dstPosA < nA + nB); - limits[(segmentBase / SAMPLE_STRIDE) + dstPosA] = ranks[(segmentBase + stride) / SAMPLE_STRIDE + i]; - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Merge step 3: merge elementary intervals (each interval is <= SAMPLE_STRIDE) -//////////////////////////////////////////////////////////////////////////////// -static void merge(uint *dstKey, - uint *dstVal, - uint *srcAKey, - uint *srcAVal, - uint *srcBKey, - uint *srcBVal, - uint lenA, - uint lenB, - uint sortDir) -{ - checkOrder(srcAKey, lenA, sortDir); - checkOrder(srcBKey, lenB, sortDir); - - for (uint i = 0; i < lenA; i++) { - uint dstPos = binarySearchExclusive(srcAKey[i], srcBKey, lenB, sortDir) + i; - assert(dstPos < lenA + lenB); - dstKey[dstPos] = srcAKey[i]; - dstVal[dstPos] = srcAVal[i]; - } - - for (uint i = 0; i < lenB; i++) { - uint dstPos = binarySearchInclusive(srcBKey[i], srcAKey, lenA, sortDir) + i; - assert(dstPos < lenA + lenB); - dstKey[dstPos] = srcBKey[i]; - dstVal[dstPos] = srcBVal[i]; - } -} - -static void mergeElementaryIntervals(uint *dstKey, - uint *dstVal, - uint *srcKey, - uint *srcVal, - uint *limitsA, - uint *limitsB, - uint stride, - uint N, - uint sortDir) -{ - uint lastSegmentElements = N % (2 * stride); - uint mergePairs = (lastSegmentElements > stride) ? getSampleCount(N) : (N - lastSegmentElements) / SAMPLE_STRIDE; - - for (uint pos = 0; pos < mergePairs; pos++) { - uint i = pos & ((2 * stride) / SAMPLE_STRIDE - 1); - uint segmentBase = (pos - i) * SAMPLE_STRIDE; - - const uint lenA = stride; - const uint lenB = umin(stride, N - segmentBase - stride); - const uint nA = stride / SAMPLE_STRIDE; - const uint nB = getSampleCount(lenB); - const uint n = nA + nB; - - const uint startPosA = limitsA[pos]; - const uint endPosA = (i + 1 < n) ? limitsA[pos + 1] : lenA; - const uint startPosB = limitsB[pos]; - const uint endPosB = (i + 1 < n) ? limitsB[pos + 1] : lenB; - const uint startPosDst = startPosA + startPosB; - - assert(startPosA <= endPosA && endPosA <= lenA); - assert(startPosB <= endPosB && endPosB <= lenB); - assert((endPosA - startPosA) <= SAMPLE_STRIDE); - assert((endPosB - startPosB) <= SAMPLE_STRIDE); - - merge(dstKey + segmentBase + startPosDst, - dstVal + segmentBase + startPosDst, - (srcKey + segmentBase + 0) + startPosA, - (srcVal + segmentBase + 0) + startPosA, - (srcKey + segmentBase + stride) + startPosB, - (srcVal + segmentBase + stride) + startPosB, - endPosA - startPosA, - endPosB - startPosB, - sortDir); - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Retarded bubble sort -//////////////////////////////////////////////////////////////////////////////// -static void bubbleSort(uint *key, uint *val, uint N, uint sortDir) -{ - if (N <= 1) { - return; - } - - for (uint bottom = 0; bottom < N - 1; bottom++) { - uint savePos = bottom; - uint saveKey = key[bottom]; - - for (uint i = bottom + 1; i < N; i++) - if ((sortDir && (key[i] < saveKey)) || (!sortDir && (key[i] > saveKey))) { - savePos = i; - saveKey = key[i]; - } - - if (savePos != bottom) { - uint t; - t = key[savePos]; - key[savePos] = key[bottom]; - key[bottom] = t; - t = val[savePos]; - val[savePos] = val[bottom]; - val[bottom] = t; - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Interface function -//////////////////////////////////////////////////////////////////////////////// -extern "C" void -mergeSortHost(uint *dstKey, uint *dstVal, uint *bufKey, uint *bufVal, uint *srcKey, uint *srcVal, uint N, uint sortDir) -{ - uint *ikey, *ival, *okey, *oval; - uint stageCount = 0; - - for (uint stride = SHARED_SIZE_LIMIT; stride < N; stride <<= 1, stageCount++) - ; - - if (stageCount & 1) { - ikey = bufKey; - ival = bufVal; - okey = dstKey; - oval = dstVal; - } - else { - ikey = dstKey; - ival = dstVal; - okey = bufKey; - oval = bufVal; - } - - printf("Bottom-level sort...\n"); - memcpy(ikey, srcKey, N * sizeof(uint)); - memcpy(ival, srcVal, N * sizeof(uint)); - - for (uint pos = 0; pos < N; pos += SHARED_SIZE_LIMIT) { - bubbleSort(ikey + pos, ival + pos, umin(SHARED_SIZE_LIMIT, N - pos), sortDir); - } - - printf("Merge...\n"); - uint *ranksA = (uint *)malloc(getSampleCount(N) * sizeof(uint)); - uint *ranksB = (uint *)malloc(getSampleCount(N) * sizeof(uint)); - uint *limitsA = (uint *)malloc(getSampleCount(N) * sizeof(uint)); - uint *limitsB = (uint *)malloc(getSampleCount(N) * sizeof(uint)); - memset(ranksA, 0xFF, getSampleCount(N) * sizeof(uint)); - memset(ranksB, 0xFF, getSampleCount(N) * sizeof(uint)); - memset(limitsA, 0xFF, getSampleCount(N) * sizeof(uint)); - memset(limitsB, 0xFF, getSampleCount(N) * sizeof(uint)); - - for (uint stride = SHARED_SIZE_LIMIT; stride < N; stride <<= 1) { - uint lastSegmentElements = N % (2 * stride); - - // Find sample ranks and prepare for limiters merge - generateSampleRanks(ranksA, ranksB, ikey, stride, N, sortDir); - - // Merge ranks and indices - mergeRanksAndIndices(limitsA, ranksA, stride, N); - mergeRanksAndIndices(limitsB, ranksB, stride, N); - - // Merge elementary intervals - mergeElementaryIntervals(okey, oval, ikey, ival, limitsA, limitsB, stride, N, sortDir); - - if (lastSegmentElements <= stride) { - // Last merge segment consists of a single array which just needs to be - // passed through - memcpy( - okey + (N - lastSegmentElements), ikey + (N - lastSegmentElements), lastSegmentElements * sizeof(uint)); - memcpy( - oval + (N - lastSegmentElements), ival + (N - lastSegmentElements), lastSegmentElements * sizeof(uint)); - } - - uint *t; - t = ikey; - ikey = okey; - okey = t; - t = ival; - ival = oval; - oval = t; - } - - free(limitsB); - free(limitsA); - free(ranksB); - free(ranksA); -} diff --git a/cpp/0_Introduction/mergeSort/mergeSort_validate.cpp b/cpp/0_Introduction/mergeSort/mergeSort_validate.cpp deleted file mode 100644 index 4eac52a7..00000000 --- a/cpp/0_Introduction/mergeSort/mergeSort_validate.cpp +++ /dev/null @@ -1,128 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -#include "mergeSort_common.h" - -//////////////////////////////////////////////////////////////////////////////// -// Validate sorted keys array (check for integrity and proper order) -//////////////////////////////////////////////////////////////////////////////// -extern "C" uint -validateSortedKeys(uint *resKey, uint *srcKey, uint batchSize, uint arrayLength, uint numValues, uint sortDir) -{ - uint *srcHist; - uint *resHist; - - if (arrayLength < 2) { - printf("validateSortedKeys(): arrays too short, exiting...\n"); - return 1; - } - - printf("...inspecting keys array: "); - srcHist = (uint *)malloc(numValues * sizeof(uint)); - resHist = (uint *)malloc(numValues * sizeof(uint)); - - int flag = 1; - - for (uint j = 0; j < batchSize; j++, srcKey += arrayLength, resKey += arrayLength) { - // Build histograms for keys arrays - memset(srcHist, 0, numValues * sizeof(uint)); - memset(resHist, 0, numValues * sizeof(uint)); - - for (uint i = 0; i < arrayLength; i++) { - if ((srcKey[i] < numValues) && (resKey[i] < numValues)) { - srcHist[srcKey[i]]++; - resHist[resKey[i]]++; - } - else { - fprintf(stderr, "***Set %u source/result key arrays are not limited properly***\n", j); - flag = 0; - goto brk; - } - } - - // Compare the histograms - for (uint i = 0; i < numValues; i++) - if (srcHist[i] != resHist[i]) { - fprintf(stderr, "***Set %u source/result keys histograms do not match***\n", j); - flag = 0; - goto brk; - } - - // Finally check the ordering - for (uint i = 0; i < arrayLength - 1; i++) - if ((sortDir && (resKey[i] > resKey[i + 1])) || (!sortDir && (resKey[i] < resKey[i + 1]))) { - fprintf(stderr, "***Set %u result key array is not ordered properly***\n", j); - flag = 0; - goto brk; - } - } - -brk: - free(resHist); - free(srcHist); - - if (flag) - printf("OK\n"); - - return flag; -} - -//////////////////////////////////////////////////////////////////////////////// -// Value validation / stability check routines -//////////////////////////////////////////////////////////////////////////////// -extern "C" void fillValues(uint *val, uint N) -{ - for (uint i = 0; i < N; i++) - val[i] = i; -} - -extern "C" int validateSortedValues(uint *resKey, uint *resVal, uint *srcKey, uint batchSize, uint arrayLength) -{ - int correctFlag = 1, stableFlag = 1; - - printf("...inspecting keys and values array: "); - - for (uint i = 0; i < batchSize; i++, resKey += arrayLength, resVal += arrayLength) { - for (uint j = 0; j < arrayLength; j++) { - if (resKey[j] != srcKey[resVal[j]]) - correctFlag = 0; - - if ((j < arrayLength - 1) && (resKey[j] == resKey[j + 1]) && (resVal[j] > resVal[j + 1])) - stableFlag = 0; - } - } - - printf(correctFlag ? "OK\n" : "***corrupted!!!***\n"); - printf(stableFlag ? "...stability property: stable!\n" : "...stability property: NOT stable\n"); - - return correctFlag; -} diff --git a/cpp/0_Introduction/simpleAWBarrier/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAWBarrier/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAWBarrier/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAWBarrier/.vscode/extensions.json b/cpp/0_Introduction/simpleAWBarrier/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAWBarrier/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAWBarrier/CMakeLists.txt b/cpp/0_Introduction/simpleAWBarrier/CMakeLists.txt index 7bcb8360..e0c31e45 100644 --- a/cpp/0_Introduction/simpleAWBarrier/CMakeLists.txt +++ b/cpp/0_Introduction/simpleAWBarrier/CMakeLists.txt @@ -1,23 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleAWBarrier LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -26,16 +21,11 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for simpleAWBarrier add_executable(simpleAWBarrier simpleAWBarrier.cu) target_compile_options(simpleAWBarrier PRIVATE $<$:--extended-lambda>) target_compile_features(simpleAWBarrier PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleAWBarrier PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleAssert/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAssert/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAssert/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAssert/.vscode/extensions.json b/cpp/0_Introduction/simpleAssert/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAssert/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAssert/CMakeLists.txt b/cpp/0_Introduction/simpleAssert/CMakeLists.txt index ab0406c8..963c6309 100644 --- a/cpp/0_Introduction/simpleAssert/CMakeLists.txt +++ b/cpp/0_Introduction/simpleAssert/CMakeLists.txt @@ -1,37 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleAssert LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Removes -DNDEBUG For Print specific logs in this sample. string(REPLACE "-DNDEBUG" "" CMAKE_CUDA_FLAGS_RELEASE "${CMAKE_CUDA_FLAGS_RELEASE}") -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleAssert add_executable(simpleAssert simpleAssert.cu) target_compile_options(simpleAssert PRIVATE $<$:--extended-lambda>) target_compile_features(simpleAssert PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleAssert PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleAssert/README.md b/cpp/0_Introduction/simpleAssert/README.md index c3d0d7ec..302a7d45 100644 --- a/cpp/0_Introduction/simpleAssert/README.md +++ b/cpp/0_Introduction/simpleAssert/README.md @@ -1,32 +1,83 @@ -# simpleAssert - simpleAssert +# Sample: Device-Side assert (simpleAssert) ## Description -This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 . +Use the standard C `assert` macro **inside a CUDA kernel**. The sample launches threads that each assert `gtid < N`; threads whose global index reaches `N` trip the assertion, print a diagnostic message to the host, and cause the kernel to fail. The host detects this through the `cudaErrorAssert` status returned by `cudaDeviceSynchronize` and reports it as the expected outcome. + +This sample demonstrates device-side `assert`, a debugging aid for catching invalid conditions in kernel code, and shows how an assertion failure surfaces on the host as an asynchronous CUDA error. + +## What You'll Learn + +- Calling `assert` directly from device code (`__global__` kernel) +- How a failed device assertion is reported to the host as `cudaErrorAssert` +- Detecting that error after the launch via `cudaDeviceSynchronize` +- Computing a global thread index from `blockIdx`, `blockDim`, and `threadIdx` +- Turning an error code into a human-readable message with `cudaGetErrorString` ## Key Concepts -Assert +- **Device-side `assert`** — `assert(condition)` in a kernel; a false condition halts the kernel, prints `file:line: function: block: ... Assertion ... failed`, and flags the launch as failed +- **Asynchronous error reporting** — the assertion failure is not seen at launch time; it is surfaced at the next synchronization point as `cudaErrorAssert` +- **Global thread indexing** — `blockIdx.x * blockDim.x + threadIdx.x` -## Supported SM Architectures +## Key APIs -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +### CUDA Runtime +- `cudaSetDevice` — select the active GPU +- `cudaDeviceGetAttribute` — query compute capability (major, minor) and SM count +- `cudaDeviceSynchronize` — block the host until the kernel finishes; flushes assert output and returns `cudaErrorAssert` if an assertion failed +- `cudaGetErrorString` — convert a `cudaError_t` into a readable description -## Supported OSes +## Requirements -Linux, Windows +### Hardware +- NVIDIA GPU with Compute Capability 7.5 or higher -## Supported CPU Architecture +### Software +- CMake 3.20 or newer +- A C++17-capable host compiler -x86_64, armv7l +## How to Build -## CUDA APIs involved +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaDeviceSynchronize, cudaGetErrorString +## How to Run -## Prerequisites +```bash +./simpleAssert +``` -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +## Expected Output -## References (for more details) +The kernel launches 2 blocks × 32 threads = 64 threads and asserts `gtid < 60`, so the 4 threads with global indices 60–63 fail the assertion. The assertion failures are **expected** — the sample reports `OK` because it successfully detected `cudaErrorAssert`: + +```text +simpleAssert starting... + +GPU Device 0: with compute capability X.Y and Number of SMs + +Launch kernel to generate assertion failures + +-- Begin assert output + +simpleAssert.cu:50: void simpleAssertKernel(int): block: [1,0,0], thread: [28,0,0] Assertion `gtid < N` failed. +... + +-- End assert output + +Device assert failed as expected, CUDA error message is: device-side assert triggered + +simpleAssert completed, returned OK +``` + +The order and exact set of failing-thread lines may vary between runs. + +## Files + +- `simpleAssert.cu` — device-side `assert` kernel + host driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA C++ Programming Guide - Assertion](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-extensions.html#assertion) diff --git a/cpp/0_Introduction/simpleAssert/simpleAssert.cu b/cpp/0_Introduction/simpleAssert/simpleAssert.cu index 64df5d20..749fd61c 100644 --- a/cpp/0_Introduction/simpleAssert/simpleAssert.cu +++ b/cpp/0_Introduction/simpleAssert/simpleAssert.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -25,86 +25,47 @@ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ -#ifdef _WIN32 -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#else -#include -#endif - -// Includes, system +// System includes #include #include -// Includes CUDA +// CUDA runtime #include -// Utilities and timing functions -#include // includes cuda.h and cuda_runtime_api.h - -// CUDA helper functions -#include // helper functions for CUDA error check - const char *sampleName = "simpleAssert"; -//////////////////////////////////////////////////////////////////////////////// // Auto-Verification Code bool testResult = true; -//////////////////////////////////////////////////////////////////////////////// -// Kernels -//////////////////////////////////////////////////////////////////////////////// -//! Tests assert function. -//! Thread whose id > N will print assertion failed error message. -//////////////////////////////////////////////////////////////////////////////// -__global__ void testKernel(int N) +// Each thread computes its global index and asserts it is below N. Threads with +// gtid >= N trip the assertion, which aborts the kernel and surfaces on the host +// as cudaErrorAssert at the next synchronization point. +__global__ void simpleAssertKernel(int N) { int gtid = blockIdx.x * blockDim.x + threadIdx.x; assert(gtid < N); } -//////////////////////////////////////////////////////////////////////////////// -// Declaration, forward -void runTest(int argc, char **argv); - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// int main(int argc, char **argv) -{ - printf("%s starting...\n", sampleName); - - runTest(argc, argv); - - printf("%s completed, returned %s\n", sampleName, testResult ? "OK" : "ERROR!"); - exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -void runTest(int argc, char **argv) { int Nblocks = 2; int Nthreads = 32; cudaError_t error; -#ifndef _WIN32 - utsname OS_System_Type; - uname(&OS_System_Type); + printf("%s starting...\n\n", sampleName); - printf("OS_System_Type.release = %s\n", OS_System_Type.release); + // Select device 0 as the active GPU + int devID = 0; + cudaSetDevice(devID); - if (!strcasecmp(OS_System_Type.sysname, "Darwin")) { - printf("simpleAssert is not current supported on Mac OSX\n\n"); - exit(EXIT_SUCCESS); - } - else { - printf("OS Info: <%s>\n\n", OS_System_Type.version); - } + // Query compute capability (major.minor) and number of SMs on the device + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); -#endif - - // This will pick the best possible CUDA capable device - findCudaDevice(argc, (const char **)argv); + // Print device info + printf("GPU Device %d: with compute capability %d.%d and Number of SMs %d\n\n", devID, major, minor, smCount); // Kernel configuration, where a one-dimensional // grid and one-dimensional blocks are configured. @@ -112,7 +73,7 @@ void runTest(int argc, char **argv) dim3 dimBlock(Nthreads); printf("Launch kernel to generate assertion failures\n"); - testKernel<<>>(60); + simpleAssertKernel<<>>(60); // Synchronize (flushes assert output). printf("\n-- Begin assert output\n\n"); @@ -127,4 +88,7 @@ void runTest(int argc, char **argv) } testResult = error == cudaErrorAssert; + + printf("%s completed, returned %s\n", sampleName, testResult ? "OK" : "ERROR!"); + exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE); } diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/extensions.json b/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/CMakeLists.txt b/cpp/0_Introduction/simpleAssert_nvrtc/CMakeLists.txt deleted file mode 100644 index d473058e..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleAssert_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add sample target executable -add_executable(simpleAssert_nvrtc simpleAssert.cpp) - -target_compile_options(simpleAssert_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleAssert_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -target_link_libraries(simpleAssert_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy clock_kernel.cu to the output directory -add_custom_command(TARGET simpleAssert_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/simpleAssert_kernel.cu ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/README.md b/cpp/0_Introduction/simpleAssert_nvrtc/README.md deleted file mode 100644 index 1d76e311..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleAssert_nvrtc - simpleAssert with libNVRTC - -## Description - -This CUDA Runtime API sample is a very basic sample that implements how to use the assert function in the device code. Requires Compute Capability 2.0 . - -## Key Concepts - -Assert, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuModuleGetFunction, cuLaunchKernel, cuCtxSynchronize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert.cpp b/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert.cpp deleted file mode 100644 index 409dc7c5..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert.cpp +++ /dev/null @@ -1,121 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifdef _WIN32 -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#else -#include -#endif - -// Includes, system -#include -#include - -// Includes CUDA -#include - -#include "nvrtc_helper.h" - -// Utilities and timing functions -#include // includes cuda.h and cuda_runtime_api.h - -const char *sampleName = "simpleAssert_nvrtc"; - -//////////////////////////////////////////////////////////////////////////////// -// Auto-Verification Code -bool testResult = true; - -//////////////////////////////////////////////////////////////////////////////// -// Declaration, forward -void runTest(int argc, char **argv); - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// - -int main(int argc, char **argv) -{ - printf("%s starting...\n", sampleName); - - runTest(argc, argv); - - exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -void runTest(int argc, char **argv) -{ - int Nblocks = 2; - int Nthreads = 32; - - // Kernel configuration, where a one-dimensional - // grid and one-dimensional blocks are configured. - - dim3 dimGrid(Nblocks); - dim3 dimBlock(Nthreads); - - printf("Launch kernel to generate assertion failures\n"); - char *cubin, *kernel_file; - size_t cubinSize; - - kernel_file = sdkFindFilePath("simpleAssert_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - - CUmodule module = loadCUBIN(cubin, argc, argv); - CUfunction kernel_addr; - - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "testKernel")); - - int count = 60; - void *args[] = {(void *)&count}; - - checkCudaErrors(cuLaunchKernel(kernel_addr, - dimGrid.x, - dimGrid.y, - dimGrid.z, /* grid dim */ - dimBlock.x, - dimBlock.y, - dimBlock.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &args[0], /* arguments */ - 0)); - - // Synchronize (flushes assert output). - printf("\n-- Begin assert output\n\n"); - CUresult res = cuCtxSynchronize(); - - printf("\n-- End assert output\n\n"); - - // Check for errors and failed asserts in asynchronous kernel launch. - if (res == CUDA_ERROR_ASSERT) { - printf("Device assert failed as expected\n"); - } - - testResult = res == CUDA_ERROR_ASSERT; -} diff --git a/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert_kernel.cu b/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert_kernel.cu deleted file mode 100644 index ada02586..00000000 --- a/cpp/0_Introduction/simpleAssert_nvrtc/simpleAssert_kernel.cu +++ /dev/null @@ -1,39 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -//////////////////////////////////////////////////////////////////////////////// -// Kernels -//////////////////////////////////////////////////////////////////////////////// -//! Tests assert function. -//! Thread whose id > N will print assertion failed error message. -//////////////////////////////////////////////////////////////////////////////// - -extern "C" __global__ void testKernel(int N) -{ - int gtid = blockIdx.x * blockDim.x + threadIdx.x; - assert(gtid < N); -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/extensions.json b/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/CMakeLists.txt b/cpp/0_Introduction/simpleAtomicIntrinsics/CMakeLists.txt index 7ec8e447..0bdbf076 100644 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/CMakeLists.txt +++ b/cpp/0_Introduction/simpleAtomicIntrinsics/CMakeLists.txt @@ -1,35 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleAtomicIntrinsics LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleAtomicIntrinsics -add_executable(simpleAtomicIntrinsics simpleAtomicIntrinsics.cu simpleAtomicIntrinsics_cpu.cpp) +add_executable(simpleAtomicIntrinsics simpleAtomicIntrinsics.cu) target_compile_options(simpleAtomicIntrinsics PRIVATE $<$:--extended-lambda>) target_compile_features(simpleAtomicIntrinsics PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleAtomicIntrinsics PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/README.md b/cpp/0_Introduction/simpleAtomicIntrinsics/README.md index 6ee89706..bc97dccc 100644 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/README.md +++ b/cpp/0_Introduction/simpleAtomicIntrinsics/README.md @@ -1,32 +1,123 @@ -# simpleAtomicIntrinsics - Simple Atomic Intrinsics +# simpleAtomicIntrinsics - Atomic vs. Non-Atomic Operations ## Description -A simple demonstration of global memory atomic instructions. +A CUDA sample that demonstrates **why atomic operations are needed** by running the same work with and without atomics under heavy contention. 1,000,000 threads write into an array of just 10 integers, so roughly 100,000 threads collide on every element. Three operations — add, max, and compare-and-swap — are each shown four ways: a non-atomic kernel, the CUDA atomic intrinsic, `cuda::std::atomic_ref` from CCCL (the `std::atomic` API in device code), and `cuda::atomic_ref` from CCCL (the CUDA-specific variant that natively supports thread scopes): + +Both CCCL variants use `atomic_ref` rather than `atomic` so they act on the *existing* plain-`int` array — this matches the behavior of the intrinsic atomics, which also adds atomic access to memory that already exists. `atomic` would instead require the array elements themselves to be declared separately as the atomic type. + +| Operation | Non-atomic | Intrinsic | cuda::std::atomic_ref | cuda::atomic_ref | +|---|---|---|---|---| +| Add 1 to an element | `increment` | `increment_atomic` | `increment_atomic_std` | `increment_atomic_cuda` | +| Keep the maximum value | `max` | `max_atomic` | `max_atomic_std` | `max_atomic_cuda` | +| Increment via compare-and-swap | `cas` | `cas_atomic` | `cas_atomic_std` | `cas_atomic_cuda` | + +The non-atomic kernels perform the read-modify-write as separate steps, so concurrent threads interleave and lose updates. The atomic kernels perform it as one indivisible hardware operation and produce the exact expected result every time. + +## What You'll Learn + +- What a race condition looks like: the non-atomic results are dramatically (add, CAS) or subtly (max) wrong +- Using the atomic intrinsics `atomicAdd`, `atomicMax`, and `atomicCAS` on global memory +- Building an atomic operation from an `atomicCAS` retry loop — the pattern that can implement any read-modify-write atomically +- Using `cuda::std::atomic_ref` (libcu++/CCCL) to write the same atomics with the standard `std::atomic` API in device code +- Using `cuda::atomic_ref` (CCCL) — the CUDA-specific variant that shares the same API as `cuda::std::atomic_ref` but natively supports CUDA thread scopes +- Why dedicated intrinsics beat CAS loops under contention (compare the `cas_atomic` timing against `increment_atomic`) +- Timing GPU work with CUDA events (`cudaEventRecord` / `cudaEventElapsedTime`) +- Resetting device buffers between runs with `cudaMemset` ## Key Concepts -Atomic Intrinsics +- **Race condition** — a plain `g[i] = g[i] + 1` is three steps (read, modify, write); two threads can read the same old value and one increment is lost +- **Atomic read-modify-write** — the hardware serializes atomic updates to the same address (performed at the L2 cache), so no update is lost +- **Compare-and-swap (CAS)** — `atomicCAS(addr, expected, desired)` swaps only if the current value equals `expected` and returns the value it found; looping until the swap succeeds makes any operation atomic +- **Contention cost** — atomics are correct but serialize colliding threads; the CAS retry loop shows this at its most extreme -## Supported SM Architectures +## Key APIs -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +### CUDA Device Intrinsics +- `atomicAdd` — atomically add a value to a memory location +- `atomicMax` — atomically store the maximum of the current and a proposed value +- `atomicCAS` — atomically compare-and-swap; returns the previous value -## Supported OSes +### libcu++ (CCCL) +- `cuda::std::atomic_ref` — wraps plain memory with the standard `std::atomic` interface, usable in device code; header: `` +- `cuda::atomic_ref` — CUDA-specific version of `std::atomic_ref`; same API but natively supports CUDA thread scopes (e.g. `cuda::thread_scope_device`); header: `` +- `fetch_add` — atomic add, `std::atomic` style +- `load` / `compare_exchange_weak` — the standard CAS retry loop; used to build max (which `std::atomic` lacks) and the CAS increment -Linux, Windows, QNX +### CUDA Runtime +- `cudaMalloc` / `cudaFree` — allocate and release device memory +- `cudaMemset` — fill device memory with a byte value (zero the array between runs) +- `cudaMemcpy` — copy results back to the host +- `cudaEventCreate` / `cudaEventRecord` / `cudaEventSynchronize` / `cudaEventElapsedTime` / `cudaEventDestroy` — GPU-timeline timing of each kernel -## Supported CPU Architecture +## Requirements -x86_64, armv7l, aarch64 +### Hardware +- NVIDIA GPU with Compute Capability 7.5 or higher -## CUDA APIs involved +### Software +- CUDA Toolkit +- CMake 3.20 or newer +- A C++17-capable host compiler -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaStreamCreateWithFlags, cudaFree, cudaMallocHost, cudaFreeHost, cudaStreamSynchronize, cudaMalloc, cudaMemcpyAsync +## How to Build -## Prerequisites +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +## How to Run -## References (for more details) +```bash +./simpleAtomicIntrinsics +``` + +No command-line arguments are required. The sample always runs on device 0. + +## Expected Output + +```text +=== Atomic vs. non-atomic operations (intrinsics, cuda::std::atomic_ref, cuda::atomic_ref) === + +GPU Device 0: with compute capability X.Y and Number of SMs + +1000000 total threads in 1000 blocks writing into 10 array elements + +[add] expected: every element = 100000 +non-atomic (0.19 ms): { 13 13 13 13 13 13 13 13 13 13 } +atomicAdd() (0.19 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } +cuda::std::atomic_ref::fetch_add() (0.26 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } +cuda::atomic_ref::fetch_add() (0.26 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } + +[max] expected: element i = 999990 + i +non-atomic (0.013 ms): { 998070 998071 998072 998073 998064 998065 998066 998067 998068 998069 } +atomicMax() (0.18 ms): { 999990 999991 999992 999993 999994 999995 999996 999997 999998 999999 } +cuda::std::atomic_ref::compare_exchange_weak() (1.0 ms): { 999990 999991 999992 999993 999994 999995 999996 999997 999998 999999 } +cuda::atomic_ref::compare_exchange_weak() (1.0 ms): { 999990 999991 999992 999993 999994 999995 999996 999997 999998 999999 } + +[CAS] expected: every element = 100000 +non-atomic (0.014 ms): { 13 13 13 13 13 13 13 13 13 13 } +atomicCAS() (1716 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } +cuda::std::atomic_ref::compare_exchange_weak() (9934 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } +cuda::atomic_ref::compare_exchange_weak() (9934 ms): { 100000 100000 100000 100000 100000 100000 100000 100000 100000 100000 } + +The non-atomic kernels lose updates when threads race on the same +element; the atomic kernels match the expected values exactly. +``` + +**Reading the numbers:** +- The non-atomic values vary from run to run — that nondeterminism is the race condition itself +- `max` is the sneakiest failure: 998070 looks plausible next to the correct 999990 +- `cas_atomic` is orders of magnitude slower than `increment_atomic` for the same result: with ~100,000 threads contending per element, almost every CAS attempt fails and retries — use the dedicated intrinsic when one exists +- The `_std` and `_cuda` kernels are correct but slower than the intrinsics: both `cuda::std::atomic_ref` and `cuda::atomic_ref` default to sequentially-consistent ordering at system scope — a stronger guarantee than the relaxed device-scope intrinsics (which is why their timings match each other in every section above) + +## Files + +- `simpleAtomicIntrinsics.cu` — the twelve kernels and the host driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA Programming Guide — Atomics](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/writing-cuda-kernels.html#atomics) +- [CUDA Programming Guide — Atomic Functions](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-extensions.html#atomic-functions) +- [CUDA Core Compute Libraries — cuda::atomic](https://nvidia.github.io/cccl/unstable/libcudacxx/extended_api/synchronization_primitives/atomic.html) diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics.cu b/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics.cu index 78fe3ee6..d6976579 100644 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics.cu +++ b/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -25,110 +25,428 @@ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ -/* A simple program demonstrating trivial use of global memory atomic - * device functions (atomic*() functions). +/* A simple program demonstrating why atomic operations are needed, using the + * atomic intrinsics atomicAdd, atomicMax, and atomicCAS. + * + * 1,000,000 threads write into an array of only 10 integers (each thread maps + * to a slot with index % ARRAY_SIZE), so ~100,000 threads update every element + * at the same time. A plain update like g[i] = g[i] + 1 is three separate + * steps: read the value, modify it, write it back. When two threads interleave + * (both read the same old value, both write back the same result), one update + * silently overwrites the other and is lost. This is a race condition: the + * result depends on thread timing, not program logic, and changes every run. + * + * Each operation is therefore run four ways: a non-atomic kernel that loses + * most of its updates to these race conditions, and three atomic kernels that + * do the read-modify-write as one indivisible operation and give the exact + * result — first with the CUDA atomic intrinsics, then with + * cuda::std::atomic_ref from CCCL (the std::atomic API in device code), and + * finally with cuda::atomic_ref from CCCL (the CUDA-specific variant that + * natively supports CUDA thread scopes). */ // includes, system -#include #include -#include -#include - -#ifdef _WIN32 -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#endif // Includes CUDA #include -// Utilities and timing functions -#include // includes cuda.h and cuda_runtime_api.h +// Includes CCCL: std::atomic as usable in device code (cuda::std::atomic_ref) +#include -// CUDA helper functions -#include // helper functions for CUDA error check +// cuda::atomic_ref: like cuda::std::atomic_ref but CUDA-specific, supports thread scopes +#include -// Includes, kernels -#include "simpleAtomicIntrinsics_kernel.cuh" +// and cuda::ceil_div for computing the launch grid size +#include -const char *sampleName = "simpleAtomicIntrinsics"; -//////////////////////////////////////////////////////////////////////////////// -// Auto-Verification Code -bool testResult = true; +// Launch configuration: many threads hammering a small array +#define NUM_THREADS 1000000 +#define BLOCK_WIDTH 1000 +#define ARRAY_SIZE 10 -//////////////////////////////////////////////////////////////////////////////// -// Declaration, forward -void runTest(int argc, char **argv); +// Increment without atomics: threads race on shared elements and lose updates +__global__ void increment(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + // ceil_div rounds up the grid, this may launch extra threads + // Adding this guard to ignore extra threads + if (tid < NUM_THREADS) { + // each thread increments one element, wrapping at ARRAY_SIZE + int i = tid % ARRAY_SIZE; + g[i] = g[i] + 1; + } +} -extern "C" bool computeGold(int *gpuData, const int len); +// Increment with atomicAdd: read-modify-write is one indivisible operation +__global__ void increment_atomic(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + atomicAdd(&g[i], 1); + } +} + +// Increment with cuda::std::atomic_ref: wraps the plain int in g[] and exposes +// the exact std::atomic API (fetch_add, load, store, ...) inside device code +__global__ void increment_atomic_std(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + cuda::std::atomic_ref ref(g[i]); + ref.fetch_add(1); + } +} + +// Increment with cuda::atomic_ref: like cuda::std::atomic_ref but CUDA-specific +// and natively supports CUDA thread scopes +__global__ void increment_atomic_cuda(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + cuda::atomic_ref ref(g[i]); + ref.fetch_add(1); + } +} + +// Max without atomics: another thread can write between the compare and the +// store, so a smaller value can overwrite a larger one +__global__ void max(int *g) +{ + // each thread contributes its global index as the value + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + if (g[i] < tid) + g[i] = tid; + } +} + +// Max with atomicMax: the compare and the store happen as one operation +__global__ void max_atomic(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + atomicMax(&g[i], tid); + } +} + +// Max with cuda::std::atomic_ref: std::atomic has no fetch_max, so max is +// built the standard C++ way — a compare_exchange loop that stops as soon as +// the stored value is already >= ours +__global__ void max_atomic_std(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + cuda::std::atomic_ref ref(g[i]); + int expected = ref.load(); + while (expected < tid && !ref.compare_exchange_weak(expected, tid)) { + } + } +} + +// Max with cuda::atomic_ref: same compare_exchange loop as the _std variant +__global__ void max_atomic_cuda(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + cuda::atomic_ref ref(g[i]); + int expected = ref.load(); + while (expected < tid && !ref.compare_exchange_weak(expected, tid)) { + } + } +} + +// Increment written as compare-and-swap, without atomics: the value can change +// between the compare and the swap, so increments are lost +__global__ void cas(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + int expected = g[i]; // read + if (g[i] == expected) // compare + g[i] = expected + 1; // swap (not atomic with the compare!) + } +} + +// Increment with an atomicCAS retry loop: the classic pattern for building +// any atomic operation out of compare-and-swap. atomicCAS returns the value +// it found: if another thread interfered, the swap did not happen and we retry +__global__ void cas_atomic(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + int old = g[i]; + int assumed; + do { + assumed = old; + old = atomicCAS(&g[i], assumed, assumed + 1); + } while (old != assumed); + } +} + +// Increment with cuda::std::atomic_ref compare_exchange: the std::atomic way +// to write a CAS retry loop. On failure, expected is updated with the value +// actually found, so the loop just retries with fresh data +__global__ void cas_atomic_std(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + cuda::std::atomic_ref ref(g[i]); + int expected = ref.load(); + while (!ref.compare_exchange_weak(expected, expected + 1)) { + } + } +} + +// CAS increment with cuda::atomic_ref: same retry loop as the _std variant +__global__ void cas_atomic_cuda(int *g) +{ + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < NUM_THREADS) { + int i = tid % ARRAY_SIZE; + + cuda::atomic_ref ref(g[i]); + int expected = ref.load(); + while (!ref.compare_exchange_weak(expected, expected + 1)) { + } + } +} + +// Print every element of the array +void print_array(int *array, int size) +{ + printf("{ "); + for (int i = 0; i < size; i++) + printf("%d ", array[i]); + printf("}\n"); +} -//////////////////////////////////////////////////////////////////////////////// // Program main -//////////////////////////////////////////////////////////////////////////////// int main(int argc, char **argv) { - printf("%s starting...\n", sampleName); + printf("=== Atomic vs. non-atomic operations (intrinsics, cuda::std::atomic_ref, cuda::atomic_ref) ===\n\n"); - runTest(argc, argv); + // Select device 0 as the active GPU + int devID = 0; + cudaSetDevice(devID); - printf("%s completed, returned %s\n", sampleName, testResult ? "OK" : "ERROR!"); - exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUDA -//////////////////////////////////////////////////////////////////////////////// -void runTest(int argc, char **argv) -{ - cudaStream_t stream; - // This will pick the best possible CUDA capable device - findCudaDevice(argc, (const char **)argv); - - StopWatchInterface *timer; - sdkCreateTimer(&timer); - sdkStartTimer(&timer); - - unsigned int numThreads = 256; - unsigned int numBlocks = 64; - unsigned int numData = 11; - unsigned int memSize = sizeof(int) * numData; - - // allocate mem for the result on host side - int *hOData; - checkCudaErrors(cudaMallocHost(&hOData, memSize)); - - // initialize the memory - for (unsigned int i = 0; i < numData; i++) - hOData[i] = 0; - - // To make the AND and XOR tests generate something other than 0... - hOData[8] = hOData[10] = 0xff; - - checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking)); - // allocate device memory for result - int *dOData; - checkCudaErrors(cudaMalloc((void **)&dOData, memSize)); - // copy host memory to device to initialize to zero - checkCudaErrors(cudaMemcpyAsync(dOData, hOData, memSize, cudaMemcpyHostToDevice, stream)); - - // execute the kernel - testKernel<<>>(dOData); - - // Copy result from device to host - checkCudaErrors(cudaMemcpyAsync(hOData, dOData, memSize, cudaMemcpyDeviceToHost, stream)); - checkCudaErrors(cudaStreamSynchronize(stream)); - - sdkStopTimer(&timer); - printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer)); - sdkDeleteTimer(&timer); - - // Compute reference solution - testResult = computeGold(hOData, numThreads * numBlocks); - - // Cleanup memory - checkCudaErrors(cudaFreeHost(hOData)); - checkCudaErrors(cudaFree(dOData)); + // Query compute capability (major.minor) and number of SMs on the device + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); + + // Print device info + printf("GPU Device %d: with compute capability %d.%d and Number of SMs %d\n\n", devID, major, minor, smCount); + + // enough blocks to cover all threads, rounding up if not evenly divisible + int numBlocks = cuda::ceil_div(NUM_THREADS, BLOCK_WIDTH); + printf("%d total threads in %d blocks writing into %d array elements\n\n", + NUM_THREADS, numBlocks, ARRAY_SIZE); + + // declare and allocate host memory + int h_array[ARRAY_SIZE]; + const int ARRAY_BYTES = ARRAY_SIZE * sizeof(int); + + // declare and allocate GPU memory (zeroed with cudaMemset before each run) + int *d_array; + cudaMalloc((void **)&d_array, ARRAY_BYTES); + + // CUDA events record timestamps on the GPU stream to measure device time + cudaEvent_t start, stop; + cudaEventCreate(&start); + cudaEventCreate(&stop); + float elapsed_ms; + + // ----- add: every thread adds 1, each element should reach 100000 ----- + printf("[add] expected: every element = %d\n", NUM_THREADS / ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + increment<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-36s (%g ms): ", "non-atomic", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + increment_atomic<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-36s (%g ms): ", "atomicAdd()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + increment_atomic_std<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-36s (%g ms): ", "cuda::std::atomic_ref::fetch_add()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + increment_atomic_cuda<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-36s (%g ms): ", "cuda::atomic_ref::fetch_add()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + // ----- max: every thread offers its index, element i should reach + // the largest index that maps to it: NUM_THREADS - ARRAY_SIZE + i ----- + printf("\n[max] expected: element i = %d + i\n", NUM_THREADS - ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + max<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "non-atomic", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + max_atomic<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "atomicMax()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + max_atomic_std<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "cuda::std::atomic_ref::compare_exchange_weak()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + max_atomic_cuda<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "cuda::atomic_ref::compare_exchange_weak()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + // ----- CAS: increment built from compare-and-swap, same expected + // result as add ----- + printf("\n[CAS] expected: every element = %d\n", NUM_THREADS / ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + cas<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "non-atomic", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + cas_atomic<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "atomicCAS()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + cas_atomic_std<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "cuda::std::atomic_ref::compare_exchange_weak()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + cudaMemset((void *)d_array, 0, ARRAY_BYTES); + + cudaEventRecord(start); + cas_atomic_cuda<<>>(d_array); + cudaEventRecord(stop); + + cudaEventSynchronize(stop); + cudaEventElapsedTime(&elapsed_ms, start, stop); + cudaMemcpy(h_array, d_array, ARRAY_BYTES, cudaMemcpyDeviceToHost); + + printf("%-46s (%g ms): ", "cuda::atomic_ref::compare_exchange_weak()", elapsed_ms); + print_array(h_array, ARRAY_SIZE); + + printf("\nThe non-atomic kernels lose updates when threads race on the same\n"); + printf("element; the atomic kernels match the expected values exactly.\n"); + + // free GPU memory allocation and timing events, then exit + cudaEventDestroy(start); + cudaEventDestroy(stop); + cudaFree(d_array); + return 0; } diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_cpu.cpp b/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_cpu.cpp deleted file mode 100644 index 92d6622d..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_cpu.cpp +++ /dev/null @@ -1,183 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include - -#define min(a, b) (a) < (b) ? (a) : (b) -#define max(a, b) (a) > (b) ? (a) : (b) - -//////////////////////////////////////////////////////////////////////////////// -// export C interface -extern "C" int computeGold(int *gpuData, const int len); - -//////////////////////////////////////////////////////////////////////////////// -//! Compute reference data set -//! Each element is multiplied with the number of threads / array length -//! @param reference reference data, computed but preallocated -//! @param idata input data as provided to device -//! @param len number of elements in reference / idata -//////////////////////////////////////////////////////////////////////////////// -int computeGold(int *gpuData, const int len) -{ - int val = 0; - - for (int i = 0; i < len; ++i) { - val += 10; - } - - if (val != gpuData[0]) { - printf("atomicAdd failed\n"); - return false; - } - - val = 0; - - for (int i = 0; i < len; ++i) { - val -= 10; - } - - if (val != gpuData[1]) { - printf("atomicSub failed\n"); - return false; - } - - bool found = false; - - for (int i = 0; i < len; ++i) { - // third element should be a member of [0, len) - if (i == gpuData[2]) { - found = true; - break; - } - } - - if (!found) { - printf("atomicExch failed\n"); - return false; - } - - val = -(1 << 8); - - for (int i = 0; i < len; ++i) { - // fourth element should be len-1 - val = max(val, i); - } - - if (val != gpuData[3]) { - printf("atomicMax failed\n"); - return false; - } - - val = 1 << 8; - - for (int i = 0; i < len; ++i) { - val = min(val, i); - } - - if (val != gpuData[4]) { - printf("atomicMin failed\n"); - return false; - } - - int limit = 17; - val = 0; - - for (int i = 0; i < len; ++i) { - val = (val >= limit) ? 0 : val + 1; - } - - if (val != gpuData[5]) { - printf("atomicInc failed\n"); - return false; - } - - limit = 137; - val = 0; - - for (int i = 0; i < len; ++i) { - val = ((val == 0) || (val > limit)) ? limit : val - 1; - } - - if (val != gpuData[6]) { - printf("atomicDec failed\n"); - return false; - } - - found = false; - - for (int i = 0; i < len; ++i) { - // eighth element should be a member of [0, len) - if (i == gpuData[7]) { - found = true; - break; - } - } - - if (!found) { - printf("atomicCAS failed\n"); - return false; - } - - val = 0xff; - - for (int i = 0; i < len; ++i) { - // 9th element should be 1 - val &= (2 * i + 7); - } - - if (val != gpuData[8]) { - printf("atomicAnd failed\n"); - return false; - } - - val = 0; - - for (int i = 0; i < len; ++i) { - // 10th element should be 0xff - val |= (1 << i); - } - - if (val != gpuData[9]) { - printf("atomicOr failed\n"); - return false; - } - - val = 0xff; - - for (int i = 0; i < len; ++i) { - // 11th element should be 0xff - val ^= i; - } - - if (val != gpuData[10]) { - printf("atomicXor failed\n"); - return false; - } - - return true; -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_kernel.cuh b/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_kernel.cuh deleted file mode 100644 index 09714d08..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics/simpleAtomicIntrinsics_kernel.cuh +++ /dev/null @@ -1,83 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* Simple kernel demonstrating atomic functions in device code. */ - -#ifndef _SIMPLEATOMICS_KERNEL_H_ -#define _SIMPLEATOMICS_KERNEL_H_ - -//////////////////////////////////////////////////////////////////////////////// -//! Simple test kernel for atomic instructions -//! @param g_idata input data in global memory -//! @param g_odata output data in global memory -//////////////////////////////////////////////////////////////////////////////// -__global__ void testKernel(int *g_odata) -{ - // access thread id - const unsigned int tid = blockDim.x * blockIdx.x + threadIdx.x; - - // Test various atomic instructions - - // Arithmetic atomic instructions - - // Atomic addition - atomicAdd(&g_odata[0], 10); - - // Atomic subtraction (final should be 0) - atomicSub(&g_odata[1], 10); - - // Atomic exchange - atomicExch(&g_odata[2], tid); - - // Atomic maximum - atomicMax(&g_odata[3], tid); - - // Atomic minimum - atomicMin(&g_odata[4], tid); - - // Atomic increment (modulo 17+1) - atomicInc((unsigned int *)&g_odata[5], 17); - - // Atomic decrement - atomicDec((unsigned int *)&g_odata[6], 137); - - // Atomic compare-and-swap - atomicCAS(&g_odata[7], tid - 1, tid); - - // Bitwise atomic instructions - - // Atomic AND - atomicAnd(&g_odata[8], 2 * tid + 7); - - // Atomic OR - atomicOr(&g_odata[9], 1 << tid); - - // Atomic XOR - atomicXor(&g_odata[10], tid); -} - -#endif // #ifndef _SIMPLEATOMICS_KERNEL_H_ diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/extensions.json b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/CMakeLists.txt b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/CMakeLists.txt deleted file mode 100644 index 9c659d62..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,44 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleAtomicIntrinsics_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add sample target executable -add_executable(simpleAtomicIntrinsics_nvrtc simpleAtomicIntrinsics_cpu.cpp simpleAtomicIntrinsics.cpp) - -target_compile_options(simpleAtomicIntrinsics_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleAtomicIntrinsics_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -target_link_libraries(simpleAtomicIntrinsics_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy clock_kernel.cu to the output directory -add_custom_command(TARGET simpleAtomicIntrinsics_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/simpleAtomicIntrinsics_kernel.cuh - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/README.md b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/README.md deleted file mode 100644 index 4b8264c9..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# simpleAtomicIntrinsics_nvrtc - Simple Atomic Intrinsics with libNVRTC - -## Description - -A simple demonstration of global memory atomic instructions.This sample makes use of NVRTC for Runtime Compilation. - -## Key Concepts - -Atomic Intrinsics, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuMemcpyHtoD, cuCtxSynchronize, cuMemAlloc, cuMemFree, cuModuleGetFunction - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaBlockSize, cudaGridSize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics.cpp b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics.cpp deleted file mode 100644 index 0e716c49..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics.cpp +++ /dev/null @@ -1,153 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* A simple program demonstrating trivial use of global memory atomic - * device functions (atomic*() functions). - */ - -// includes, system -#include -#include -#include -#include - -#ifdef _WIN32 -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#endif - -// Includes CUDA -#include -#include - -// Utilities and timing functions -#include // includes cuda.h and cuda_runtime_api.h - -const char *sampleName = "simpleAtomicIntrinsics_nvrtc"; - -//////////////////////////////////////////////////////////////////////////////// -// Auto-Verification Code -bool testResult = true; - -//////////////////////////////////////////////////////////////////////////////// -// Declaration, forward -void runTest(int argc, char **argv); - -extern "C" bool computeGold(int *gpuData, const int len); - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// - -int main(int argc, char **argv) -{ - printf("%s starting...\n", sampleName); - - runTest(argc, argv); - - printf("%s completed, returned %s\n", sampleName, testResult ? "OK" : "ERROR!"); - - exit(testResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUDA -//////////////////////////////////////////////////////////////////////////////// - -void runTest(int argc, char **argv) -{ - int dev = 0; - - char *cubin, *kernel_file; - size_t cubinSize; - - kernel_file = sdkFindFilePath("simpleAtomicIntrinsics_kernel.cuh", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - - CUmodule module = loadCUBIN(cubin, argc, argv); - CUfunction kernel_addr; - - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "testKernel")); - - StopWatchInterface *timer; - sdkCreateTimer(&timer); - sdkStartTimer(&timer); - - unsigned int numThreads = 256; - unsigned int numBlocks = 64; - unsigned int numData = 11; - unsigned int memSize = sizeof(int) * numData; - - // allocate mem for the result on host side - int *hOData = (int *)malloc(memSize); - - // initialize the memory - for (unsigned int i = 0; i < numData; i++) - hOData[i] = 0; - - // To make the AND and XOR tests generate something other than 0... - hOData[8] = hOData[10] = 0xff; - - // allocate device memory for result - CUdeviceptr dOData; - checkCudaErrors(cuMemAlloc(&dOData, memSize)); - checkCudaErrors(cuMemcpyHtoD(dOData, hOData, memSize)); - - // execute the kernel - dim3 cudaBlockSize(numThreads, 1, 1); - dim3 cudaGridSize(numBlocks, 1, 1); - - void *arr[] = {(void *)&dOData}; - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - cudaBlockSize.x, - cudaBlockSize.y, - cudaBlockSize.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &arr[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); - - checkCudaErrors(cuMemcpyDtoH(hOData, dOData, memSize)); - - // Copy result from device to host - sdkStopTimer(&timer); - printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer)); - sdkDeleteTimer(&timer); - - // Compute reference solution - testResult = computeGold(hOData, numThreads * numBlocks); - - // Cleanup memory - free(hOData); - checkCudaErrors(cuMemFree(dOData)); -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_cpu.cpp b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_cpu.cpp deleted file mode 100644 index 8510b49f..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_cpu.cpp +++ /dev/null @@ -1,182 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include - -#define min(a, b) (a) < (b) ? (a) : (b) -#define max(a, b) (a) > (b) ? (a) : (b) - -//////////////////////////////////////////////////////////////////////////////// -// export C interface -extern "C" int computeGold(int *gpuData, const int len); - -//////////////////////////////////////////////////////////////////////////////// -//! Compute reference data set -//! Each element is multiplied with the number of threads / array length -//! @param reference reference data, computed but preallocated -//! @param idata input data as provided to device -//! @param len number of elements in reference / idata -//////////////////////////////////////////////////////////////////////////////// - -int computeGold(int *gpuData, const int len) -{ - int val = 0; - - for (int i = 0; i < len; ++i) { - val += 10; - } - - if (val != gpuData[0]) { - printf("atomicAdd failed\n"); - return false; - } - - val = 0; - - for (int i = 0; i < len; ++i) { - val -= 10; - } - - if (val != gpuData[1]) { - printf("atomicSub failed\n"); - return false; - } - - bool found = false; - - for (int i = 0; i < len; ++i) { - // third element should be a member of [0, len) - if (i == gpuData[2]) { - found = true; - break; - } - } - - if (!found) { - printf("atomicExch failed\n"); - return false; - } - - val = -(1 << 8); - - for (int i = 0; i < len; ++i) { - // fourth element should be len-1 - val = max(val, i); - } - - if (val != gpuData[3]) { - printf("atomicMax failed\n"); - return false; - } - - val = 1 << 8; - - for (int i = 0; i < len; ++i) { - val = min(val, i); - } - - if (val != gpuData[4]) { - printf("atomicMin failed\n"); - return false; - } - - int limit = 17; - val = 0; - - for (int i = 0; i < len; ++i) { - val = (val >= limit) ? 0 : val + 1; - } - - if (val != gpuData[5]) { - printf("atomicInc failed\n"); - return false; - } - - limit = 137; - val = 0; - - for (int i = 0; i < len; ++i) { - val = ((val == 0) || (val > limit)) ? limit : val - 1; - } - - if (val != gpuData[6]) { - printf("atomicDec failed\n"); - return false; - } - - found = false; - - for (int i = 0; i < len; ++i) { - // eighth element should be a member of [0, len) - if (i == gpuData[7]) { - found = true; - break; - } - } - - if (!found) { - printf("atomicCAS failed\n"); - return false; - } - - val = 0xff; - for (int i = 0; i < len; ++i) { - // 9th element should be 1 - val &= (2 * i + 7); - } - - if (val != gpuData[8]) { - printf("atomicAnd failed\n"); - return false; - } - - val = 0; - for (int i = 0; i < len; ++i) { - // 10th element should be 0xff - val |= (1 << i); - } - - if (val != gpuData[9]) { - printf("atomicOr failed\n"); - return false; - } - - val = 0xff; - - for (int i = 0; i < len; ++i) { - // 11th element should be 0xff - val ^= i; - } - - if (val != gpuData[10]) { - printf("atomicXor failed\n"); - return false; - } - - return true; -} diff --git a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_kernel.cuh b/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_kernel.cuh deleted file mode 100644 index 3c4d284c..00000000 --- a/cpp/0_Introduction/simpleAtomicIntrinsics_nvrtc/simpleAtomicIntrinsics_kernel.cuh +++ /dev/null @@ -1,81 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* Simple kernel demonstrating atomic functions in device code. */ - -#ifndef _SIMPLEATOMICS_KERNEL_H_ -#define _SIMPLEATOMICS_KERNEL_H_ - -//////////////////////////////////////////////////////////////////////////////// -//! Simple test kernel for atomic instructions -//! @param g_idata input data in global memory -//! @param g_odata output data in global memory -//////////////////////////////////////////////////////////////////////////////// - -extern "C" __global__ void testKernel(int *g_odata) -{ - // access thread id - const unsigned int tid = blockDim.x * blockIdx.x + threadIdx.x; - - // Test various atomic instructions - // Arithmetic atomic instructions - // Atomic addition - atomicAdd(&g_odata[0], 10); - - // Atomic subtraction (final should be 0) - atomicSub(&g_odata[1], 10); - - // Atomic exchange - atomicExch(&g_odata[2], tid); - - // Atomic maximum - atomicMax(&g_odata[3], tid); - - // Atomic minimum - atomicMin(&g_odata[4], tid); - - // Atomic increment (modulo 17+1) - atomicInc((unsigned int *)&g_odata[5], 17); - - // Atomic decrement - atomicDec((unsigned int *)&g_odata[6], 137); - - // Atomic compare-and-swap - atomicCAS(&g_odata[7], tid - 1, tid); - - // Bitwise atomic instructions - // Atomic AND - atomicAnd(&g_odata[8], 2 * tid + 7); - - // Atomic OR - atomicOr(&g_odata[9], 1 << tid); - - // Atomic XOR - atomicXor(&g_odata[10], tid); -} - -#endif // #ifndef _SIMPLEATOMICS_KERNEL_H_ diff --git a/cpp/0_Introduction/simpleAttributes/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleAttributes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleAttributes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleAttributes/.vscode/extensions.json b/cpp/0_Introduction/simpleAttributes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleAttributes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleAttributes/CMakeLists.txt b/cpp/0_Introduction/simpleAttributes/CMakeLists.txt index d481f57b..d15710c3 100644 --- a/cpp/0_Introduction/simpleAttributes/CMakeLists.txt +++ b/cpp/0_Introduction/simpleAttributes/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleAttributes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleAttributes add_executable(simpleAttributes simpleAttributes.cu) target_compile_options(simpleAttributes PRIVATE $<$:--extended-lambda>) target_compile_features(simpleAttributes PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleAttributes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleCUDA2GL/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleCUDA2GL/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleCUDA2GL/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleCUDA2GL/.vscode/extensions.json b/cpp/0_Introduction/simpleCUDA2GL/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleCUDA2GL/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleCUDA2GL/CMakeLists.txt b/cpp/0_Introduction/simpleCUDA2GL/CMakeLists.txt index 661eba02..3bb34e01 100644 --- a/cpp/0_Introduction/simpleCUDA2GL/CMakeLists.txt +++ b/cpp/0_Introduction/simpleCUDA2GL/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleCUDA2GL LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -30,31 +26,22 @@ if(WIN32) endif() endif() - find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample simpleCUDA2GL - not supported on aarch64") else() - # Add target for simpleCUDA2GL add_executable(simpleCUDA2GL simpleCUDA2GL.cu main.cpp) - target_compile_options(simpleCUDA2GL PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleCUDA2GL PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleCUDA2GL PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleCUDA2GL PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(simpleCUDA2GL ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} @@ -64,14 +51,12 @@ if(${OpenGL_FOUND}) ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET simpleCUDA2GL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET simpleCUDA2GL POST_BUILD COMMAND ${CMAKE_COMMAND} -E @@ -79,6 +64,8 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() + include(InstallSamples) + setup_samples_install() endif() else() message(STATUS "GLUT not found - will not build sample 'simpleCUDA2GL'") @@ -86,7 +73,3 @@ if(${OpenGL_FOUND}) else() message(STATUS "OpenGL not found - will not build sample 'simpleCUDA2GL'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleCallback/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleCallback/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleCallback/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleCallback/.vscode/extensions.json b/cpp/0_Introduction/simpleCallback/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleCallback/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleCallback/CMakeLists.txt b/cpp/0_Introduction/simpleCallback/CMakeLists.txt index 8c19b28f..9bd36e54 100644 --- a/cpp/0_Introduction/simpleCallback/CMakeLists.txt +++ b/cpp/0_Introduction/simpleCallback/CMakeLists.txt @@ -1,34 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleCallback LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleCallback -add_executable(simpleCallback simpleCallback.cu multithreading.cpp) +add_executable(simpleCallback simpleCallback.cu) target_compile_options(simpleCallback PRIVATE $<$:--extended-lambda>) target_compile_features(simpleCallback PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleCallback PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +# std::thread is backed by pthreads on Linux +find_package(Threads REQUIRED) +target_link_libraries(simpleCallback PRIVATE Threads::Threads) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) + +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleCallback/README.md b/cpp/0_Introduction/simpleCallback/README.md index 53e23aab..9ba7aa00 100644 --- a/cpp/0_Introduction/simpleCallback/README.md +++ b/cpp/0_Introduction/simpleCallback/README.md @@ -1,32 +1,133 @@ -# simpleCallback - Simple CUDA Callbacks +# Sample: simpleCallback ## Description -This sample implements multi-threaded heterogeneous computing workloads with the new CPU callbacks for CUDA streams and events introduced with CUDA 5.0. +A heterogeneous pipeline demo: **CPU pre-process → GPU kernel → CPU post-process**, all +coordinated by a single CUDA stream. A worker thread (`std::thread`) fills the input buffer, +enqueues the GPU work (H2D copy, kernel, D2H copy), and registers a host function with +`cudaLaunchHostFunc` — the modern replacement for the deprecated `cudaStreamAddCallback`. +CUDA invokes that host function automatically once all preceding stream work completes, +so the CPU post-processing (result verification) runs without the main thread ever polling. + +## What You'll Learn + +- Building a CPU → GPU → CPU pipeline ordered by a single CUDA stream +- Scheduling CPU post-processing on a stream with `cudaLaunchHostFunc` +- Enqueuing GPU work from a `std::thread` worker — all host threads share the device's primary context +- The rule that a host function must never call CUDA APIs +- Why pinned host memory (`cudaMallocHost`) is required for `cudaMemcpyAsync` to be truly asynchronous +- Querying GPU properties (compute capability, SM count) with `cudaDeviceGetAttribute` ## Key Concepts -CUDA Streams, Callback Functions, Multithreading +### Stream-Ordered Pipeline -## Supported SM Architectures +A stream executes its operations strictly in order. The worker thread pushes the whole +pipeline onto one stream and CUDA handles every dependency: -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +```text +stream: [H2D copy] → [incrementKernel] → [D2H copy] → [postprocess host func] +``` -## Supported OSes +The main thread only has to `cudaStreamSynchronize` once at the end — when the stream +drains, the results are already verified. -Linux, Windows +### Host Functions (`cudaLaunchHostFunc`) -## Supported CPU Architecture +A host function is a CPU callback enqueued on a stream like any other operation. CUDA runs +it on an internal thread once all prior work in the stream has finished. Two rules apply: -x86_64, armv7l +- It must **not** call any CUDA runtime or driver API (no `cudaFree`, no kernel launches) +- It should be short — the stream cannot proceed until it returns -## CUDA APIs involved +### Threads Share the Primary Context -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaHostAlloc, cudaStreamDestroy, cudaFree, cudaSetDevice, cudaGetDeviceCount, cudaFreeHost, cudaStreamAddCallback, cudaMalloc, cudaMemcpyAsync, cudaStreamCreate, cudaGetDeviceProperties +With the CUDA runtime API, every host thread in the process shares the device's primary +context. That is why the worker thread can enqueue copies and kernels onto a stream that +`main` created. -## Prerequisites +### Pinned Host Memory -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +`cudaMemcpyAsync` only runs *truly asynchronously* when the host buffer is page-locked (pinned), +so the OS cannot move the pages mid-transfer; with ordinary pageable memory the copy falls back to +synchronous behavior. The sample allocates the host buffer with `cudaMallocHost`. -## References (for more details) +## Key APIs + +### CUDA Runtime + +- `cudaSetDevice` — select the active GPU for all subsequent CUDA calls +- `cudaDeviceGetAttribute` — query compute capability and SM count +- `cudaMallocHost` / `cudaFreeHost` — allocate and free pinned host memory +- `cudaMalloc` / `cudaFree` — allocate and free device memory +- `cudaStreamCreate` / `cudaStreamDestroy` — create and destroy the stream that orders the pipeline +- `cudaMemcpyAsync` — non-blocking H2D and D2H transfers on the stream +- `cudaLaunchHostFunc` — enqueue a CPU callback on the stream +- `cudaStreamSynchronize` — block the host until the stream (including the host function) is done + +### C++ Standard Library + +- `std::thread` / `join()` — worker-thread creation + +## Requirements + +### Hardware + +- NVIDIA GPU with Compute Capability 7.5 or higher + +### Software + +- CMake 3.20 or newer +- A C++17-capable host compiler + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +```bash +./simpleCallback +``` + +No command-line arguments are required. The sample always runs on device 0. + +## Expected Output + +```text +===================================================== + simpleCallback: CPU -> GPU -> CPU pipeline demo +===================================================== +Using GPU 0: compute capability X.Y, SMs +[thread] Stage 1: CPU pre-processing on a worker thread... +[thread] Filled 100000 elements. First 3 inputs: 42, 43, 44 + +[thread] Stage 2: enqueuing H2D copy, kernel, D2H copy on the stream +[thread] Registering post-processing callback with cudaLaunchHostFunc + +[main] Worker thread joined; GPU work has been enqueued. +[main] Waiting for the stream (and callback) to finish... + +[host func] Stage 3: callback fired automatically - the GPU work is done! +[host func] First 3 results: 43, 44, 45 (each input +1) +[host func] Verified all 100000 results: PASS + +[main] Pipeline complete. Result: SUCCESS +``` + +**Reading the output:** +- Inputs start at 42 because each element is `workload.id + i` with `id = 42` +- Each result is its input plus one — the kernel's only job +- The `[host func]` lines print from a CUDA internal thread, not from `main` + +## Files + +- `simpleCallback.cu` — kernel, worker-thread function, host-function callback, and main driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA Programming Guide — Callback Functions from Streams](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#callback-functions-from-streams) +- [CUDA Programming Guide — CUDA Streams](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#cuda-streams) +- [CUDA Runtime API — Execution Control (cudaLaunchHostFunc)](https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EXECUTION.html#group__CUDART__EXECUTION_1g05841eaa5f90f27124241baafb3e856f) diff --git a/cpp/0_Introduction/simpleCallback/multithreading.cpp b/cpp/0_Introduction/simpleCallback/multithreading.cpp deleted file mode 100644 index d4c89913..00000000 --- a/cpp/0_Introduction/simpleCallback/multithreading.cpp +++ /dev/null @@ -1,152 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include "multithreading.h" - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -// Create thread -CUTThread cutStartThread(CUT_THREADROUTINE func, void *data) -{ - return CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE)func, data, 0, NULL); -} - -// Wait for thread to finish -void cutEndThread(CUTThread thread) -{ - WaitForSingleObject(thread, INFINITE); - CloseHandle(thread); -} - -// Wait for multiple threads -void cutWaitForThreads(const CUTThread *threads, int num) -{ - WaitForMultipleObjects(num, threads, true, INFINITE); - - for (int i = 0; i < num; i++) { - CloseHandle(threads[i]); - } -} - -// Create barrier. -CUTBarrier cutCreateBarrier(int releaseCount) -{ - CUTBarrier barrier; - - InitializeCriticalSection(&barrier.criticalSection); - barrier.barrierEvent = CreateEvent(NULL, TRUE, FALSE, TEXT("BarrierEvent")); - barrier.count = 0; - barrier.releaseCount = releaseCount; - - return barrier; -} - -// Increment barrier. (execution continues) -void cutIncrementBarrier(CUTBarrier *barrier) -{ - int myBarrierCount; - EnterCriticalSection(&barrier->criticalSection); - myBarrierCount = ++barrier->count; - LeaveCriticalSection(&barrier->criticalSection); - - if (myBarrierCount >= barrier->releaseCount) { - SetEvent(barrier->barrierEvent); - } -} - -// Wait for barrier release. -void cutWaitForBarrier(CUTBarrier *barrier) { WaitForSingleObject(barrier->barrierEvent, INFINITE); } - -// Destroy barrier -void cutDestroyBarrier(CUTBarrier *barrier) {} - -#else -// Create thread -CUTThread cutStartThread(CUT_THREADROUTINE func, void *data) -{ - pthread_t thread; - pthread_create(&thread, NULL, func, data); - return thread; -} - -// Wait for thread to finish -void cutEndThread(CUTThread thread) { pthread_join(thread, NULL); } - -// Wait for multiple threads -void cutWaitForThreads(const CUTThread *threads, int num) -{ - for (int i = 0; i < num; i++) { - cutEndThread(threads[i]); - } -} - -// Create barrier. -CUTBarrier cutCreateBarrier(int releaseCount) -{ - CUTBarrier barrier; - - barrier.count = 0; - barrier.releaseCount = releaseCount; - - pthread_mutex_init(&barrier.mutex, 0); - pthread_cond_init(&barrier.conditionVariable, 0); - - return barrier; -} - -// Increment barrier. (execution continues) -void cutIncrementBarrier(CUTBarrier *barrier) -{ - int myBarrierCount; - pthread_mutex_lock(&barrier->mutex); - myBarrierCount = ++barrier->count; - pthread_mutex_unlock(&barrier->mutex); - - if (myBarrierCount >= barrier->releaseCount) { - pthread_cond_signal(&barrier->conditionVariable); - } -} - -// Wait for barrier release. -void cutWaitForBarrier(CUTBarrier *barrier) -{ - pthread_mutex_lock(&barrier->mutex); - - while (barrier->count < barrier->releaseCount) { - pthread_cond_wait(&barrier->conditionVariable, &barrier->mutex); - } - - pthread_mutex_unlock(&barrier->mutex); -} - -// Destroy barrier -void cutDestroyBarrier(CUTBarrier *barrier) -{ - pthread_mutex_destroy(&barrier->mutex); - pthread_cond_destroy(&barrier->conditionVariable); -} - -#endif diff --git a/cpp/0_Introduction/simpleCallback/multithreading.h b/cpp/0_Introduction/simpleCallback/multithreading.h deleted file mode 100644 index d9b4b2a0..00000000 --- a/cpp/0_Introduction/simpleCallback/multithreading.h +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef MULTITHREADING_H -#define MULTITHREADING_H - -// Simple portable thread library. - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -// Windows threads. -#include - -typedef HANDLE CUTThread; -typedef unsigned(WINAPI *CUT_THREADROUTINE)(void *); - -struct CUTBarrier -{ - CRITICAL_SECTION criticalSection; - HANDLE barrierEvent; - int releaseCount; - int count; -}; - -#define CUT_THREADPROC unsigned WINAPI -#define CUT_THREADEND return 0 - -#else -// POSIX threads. -#include - -typedef pthread_t CUTThread; -typedef void *(*CUT_THREADROUTINE)(void *); - -#define CUT_THREADPROC void * -#define CUT_THREADEND return 0 - -struct CUTBarrier -{ - pthread_mutex_t mutex; - pthread_cond_t conditionVariable; - int releaseCount; - int count; -}; - -#endif - -#ifdef __cplusplus -extern "C" -{ -#endif - - // Create thread. - CUTThread cutStartThread(CUT_THREADROUTINE, void *data); - - // Wait for thread to finish. - void cutEndThread(CUTThread thread); - - // Wait for multiple threads. - void cutWaitForThreads(const CUTThread *threads, int num); - - // Create barrier. - CUTBarrier cutCreateBarrier(int releaseCount); - - // Increment barrier. (execution continues) - void cutIncrementBarrier(CUTBarrier *barrier); - - // Wait for barrier release. - void cutWaitForBarrier(CUTBarrier *barrier); - - // Destroy barrier - void cutDestroyBarrier(CUTBarrier *barrier); - -#ifdef __cplusplus -} // extern "C" -#endif - -#endif // MULTITHREADING_H diff --git a/cpp/0_Introduction/simpleCallback/simpleCallback.cu b/cpp/0_Introduction/simpleCallback/simpleCallback.cu index c8df6ee0..b57ad8ec 100644 --- a/cpp/0_Introduction/simpleCallback/simpleCallback.cu +++ b/cpp/0_Introduction/simpleCallback/simpleCallback.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -26,190 +26,171 @@ */ /* - * This sample implements multi-threaded heterogeneous computing workloads with - * the new CPU callbacks for CUDA streams and events introduced with CUDA 5.0. - * Together with the thread safety of the CUDA API implementing heterogeneous - * workloads that float between CPU threads and GPUs has become simple and - * efficient. + * simpleCallback + * -------------- + * An example of a heterogeneous pipeline in the form: * - * The workloads in the sample follow the form CPU preprocess -> GPU process -> - * CPU postprocess. - * Each CPU processing step is handled by its own dedicated thread. GPU - * workloads are sent to all available GPUs in the system. + * CPU pre-process -> GPU kernel -> CPU post-process * + * The whole pipeline is coordinated by a *single* CUDA stream. A stream + * executes the operations enqueued on it in order, so we can simply push work + * onto it and let CUDA take care of ordering and dependencies for us. + * + * Two different "CPU" mechanisms appear in this sample so you can see how they + * differ: + * + * 1. The CPU pre-processing runs on a dedicated worker thread created with + * C++ std::thread. This shows how ordinary host-side threading can be + * combined with CUDA. The worker fills the input buffer and then enqueues + * the GPU pipeline itself; main joins it and waits on the stream. + * + * 2. The CPU post-processing is scheduled *on the stream itself* with + * cudaLaunchHostFunc(). CUDA calls our host function automatically once all + * the preceding stream work (copies + kernel) has completed. This is the + * modern, non-deprecated replacement for cudaStreamAddCallback(). + * + * IMPORTANT rule for cudaLaunchHostFunc callbacks: + * The host function must NOT call any CUDA runtime/driver API (no cudaMalloc, + * cudaFree, kernel launches, etc.). It should only do plain CPU work. + * + * NOTE: For clarity this sample intentionally omits CUDA error checking. Real + * applications should check the return code of every CUDA call. */ // System includes -#include +#include +#include -// helper functions and utilities to work with CUDA -#include -#include +#include // std::thread — C++11 standard threading -#include "multithreading.h" +// CUDA runtime +#include -const int N_workloads = 8; -const int N_elements_per_workload = 100000; +const int NumElements = 100000; -CUTBarrier thread_barrier; - -void CUDART_CB myStreamCallback(cudaStream_t event, cudaError_t status, void *data); - -struct heterogeneous_workload +// Everything a single pipeline needs. Passed by pointer to both the +// std::thread worker and to the stream host-function callback. +struct Workload { - int id; - int cudaDeviceID; - - int *h_data; - int *d_data; - cudaStream_t stream; - - bool success; + int id = 0; // arbitrary tag added to each element + int *h_data = nullptr; // pinned host buffer (fast async copies) + int *d_data = nullptr; // device buffer + cudaStream_t stream = nullptr; // the single stream that orders all the work + bool success = false; // set by the post-processing callback }; -__global__ void incKernel(int *data, int N) +// GPU kernel: increment every element by one. +__global__ void incrementKernel(int *data, int n) { int i = blockIdx.x * blockDim.x + threadIdx.x; - - if (i < N) - data[i]++; + if (i < n) { + data[i] += 1; + } } -CUT_THREADPROC launch(void *void_arg) +// Forward declaration: postprocess is defined below but called from preprocess. +void CUDART_CB postprocess(void *arg); + +// Worker thread: CPU pre-processing + full GPU pipeline enqueue. +// +// std::thread passes arguments with their actual types, so no void* casting +// is needed. This thread fills the input buffer, then enqueues the full GPU +// pipeline (H2D, kernel, D2H, host func) onto the workload's stream. +void preprocess(Workload *workload) { - heterogeneous_workload *workload = (heterogeneous_workload *)void_arg; - - // Select GPU for this CPU thread - checkCudaErrors(cudaSetDevice(workload->cudaDeviceID)); - - // Allocate Resources - checkCudaErrors(cudaStreamCreate(&workload->stream)); - checkCudaErrors(cudaMalloc(&workload->d_data, N_elements_per_workload * sizeof(int))); - checkCudaErrors(cudaHostAlloc(&workload->h_data, N_elements_per_workload * sizeof(int), cudaHostAllocPortable)); - - // CPU thread generates data - for (int i = 0; i < N_elements_per_workload; ++i) { + // Stage 1: CPU pre-processing ---------------------------------- + printf("[thread] Stage 1: CPU pre-processing on a worker thread...\n"); + for (int i = 0; i < NumElements; ++i) { workload->h_data[i] = workload->id + i; } + printf("[thread] Filled %d elements. First 3 inputs: %d, %d, %d\n\n", + NumElements, workload->h_data[0], workload->h_data[1], workload->h_data[2]); - // Schedule work for GPU in CUDA stream without blocking the CPU thread - // Note: Dedicated streams enable concurrent execution of workloads on the GPU - dim3 block(512); - dim3 grid((N_elements_per_workload + block.x - 1) / block.x); + // Stage 2: enqueue GPU work from this thread ------------------- + const size_t bytes = NumElements * sizeof(int); + const int threadsPerBlock = 256; + const int blocks = (NumElements + threadsPerBlock - 1) / threadsPerBlock; - checkCudaErrors(cudaMemcpyAsync(workload->d_data, - workload->h_data, - N_elements_per_workload * sizeof(int), - cudaMemcpyHostToDevice, - workload->stream)); - incKernel<<stream>>>(workload->d_data, N_elements_per_workload); - checkCudaErrors(cudaMemcpyAsync(workload->h_data, - workload->d_data, - N_elements_per_workload * sizeof(int), - cudaMemcpyDeviceToHost, - workload->stream)); + printf("[thread] Stage 2: enqueuing H2D copy, kernel, D2H copy on the stream\n"); + cudaMemcpyAsync(workload->d_data, workload->h_data, bytes, cudaMemcpyHostToDevice, workload->stream); + incrementKernel<<stream>>>(workload->d_data, NumElements); + cudaMemcpyAsync(workload->h_data, workload->d_data, bytes, cudaMemcpyDeviceToHost, workload->stream); - // New in CUDA 5.0: Add a CPU callback which is called once all currently - // pending operations in the CUDA stream have finished - checkCudaErrors(cudaStreamAddCallback(workload->stream, myStreamCallback, workload, 0)); - - CUT_THREADEND; - // CPU thread end of life, GPU continues to process data... + // Stage 3: schedule CPU post-processing on the stream ----------- + printf("[thread] Registering post-processing callback with cudaLaunchHostFunc\n\n"); + cudaLaunchHostFunc(workload->stream, postprocess, workload); } -CUT_THREADPROC postprocess(void *void_arg) +// Stage 3: CPU post-processing, scheduled on the stream via cudaLaunchHostFunc. +// +// CUDA invokes this automatically once the H2D copy, the kernel, and the D2H +// copy that precede it on the stream have all finished. Every element should +// now equal its original value plus one (from the kernel). +// +// Reminder: DO NOT call any CUDA API from inside this function. +void CUDART_CB postprocess(void *arg) { - heterogeneous_workload *workload = (heterogeneous_workload *)void_arg; - // ... GPU is done with processing, continue on new CPU thread... + // recover the typed pointer from the callback's void* arg + auto *workload = static_cast(arg); - // Select GPU for this CPU thread - checkCudaErrors(cudaSetDevice(workload->cudaDeviceID)); + // Stage 3: CPU post-processing — runs automatically when all stream work above is done + printf("[host func] Stage 3: callback fired automatically - the GPU work is done!\n"); + printf("[host func] First 3 results: %d, %d, %d (each input +1)\n", + workload->h_data[0], workload->h_data[1], workload->h_data[2]); - // CPU thread consumes results from GPU - workload->success = true; - - for (int i = 0; i < N_workloads; ++i) { - workload->success &= workload->h_data[i] == i + workload->id + 1; - } - - // Free Resources - checkCudaErrors(cudaFree(workload->d_data)); - checkCudaErrors(cudaFreeHost(workload->h_data)); - checkCudaErrors(cudaStreamDestroy(workload->stream)); - - // Signal the end of the heterogeneous workload to main thread - cutIncrementBarrier(&thread_barrier); - - CUT_THREADEND; -} - -void CUDART_CB myStreamCallback(cudaStream_t stream, cudaError_t status, void *data) -{ - // Check status of GPU after stream operations are done - checkCudaErrors(status); - - // Spawn new CPU worker thread and continue processing on the CPU - cutStartThread(postprocess, data); -} - -int main(int argc, char **argv) -{ - int N_gpus, max_gpus = 0; - int gpuInfo[32]; // assume a maximum of 32 GPUs in a system configuration - - printf("Starting simpleCallback\n"); - - checkCudaErrors(cudaGetDeviceCount(&N_gpus)); - printf("Found %d CUDA capable GPUs\n", N_gpus); - - if (N_gpus > 32) { - printf("simpleCallback only supports 32 GPU(s)\n"); - } - - for (int devid = 0; devid < N_gpus; devid++) { - int SMversion; - cudaDeviceProp deviceProp; - cudaSetDevice(devid); - cudaGetDeviceProperties(&deviceProp, devid); - SMversion = deviceProp.major << 4 + deviceProp.minor; - printf("GPU[%d] %s supports SM %d.%d", devid, deviceProp.name, deviceProp.major, deviceProp.minor); - printf(", %s GPU Callback Functions\n", (SMversion >= 0x11) ? "capable" : "NOT capable"); - - if (SMversion >= 0x11) { - gpuInfo[max_gpus++] = devid; + bool verify = true; + for (int i = 0; i < NumElements; ++i) { + if (workload->h_data[i] != workload->id + i + 1) { + verify = false; + break; } } - - printf("%d GPUs available to run Callback Functions\n", max_gpus); - - heterogeneous_workload *workloads; - workloads = (heterogeneous_workload *)malloc(N_workloads * sizeof(heterogeneous_workload)); - ; - thread_barrier = cutCreateBarrier(N_workloads); - - // Main thread spawns a CPU worker thread for each heterogeneous workload - printf("Starting %d heterogeneous computing workloads\n", N_workloads); - - for (int i = 0; i < N_workloads; ++i) { - workloads[i].id = i; - workloads[i].cudaDeviceID = gpuInfo[i % max_gpus]; // i % N_gpus; - - cutStartThread(launch, &workloads[i]); - } - - // Sleep until all workloads have finished - cutWaitForBarrier(&thread_barrier); - printf("Total of %d workloads finished:\n", N_workloads); - - bool success = true; - - for (int i = 0; i < N_workloads; ++i) { - success &= workloads[i].success; - } - - printf("%s\n", success ? "Success" : "Failure"); - - free(workloads); - - exit(success ? EXIT_SUCCESS : EXIT_FAILURE); + workload->success = verify; + printf("[host func] Verified all %d results: %s\n", NumElements, verify ? "PASS" : "MISMATCH"); +} + +int main() +{ + printf("=====================================================\n"); + printf(" simpleCallback: CPU -> GPU -> CPU pipeline demo\n"); + printf("=====================================================\n"); + + // Use the first available CUDA device. + int devID = 0; + cudaSetDevice(devID); + + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); + printf("Using GPU %d: compute capability %d.%d, %d SMs\n", devID, major, minor, smCount); + + // Allocate resources + Workload workload; + workload.id = 42; // Any number can be used + + // Pinned (page-locked) host memory enables true asynchronous copies. + const size_t bytes = NumElements * sizeof(int); + cudaMallocHost(&workload.h_data, bytes); + cudaMalloc(&workload.d_data, bytes); + cudaStreamCreate(&workload.stream); + + // Launch worker thread; it fills the buffer AND enqueues the full GPU pipeline. + std::thread worker(preprocess, &workload); + worker.join(); + printf("[main] Worker thread joined; GPU work has been enqueued.\n"); + + // Block until the whole pipeline (including the host function) is complete. + printf("[main] Waiting for the stream (and callback) to finish...\n\n"); + cudaStreamSynchronize(workload.stream); + + // Clean up + // Safe to call CUDA APIs here: we are back on the main thread, not inside + // the host-function callback. + cudaStreamDestroy(workload.stream); + cudaFree(workload.d_data); + cudaFreeHost(workload.h_data); + + printf("\n[main] Pipeline complete. Result: %s\n", workload.success ? "SUCCESS" : "FAILURE"); + return workload.success ? EXIT_SUCCESS : EXIT_FAILURE; } diff --git a/cpp/0_Introduction/simpleCooperativeGroups/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleCooperativeGroups/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleCooperativeGroups/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleCooperativeGroups/.vscode/extensions.json b/cpp/0_Introduction/simpleCooperativeGroups/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleCooperativeGroups/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleCooperativeGroups/CMakeLists.txt b/cpp/0_Introduction/simpleCooperativeGroups/CMakeLists.txt index cd0659f0..8fb2c01e 100644 --- a/cpp/0_Introduction/simpleCooperativeGroups/CMakeLists.txt +++ b/cpp/0_Introduction/simpleCooperativeGroups/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleCooperativeGroups LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleCooperativeGroups add_executable(simpleCooperativeGroups simpleCooperativeGroups.cu) target_compile_options(simpleCooperativeGroups PRIVATE $<$:--extended-lambda>) target_compile_features(simpleCooperativeGroups PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleCooperativeGroups PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleCubemapTexture/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleCubemapTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleCubemapTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleCubemapTexture/.vscode/extensions.json b/cpp/0_Introduction/simpleCubemapTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleCubemapTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleCubemapTexture/CMakeLists.txt b/cpp/0_Introduction/simpleCubemapTexture/CMakeLists.txt index 2ec278b2..0d1381de 100644 --- a/cpp/0_Introduction/simpleCubemapTexture/CMakeLists.txt +++ b/cpp/0_Introduction/simpleCubemapTexture/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleCubemapTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleCubemapTexture add_executable(simpleCubemapTexture simpleCubemapTexture.cu) target_compile_options(simpleCubemapTexture PRIVATE $<$:--extended-lambda>) target_compile_features(simpleCubemapTexture PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleCubemapTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleDrvRuntime/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleDrvRuntime/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleDrvRuntime/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleDrvRuntime/.vscode/extensions.json b/cpp/0_Introduction/simpleDrvRuntime/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleDrvRuntime/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleDrvRuntime/CMakeLists.txt b/cpp/0_Introduction/simpleDrvRuntime/CMakeLists.txt index a05e5b60..51568ebd 100644 --- a/cpp/0_Introduction/simpleDrvRuntime/CMakeLists.txt +++ b/cpp/0_Introduction/simpleDrvRuntime/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleDrvRuntime LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleDrvRuntime add_executable(simpleDrvRuntime simpleDrvRuntime.cpp) target_compile_options(simpleDrvRuntime PRIVATE $<$:--extended-lambda>) target_compile_features(simpleDrvRuntime PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleDrvRuntime PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleDrvRuntime PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -58,7 +50,7 @@ endif() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -69,6 +61,5 @@ add_custom_target(generate_fatbin_simpleDrv ALL DEPENDS ${CUDA_FATBIN_FILE}) # Ensure simpleDrvRuntime depends on the fatbin add_dependencies(simpleDrvRuntime generate_fatbin_simpleDrv) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleHyperQ/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleHyperQ/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleHyperQ/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleHyperQ/.vscode/extensions.json b/cpp/0_Introduction/simpleHyperQ/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleHyperQ/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleHyperQ/CMakeLists.txt b/cpp/0_Introduction/simpleHyperQ/CMakeLists.txt deleted file mode 100644 index 160d058f..00000000 --- a/cpp/0_Introduction/simpleHyperQ/CMakeLists.txt +++ /dev/null @@ -1,34 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleHyperQ LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleHyperQ -add_executable(simpleHyperQ simpleHyperQ.cu) - -target_compile_options(simpleHyperQ PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleHyperQ PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleHyperQ PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleHyperQ/README.md b/cpp/0_Introduction/simpleHyperQ/README.md deleted file mode 100644 index 18c92b17..00000000 --- a/cpp/0_Introduction/simpleHyperQ/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# simpleHyperQ - simpleHyperQ - -## Description - -This sample demonstrates the use of CUDA streams for concurrent execution of several kernels on devices which provide HyperQ (SM 3.5). Devices without HyperQ (SM 2.0 and SM 3.0) will run a maximum of two kernels concurrently. - -## Key Concepts - -CUDA Systems Integration, Performance Strategies - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaStreamDestroy, cudaMalloc, cudaFree, cudaMallocHost, cudaEventSynchronize, cudaEventRecord, cudaFreeHost, cudaGetDevice, cudaEventDestroy, cudaEventElapsedTime, cudaStreamCreate, cudaGetDeviceProperties, cudaEventCreate - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) - -[whitepaper](./doc/HyperQ.pdf) diff --git a/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.docx b/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.docx deleted file mode 100644 index 6733286e..00000000 Binary files a/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.docx and /dev/null differ diff --git a/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.pdf b/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.pdf deleted file mode 100644 index 1b7d53a5..00000000 Binary files a/cpp/0_Introduction/simpleHyperQ/doc/HyperQ.pdf and /dev/null differ diff --git a/cpp/0_Introduction/simpleHyperQ/simpleHyperQ.cu b/cpp/0_Introduction/simpleHyperQ/simpleHyperQ.cu deleted file mode 100644 index 2a1ac958..00000000 --- a/cpp/0_Introduction/simpleHyperQ/simpleHyperQ.cu +++ /dev/null @@ -1,235 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// -// This sample demonstrates how HyperQ allows supporting devices to avoid false -// dependencies between kernels in different streams. -// -// - Devices without HyperQ will run a maximum of two kernels at a time (one -// kernel_A and one kernel_B). -// - Devices with HyperQ will run up to 32 kernels simultaneously. - -#include -#include - -namespace cg = cooperative_groups; -#include -#include - -const char *sSDKsample = "hyperQ"; - -// This subroutine does no real work but runs for at least the specified number -// of clock ticks. -__device__ void clock_block(clock_t *d_o, clock_t clock_count) -{ - unsigned int start_clock = (unsigned int)clock(); - - clock_t clock_offset = 0; - - while (clock_offset < clock_count) { - unsigned int end_clock = (unsigned int)clock(); - - // The code below should work like - // this (thanks to modular arithmetics): - // - // clock_offset = (clock_t) (end_clock > start_clock ? - // end_clock - start_clock : - // end_clock + (0xffffffffu - start_clock)); - // - // Indeed, let m = 2^32 then - // end - start = end + m - start (mod m). - - clock_offset = (clock_t)(end_clock - start_clock); - } - - d_o[0] = clock_offset; -} - -// We create two identical kernels calling clock_block(), we create two so that -// we can identify dependencies in the profile timeline ("kernel_B" is always -// dependent on "kernel_A" in the same stream). -__global__ void kernel_A(clock_t *d_o, clock_t clock_count) { clock_block(d_o, clock_count); } -__global__ void kernel_B(clock_t *d_o, clock_t clock_count) { clock_block(d_o, clock_count); } - -// Single-warp reduction kernel (note: this is not optimized for simplicity) -__global__ void sum(clock_t *d_clocks, int N) -{ - // Handle to thread block group - cg::thread_block cta = cg::this_thread_block(); - __shared__ clock_t s_clocks[32]; - - clock_t my_sum = 0; - - for (int i = threadIdx.x; i < N; i += blockDim.x) { - my_sum += d_clocks[i]; - } - - s_clocks[threadIdx.x] = my_sum; - cg::sync(cta); - - for (int i = warpSize / 2; i > 0; i /= 2) { - if (threadIdx.x < i) { - s_clocks[threadIdx.x] += s_clocks[threadIdx.x + i]; - } - - cg::sync(cta); - } - - if (threadIdx.x == 0) { - d_clocks[0] = s_clocks[0]; - } -} - -int main(int argc, char **argv) -{ - int nstreams = 32; // One stream for each pair of kernels - float kernel_time = 10; // Time each kernel should run in ms - float elapsed_time; - int cuda_device = 0; - - printf("starting %s...\n", sSDKsample); - - // Get number of streams (if overridden on the command line) - if (checkCmdLineFlag(argc, (const char **)argv, "nstreams")) { - nstreams = getCmdLineArgumentInt(argc, (const char **)argv, "nstreams"); - } - - // Use command-line specified CUDA device, otherwise use device with - // highest Gflops/s - cuda_device = findCudaDevice(argc, (const char **)argv); - - // Get device properties - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDevice(&cuda_device)); - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, cuda_device)); - - // Get device clock rate - int clockRate; - checkCudaErrors(cudaDeviceGetAttribute(&clockRate, cudaDevAttrClockRate, cuda_device)); - - // HyperQ is available in devices of Compute Capability 3.5 and higher - if (deviceProp.major < 3 || (deviceProp.major == 3 && deviceProp.minor < 5)) { - if (deviceProp.concurrentKernels == 0) { - printf("> GPU does not support concurrent kernel execution (SM 3.5 or " - "higher required)\n"); - printf(" CUDA kernel runs will be serialized\n"); - } - else { - printf("> GPU does not support HyperQ\n"); - printf(" CUDA kernel runs will have limited concurrency\n"); - } - } - - printf("> Detected Compute SM %d.%d hardware with %d multi-processors\n", - deviceProp.major, - deviceProp.minor, - deviceProp.multiProcessorCount); - - // Allocate host memory for the output (reduced to a single value) - clock_t *a = 0; - checkCudaErrors(cudaMallocHost((void **)&a, sizeof(clock_t))); - - // Allocate device memory for the output (one value for each kernel) - clock_t *d_a = 0; - checkCudaErrors(cudaMalloc((void **)&d_a, 2 * nstreams * sizeof(clock_t))); - - // Allocate and initialize an array of stream handles - cudaStream_t *streams = (cudaStream_t *)malloc(nstreams * sizeof(cudaStream_t)); - - for (int i = 0; i < nstreams; i++) { - checkCudaErrors(cudaStreamCreate(&(streams[i]))); - } - - // Create CUDA event handles - cudaEvent_t start_event, stop_event; - checkCudaErrors(cudaEventCreate(&start_event)); - checkCudaErrors(cudaEventCreate(&stop_event)); - - // Target time per kernel is kernel_time ms, clockRate is in KHz - // Target number of clocks = target time * clock frequency -#if defined(__arm__) || defined(__aarch64__) - // the kernel takes more time than the channel reset time on arm archs, so to - // prevent hangs reduce time_clocks. - clock_t time_clocks = (clock_t)(kernel_time * (clockRate / 100)); -#else - clock_t time_clocks = (clock_t)(kernel_time * clockRate); -#endif - clock_t total_clocks = 0; - - // Start the clock - checkCudaErrors(cudaEventRecord(start_event, 0)); - - // Queue pairs of {kernel_A, kernel_B} in separate streams - for (int i = 0; i < nstreams; ++i) { - kernel_A<<<1, 1, 0, streams[i]>>>(&d_a[2 * i], time_clocks); - total_clocks += time_clocks; - kernel_B<<<1, 1, 0, streams[i]>>>(&d_a[2 * i + 1], time_clocks); - total_clocks += time_clocks; - } - - // Stop the clock in stream 0 (i.e. all previous kernels will be complete) - checkCudaErrors(cudaEventRecord(stop_event, 0)); - - // At this point the CPU has dispatched all work for the GPU and can - // continue processing other tasks in parallel. In this sample we just want - // to wait until all work is done so we use a blocking cudaMemcpy below. - - // Run the sum kernel and copy the result back to host - sum<<<1, 32>>>(d_a, 2 * nstreams); - checkCudaErrors(cudaMemcpy(a, d_a, sizeof(clock_t), cudaMemcpyDeviceToHost)); - - // stop_event will have been recorded but including the synchronize here to - // prevent copy/paste errors! - checkCudaErrors(cudaEventSynchronize(stop_event)); - checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event)); - - printf("Expected time for serial execution of %d sets of kernels is between " - "approx. %.3fs and %.3fs\n", - nstreams, - (nstreams + 1) * kernel_time / 1000.0f, - 2 * nstreams * kernel_time / 1000.0f); - printf("Expected time for fully concurrent execution of %d sets of kernels is " - "approx. %.3fs\n", - nstreams, - 2 * kernel_time / 1000.0f); - printf("Measured time for sample = %.3fs\n", elapsed_time / 1000.0f); - - bool bTestResult = (a[0] >= total_clocks); - - // Release resources - for (int i = 0; i < nstreams; i++) { - cudaStreamDestroy(streams[i]); - } - - free(streams); - cudaEventDestroy(start_event); - cudaEventDestroy(stop_event); - cudaFreeHost(a); - cudaFree(d_a); - - exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/0_Introduction/simpleIPC/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleIPC/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleIPC/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleIPC/.vscode/extensions.json b/cpp/0_Introduction/simpleIPC/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleIPC/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleIPC/CMakeLists.txt b/cpp/0_Introduction/simpleIPC/CMakeLists.txt index 2e88a7e0..a3785bb0 100644 --- a/cpp/0_Introduction/simpleIPC/CMakeLists.txt +++ b/cpp/0_Introduction/simpleIPC/CMakeLists.txt @@ -1,38 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleIPC LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample simpleIPC - not supported on aarch64") else() - # Source file - # Add target for simpleIPC add_executable(simpleIPC simpleIPC.cu ../../../Common/helper_multiprocess.cpp) target_compile_options(simpleIPC PRIVATE $<$:--extended-lambda>) target_compile_features(simpleIPC PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(simpleIPC PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleLayeredTexture/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleLayeredTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleLayeredTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleLayeredTexture/.vscode/extensions.json b/cpp/0_Introduction/simpleLayeredTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleLayeredTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleLayeredTexture/CMakeLists.txt b/cpp/0_Introduction/simpleLayeredTexture/CMakeLists.txt index 86ad2817..03cfe202 100644 --- a/cpp/0_Introduction/simpleLayeredTexture/CMakeLists.txt +++ b/cpp/0_Introduction/simpleLayeredTexture/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleLayeredTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleLayeredTexture add_executable(simpleLayeredTexture simpleLayeredTexture.cu) target_compile_options(simpleLayeredTexture PRIVATE $<$:--extended-lambda>) target_compile_features(simpleLayeredTexture PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleLayeredTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleMPI/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleMPI/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleMPI/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleMPI/.vscode/extensions.json b/cpp/0_Introduction/simpleMPI/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleMPI/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleMPI/CMakeLists.txt b/cpp/0_Introduction/simpleMPI/CMakeLists.txt index 77b78c10..48b88ea8 100644 --- a/cpp/0_Introduction/simpleMPI/CMakeLists.txt +++ b/cpp/0_Introduction/simpleMPI/CMakeLists.txt @@ -1,50 +1,35 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleMPI LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(MPI) -# Source file if(${MPI_FOUND}) - # Add target for simpleMPI add_executable(simpleMPI simpleMPI.cpp simpleMPI.cu) - -target_compile_options(simpleMPI PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleMPI PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleMPI PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(simpleMPI PRIVATE $<$:--extended-lambda>) + target_compile_features(simpleMPI PRIVATE cxx_std_17 cuda_std_17) target_include_directories(simpleMPI PUBLIC ${MPI_INCLUDE_PATH} ) - target_link_libraries(simpleMPI PUBLIC ${MPI_C_LIBRARIES} ${MPI_CXX_LIBRARIES} ) - + include(InstallSamples) + setup_samples_install() else() message(STATUS "MPI not found - will not build sample 'simpleMPI'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleMultiCopy/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleMultiCopy/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleMultiCopy/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleMultiCopy/.vscode/extensions.json b/cpp/0_Introduction/simpleMultiCopy/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleMultiCopy/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleMultiCopy/CMakeLists.txt b/cpp/0_Introduction/simpleMultiCopy/CMakeLists.txt index 1e2d809e..e6d06df6 100644 --- a/cpp/0_Introduction/simpleMultiCopy/CMakeLists.txt +++ b/cpp/0_Introduction/simpleMultiCopy/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleMultiCopy LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleMultiCopy add_executable(simpleMultiCopy simpleMultiCopy.cu) target_compile_options(simpleMultiCopy PRIVATE $<$:--extended-lambda>) target_compile_features(simpleMultiCopy PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleMultiCopy PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleMultiGPU/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleMultiGPU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleMultiGPU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleMultiGPU/.vscode/extensions.json b/cpp/0_Introduction/simpleMultiGPU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleMultiGPU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleMultiGPU/CMakeLists.txt b/cpp/0_Introduction/simpleMultiGPU/CMakeLists.txt index e88e0c29..6533aadd 100644 --- a/cpp/0_Introduction/simpleMultiGPU/CMakeLists.txt +++ b/cpp/0_Introduction/simpleMultiGPU/CMakeLists.txt @@ -1,34 +1,21 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleMultiGPU LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleMultiGPU add_executable(simpleMultiGPU simpleMultiGPU.cu) -target_compile_options(simpleMultiGPU PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleMultiGPU PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleMultiGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleMultiGPU/README.md b/cpp/0_Introduction/simpleMultiGPU/README.md index 105fe014..ffb595a6 100644 --- a/cpp/0_Introduction/simpleMultiGPU/README.md +++ b/cpp/0_Introduction/simpleMultiGPU/README.md @@ -1,32 +1,130 @@ -# simpleMultiGPU - Simple Multi-GPU +# Sample: simpleMultiGPU ## Description -This application demonstrates how to use the new CUDA 4.0 API for CUDA context management and multi-threaded access to run CUDA kernels on multiple-GPUs. +A multi-GPU reduction demo: the input vector is split across every GPU in the system, each +GPU sums its slice concurrently, and the host combines the per-GPU results. Each GPU gets its +own CUDA stream, so the H2D copy → reduction kernel → D2H copy pipelines run in parallel across +devices. The device-side reduction uses `cub::BlockReduce`; the host finishes the sum across +blocks and across GPUs and checks it against a CPU reference. The whole GPU phase is timed with +CUDA events. + +## What You'll Learn + +- Enumerating and driving multiple GPUs with `cudaGetDeviceCount` and `cudaSetDevice` +- Partitioning a workload across GPUs and running them concurrently, one CUDA stream per GPU +- Overlapping H2D copy, kernel, and D2H copy with `cudaMemcpyAsync` on independent streams +- Reducing within a block using `cub::BlockReduce`, then finishing the reduction on the host +- Timing GPU work with CUDA events (`cudaEventRecord` / `cudaEventElapsedTime`) +- Why pinned host memory (`cudaMallocHost`) is required for `cudaMemcpyAsync` to be asynchronous ## Key Concepts -Asynchronous Data Transfers, CUDA Streams and Events, Multithreading, Multi-GPU +### One Stream per GPU -## Supported SM Architectures +The work is held in a `std::vector` with one entry per device, each carrying its own stream, +buffers, and data slice. Selecting a device with `cudaSetDevice` and issuing async work on that +device's stream lets all GPUs run at the same time: -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +```text +GPU 0 stream: [H2D slice 0] → [reduceKernel] → [D2H partials 0] +GPU 1 stream: [H2D slice 1] → [reduceKernel] → [D2H partials 1] +... +``` -## Supported OSes +The host launches every GPU's pipeline before synchronizing any of them, so the copies and kernels +overlap across devices. -Linux, Windows +### Multi-Level Reduction -## Supported CPU Architecture +The reduction happens in three stages. On each GPU, `reduceKernel` launches `BLOCK_N` blocks of `THREAD_N` +threads: every thread accumulates a grid-strided partial sum, then `cub::BlockReduce` combines the +per-thread partials within its block and thread 0 writes one value per block. The host then adds the +`BLOCK_N` per-block partials into that GPU's sum, and finally adds the per-GPU sums together. -x86_64, armv7l +### Pinned Host Memory -## CUDA APIs involved +`cudaMemcpyAsync` only runs asynchronously when the host buffer is page-locked (pinned), so the OS +cannot move the pages mid-transfer; with ordinary pageable memory the copy falls back to synchronous +behavior. Both the input slice and the partial-sum buffer are allocated with `cudaMallocHost`. -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaStreamDestroy, cudaFree, cudaMallocHost, cudaSetDevice, cudaFreeHost, cudaStreamSynchronize, cudaMalloc, cudaMemcpyAsync, cudaStreamCreate, cudaGetDeviceCount +### Timing with CUDA Events -## Prerequisites +CUDA events are recorded on device 0's stream to bracket the GPU phase. `cudaEventElapsedTime` returns +the milliseconds between the recorded events. -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +## Key APIs -## References (for more details) +### CUDA Runtime + +- `cudaGetDeviceCount` — count the CUDA-capable GPUs in the system +- `cudaSetDevice` — select the active GPU for subsequent CUDA calls +- `cudaStreamCreate` / `cudaStreamDestroy` — per-GPU stream that orders each pipeline +- `cudaMalloc` / `cudaFree` — allocate and free device memory +- `cudaMallocHost` / `cudaFreeHost` — allocate and free pinned host memory +- `cudaMemcpyAsync` — non-blocking H2D and D2H transfers on a stream +- `cudaStreamSynchronize` — block the host until a GPU's stream is done +- `cudaEventCreate` / `cudaEventRecord` / `cudaEventSynchronize` / `cudaEventElapsedTime` / `cudaEventDestroy` — event-based timing + +### CCCL / CUB + +- `cub::BlockReduce` — block-wide reduction inside the kernel + +## Requirements + +### Hardware + +- Two or more NVIDIA GPUs with Compute Capability 7.5 or higher + +### Software + +- CMake 3.20 or newer +- A C++17-capable host compiler + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +```bash +./simpleMultiGPU +``` + +No command-line arguments are required. The sample uses every GPU it detects, and exits without +running if it finds fewer than two. + +## Expected Output + +```text +Starting simpleMultiGPU +CUDA-capable device count: 2 +Generating input data... + +Computing with 2 GPUs... + GPU Processing time: 8.138752 (ms) + +Computing with Host CPU... + +Comparing GPU and Host CPU results... + GPU sum: 16777294.000000 + CPU sum: 16777294.395033 + Relative difference: 2.354566E-08 +``` + +**Reading the output:** +- The device count and processing time depend on your system +- The sums are the same every run because the input is filled from an unseeded `rand()` +- The sample passes when the GPU and CPU results agree to within a relative difference of `1e-5` + +## Files + +- `simpleMultiGPU.cu` — reduction kernel and the multi-GPU driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA Programming Guide — CUDA Streams](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#cuda-streams) +- [CUDA Programming Guide — CUDA Events](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#cuda-events) +- [CUDA Core Compute Libraries — What is CUB?](https://nvidia.github.io/cccl/unstable/cub/index.html#what-is-cub) diff --git a/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.cu b/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.cu index 94324151..bd5a8137 100644 --- a/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.cu +++ b/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -26,46 +26,54 @@ */ /* - * This application demonstrates how to use the CUDA API to use multiple GPUs, - * with an emphasis on simple illustration of the techniques (not on - * performance). + * Sums a large vector across every GPU in the system. * - * Note that in order to detect multiple GPUs in your system you have to disable - * SLI in the nvidia control panel. Otherwise only one GPU is visible to the - * application. On the other side, you can still extend your desktop to screens - * attached to both GPUs. + * The input is split into one slice per GPU, each with its own stream, so the H2D copy, + * reduction kernel, and D2H copy run concurrently across devices. cub::BlockReduce reduces + * within each thread block; the host then adds the per-block values into a per-GPU total and + * combines those into the final sum, checked against a CPU reference. CUDA events time the + * GPU phase. */ // System includes -#include +#include #include +#include +#include // CUDA runtime #include -// helper functions and utilities to work with CUDA -#include -#include +// CCCL / CUB block-wide reduction +#include -#ifndef MAX -#define MAX(a, b) (a > b ? a : b) -#endif - -#include "simpleMultiGPU.h" - -//////////////////////////////////////////////////////////////////////////////// -// Data configuration -//////////////////////////////////////////////////////////////////////////////// -const int MAX_GPU_COUNT = 32; -const int DATA_N = 1048576 * 32; - -//////////////////////////////////////////////////////////////////////////////// -// Simple reduction kernel. -// Refer to the 'reduction' CUDA Sample describing -// reduction optimization strategies -//////////////////////////////////////////////////////////////////////////////// -__global__ static void reduceKernel(float *d_Result, float *d_Input, int N) +// Everything one GPU needs: its slice of the input, its buffers, and its stream +struct TGPUplan { + int dataN; // elements assigned to this GPU + float *h_Data; // pinned input slice + float *d_Data; // the slice, on the device + float *d_Sum; // one partial sum per block + float *h_Sum_from_device; // those partial sums, copied back + cudaStream_t stream; // orders this GPU's copies and kernel +}; + +// Data configuration +constexpr int DATA_N = 1048576 * 32; + +// Reduction launch configuration +constexpr int BLOCK_N = 32; // thread blocks launched per GPU +constexpr int THREAD_N = 256; // threads per block + +// Per-block reduction kernel. +// Threads walk the input in a grid-stride loop, then cub::BlockReduce combines their totals. +// Thread 0 writes one partial sum per block (BLOCK_N values total); the host +// adds those up to obtain this GPU's final sum. +__global__ static void reduceKernel(float *d_Result, const float *d_Input, int N) +{ + using BlockReduce = cub::BlockReduce; + __shared__ typename BlockReduce::TempStorage temp_storage; + const int tid = blockIdx.x * blockDim.x + threadIdx.x; const int threadN = gridDim.x * blockDim.x; float sum = 0; @@ -73,160 +81,145 @@ __global__ static void reduceKernel(float *d_Result, float *d_Input, int N) for (int pos = tid; pos < N; pos += threadN) sum += d_Input[pos]; - d_Result[tid] = sum; + // Reduce the per-thread partial sums across the block (result valid in thread 0) + sum = BlockReduce(temp_storage).Sum(sum); + + if (threadIdx.x == 0) + d_Result[blockIdx.x] = sum; } -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) +int main() { - // Solver config - TGPUplan plan[MAX_GPU_COUNT]; - - // GPU reduction results - float h_SumGPU[MAX_GPU_COUNT]; - - float sumGPU; - double sumCPU, diff; - - int i, j, gpuBase, GPU_N; - - const int BLOCK_N = 32; - const int THREAD_N = 256; - const int ACCUM_N = BLOCK_N * THREAD_N; - printf("Starting simpleMultiGPU\n"); - checkCudaErrors(cudaGetDeviceCount(&GPU_N)); - if (GPU_N > MAX_GPU_COUNT) { - GPU_N = MAX_GPU_COUNT; + int deviceCount = 0; + cudaGetDeviceCount(&deviceCount); + + printf("CUDA-capable device count: %i\n", deviceCount); + + // This sample needs at least two GPUs to show work running on several devices at once + if (deviceCount < 2) { + printf("Two or more CUDA-capable devices are required. Exiting.\n"); + return EXIT_SUCCESS; } - printf("CUDA-capable device count: %i\n", GPU_N); - printf("Generating input data...\n\n"); + std::vector plan(deviceCount); + std::vector h_SumGPU(deviceCount); // one result per GPU + // Subdividing input data across GPUs // Get data sizes for each GPU - for (i = 0; i < GPU_N; i++) { - plan[i].dataN = DATA_N / GPU_N; + for (int i = 0; i < deviceCount; i++) { + plan[i].dataN = DATA_N / deviceCount; } // Take into account "odd" data sizes - for (i = 0; i < DATA_N % GPU_N; i++) { + for (int i = 0; i < DATA_N % deviceCount; i++) { plan[i].dataN++; } - // Assign data ranges to GPUs - gpuBase = 0; - - for (i = 0; i < GPU_N; i++) { - plan[i].h_Sum = h_SumGPU + i; - gpuBase += plan[i].dataN; - } - // Create streams for issuing GPU command asynchronously and allocate memory // (GPU and System page-locked) - for (i = 0; i < GPU_N; i++) { - checkCudaErrors(cudaSetDevice(i)); - checkCudaErrors(cudaStreamCreate(&plan[i].stream)); - // Allocate memory - checkCudaErrors(cudaMalloc((void **)&plan[i].d_Data, plan[i].dataN * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&plan[i].d_Sum, ACCUM_N * sizeof(float))); - checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Sum_from_device, ACCUM_N * sizeof(float))); - checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Data, plan[i].dataN * sizeof(float))); + for (int i = 0; i < deviceCount; i++) { + cudaSetDevice(i); + cudaStreamCreate(&plan[i].stream); - for (j = 0; j < plan[i].dataN; j++) { + cudaMalloc(&plan[i].d_Data, plan[i].dataN * sizeof(float)); + cudaMalloc(&plan[i].d_Sum, BLOCK_N * sizeof(float)); + cudaMallocHost(&plan[i].h_Sum_from_device, BLOCK_N * sizeof(float)); + cudaMallocHost(&plan[i].h_Data, plan[i].dataN * sizeof(float)); + + for (int j = 0; j < plan[i].dataN; j++) { plan[i].h_Data[j] = (float)rand() / (float)RAND_MAX; } } // Start timing and compute on GPU(s) - printf("Computing with %d GPUs...\n", GPU_N); - // create and start timer - StopWatchInterface *timer = NULL; - sdkCreateTimer(&timer); + printf("Computing with %d GPUs...\n", deviceCount); + // Time the multi-GPU work with CUDA events (recorded on device 0) + cudaEvent_t start, stop; + cudaSetDevice(0); + cudaEventCreate(&start); + cudaEventCreate(&stop); + cudaEventRecord(start); - // start the timer - sdkStartTimer(&timer); - - // Copy data to GPU, launch the kernel and copy data back. All asynchronously - for (i = 0; i < GPU_N; i++) { - // Set device - checkCudaErrors(cudaSetDevice(i)); + // Queue every GPU's copy-in, kernel and copy-out before waiting on any of them, + // so the devices run concurrently instead of one after another + for (int i = 0; i < deviceCount; i++) { + TGPUplan &gpu = plan[i]; + cudaSetDevice(i); // Copy input data from CPU - checkCudaErrors(cudaMemcpyAsync( - plan[i].d_Data, plan[i].h_Data, plan[i].dataN * sizeof(float), cudaMemcpyHostToDevice, plan[i].stream)); + cudaMemcpyAsync(gpu.d_Data, gpu.h_Data, gpu.dataN * sizeof(float), cudaMemcpyHostToDevice, gpu.stream); - // Perform GPU computations - reduceKernel<<>>(plan[i].d_Sum, plan[i].d_Data, plan[i].dataN); - getLastCudaError("reduceKernel() execution failed.\n"); + // Launch Kernel + reduceKernel<<>>(gpu.d_Sum, gpu.d_Data, gpu.dataN); // Read back GPU results - checkCudaErrors(cudaMemcpyAsync( - plan[i].h_Sum_from_device, plan[i].d_Sum, ACCUM_N * sizeof(float), cudaMemcpyDeviceToHost, plan[i].stream)); + cudaMemcpyAsync(gpu.h_Sum_from_device, gpu.d_Sum, BLOCK_N * sizeof(float), cudaMemcpyDeviceToHost, gpu.stream); } // Process GPU results - for (i = 0; i < GPU_N; i++) { - float sum; - - // Set device - checkCudaErrors(cudaSetDevice(i)); + for (int i = 0; i < deviceCount; i++) { + cudaSetDevice(i); // Wait for all operations to finish cudaStreamSynchronize(plan[i].stream); // Finalize GPU reduction for current subvector - sum = 0; + float sum = 0; - for (j = 0; j < ACCUM_N; j++) { + for (int j = 0; j < BLOCK_N; j++) { sum += plan[i].h_Sum_from_device[j]; } - *(plan[i].h_Sum) = (float)sum; + h_SumGPU[i] = sum; - // Shut down this GPU - checkCudaErrors(cudaFreeHost(plan[i].h_Sum_from_device)); - checkCudaErrors(cudaFree(plan[i].d_Sum)); - checkCudaErrors(cudaFree(plan[i].d_Data)); - checkCudaErrors(cudaStreamDestroy(plan[i].stream)); + // Free up this GPU's resources. h_Data stays alive for the CPU check below. + cudaFreeHost(plan[i].h_Sum_from_device); + cudaFree(plan[i].d_Sum); + cudaFree(plan[i].d_Data); + cudaStreamDestroy(plan[i].stream); } - sumGPU = 0; + float sumGPU = 0; - for (i = 0; i < GPU_N; i++) { + for (int i = 0; i < deviceCount; i++) { sumGPU += h_SumGPU[i]; } - sdkStopTimer(&timer); - printf(" GPU Processing time: %f (ms)\n\n", sdkGetTimerValue(&timer)); - sdkDeleteTimer(&timer); + cudaSetDevice(0); + cudaEventRecord(stop); + cudaEventSynchronize(stop); + + float gpuTime = 0; + cudaEventElapsedTime(&gpuTime, start, stop); + printf(" GPU Processing time: %f (ms)\n\n", gpuTime); + cudaEventDestroy(start); + cudaEventDestroy(stop); // Compute on Host CPU printf("Computing with Host CPU...\n\n"); - sumCPU = 0; + double sumCPU = 0; - for (i = 0; i < GPU_N; i++) { - for (j = 0; j < plan[i].dataN; j++) { + for (int i = 0; i < deviceCount; i++) { + for (int j = 0; j < plan[i].dataN; j++) { sumCPU += plan[i].h_Data[j]; } } // Compare GPU and CPU results printf("Comparing GPU and Host CPU results...\n"); - diff = fabs(sumCPU - sumGPU) / fabs(sumCPU); + const double diff = fabs(sumCPU - sumGPU) / fabs(sumCPU); printf(" GPU sum: %f\n CPU sum: %f\n", sumGPU, sumCPU); printf(" Relative difference: %E \n\n", diff); // Cleanup and shutdown - for (i = 0; i < GPU_N; i++) { - checkCudaErrors(cudaSetDevice(i)); - checkCudaErrors(cudaFreeHost(plan[i].h_Data)); + for (int i = 0; i < deviceCount; i++) { + cudaFreeHost(plan[i].h_Data); } - exit((diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE); + return (diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE; } diff --git a/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.h b/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.h deleted file mode 100644 index 3bfeef21..00000000 --- a/cpp/0_Introduction/simpleMultiGPU/simpleMultiGPU.h +++ /dev/null @@ -1,62 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This application demonstrates how to use the CUDA API to use multiple GPUs. - * - * Note that in order to detect multiple GPUs in your system you have to disable - * SLI in the nvidia control panel. Otherwise only one GPU is visible to the - * application. On the other side, you can still extend your desktop to screens - * attached to both GPUs. - */ - -#ifndef SIMPLEMULTIGPU_H -#define SIMPLEMULTIGPU_H - -typedef struct -{ - // Host-side input data - int dataN; - float *h_Data; - - // Partial sum for this GPU - float *h_Sum; - - // Device buffers - float *d_Data, *d_Sum; - - // Reduction copied back from GPU - float *h_Sum_from_device; - - // Stream for asynchronous command execution - cudaStream_t stream; - -} TGPUplan; - -extern "C" void launch_reduceKernel(float *d_Result, float *d_Input, int N, int BLOCK_N, int THREAD_N, cudaStream_t &s); - -#endif diff --git a/cpp/0_Introduction/simpleOccupancy/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleOccupancy/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleOccupancy/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleOccupancy/.vscode/extensions.json b/cpp/0_Introduction/simpleOccupancy/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleOccupancy/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleOccupancy/CMakeLists.txt b/cpp/0_Introduction/simpleOccupancy/CMakeLists.txt deleted file mode 100644 index fbd4ec77..00000000 --- a/cpp/0_Introduction/simpleOccupancy/CMakeLists.txt +++ /dev/null @@ -1,34 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleOccupancy LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleOccupancy -add_executable(simpleOccupancy simpleOccupancy.cu) - -target_compile_options(simpleOccupancy PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleOccupancy PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleOccupancy PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleOccupancy/README.md b/cpp/0_Introduction/simpleOccupancy/README.md deleted file mode 100644 index d47e7f2e..00000000 --- a/cpp/0_Introduction/simpleOccupancy/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# simpleOccupancy - simpleOccupancy - -## Description - -This sample demonstrates the basic usage of the CUDA occupancy calculator and occupancy-based launch configurator APIs by launching a kernel with the launch configurator, and measures the utilization difference against a manually configured launch. - -## Key Concepts - -Occupancy Calculator - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaFree, cudaDeviceSynchronize, cudaEventRecord, cudaGetDevice, cudaMalloc, cudaEventElapsedTime, cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaGetDeviceProperties, cudaOccupancyMaxPotentialBlockSize, cudaEventCreate - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) diff --git a/cpp/0_Introduction/simpleOccupancy/simpleOccupancy.cu b/cpp/0_Introduction/simpleOccupancy/simpleOccupancy.cu deleted file mode 100644 index 0411006c..00000000 --- a/cpp/0_Introduction/simpleOccupancy/simpleOccupancy.cu +++ /dev/null @@ -1,240 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include // helper functions for CUDA error check -#include - -const int manualBlockSize = 32; - -//////////////////////////////////////////////////////////////////////////////// -// Test kernel -// -// This kernel squares each array element. Each thread addresses -// himself with threadIdx and blockIdx, so that it can handle any -// execution configuration, including anything the launch configurator -// API suggests. -//////////////////////////////////////////////////////////////////////////////// -__global__ void square(uint32_t *array, int arrayCount) -{ - extern __shared__ int dynamicSmem[]; - int idx = threadIdx.x + blockIdx.x * blockDim.x; - - if (idx < arrayCount) { - array[idx] *= array[idx]; - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Potential occupancy calculator -// -// The potential occupancy is calculated according to the kernel and -// execution configuration the user desires. Occupancy is defined in -// terms of active blocks per multiprocessor, and the user can convert -// it to other metrics. -// -// This wrapper routine computes the occupancy of kernel, and reports -// it in terms of active warps / maximum warps per SM. -//////////////////////////////////////////////////////////////////////////////// -static double reportPotentialOccupancy(void *kernel, int blockSize, size_t dynamicSMem) -{ - int device; - cudaDeviceProp prop; - - int numBlocks; - int activeWarps; - int maxWarps; - - double occupancy; - - checkCudaErrors(cudaGetDevice(&device)); - checkCudaErrors(cudaGetDeviceProperties(&prop, device)); - - checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&numBlocks, kernel, blockSize, dynamicSMem)); - - activeWarps = numBlocks * blockSize / prop.warpSize; - maxWarps = prop.maxThreadsPerMultiProcessor / prop.warpSize; - - occupancy = (double)activeWarps / maxWarps; - - return occupancy; -} - -//////////////////////////////////////////////////////////////////////////////// -// Occupancy-based launch configurator -// -// The launch configurator, cudaOccupancyMaxPotentialBlockSize and -// cudaOccupancyMaxPotentialBlockSizeVariableSMem, suggests a block -// size that achieves the best theoretical occupancy. It also returns -// the minimum number of blocks needed to achieve the occupancy on the -// whole device. -// -// This launch configurator is purely occupancy-based. It doesn't -// translate directly to performance, but the suggestion should -// nevertheless be a good starting point for further optimizations. -// -// This function configures the launch based on the "automatic" -// argument, records the runtime, and reports occupancy and runtime. -//////////////////////////////////////////////////////////////////////////////// -static int launchConfig(uint32_t *array, int arrayCount, bool automatic) -{ - int blockSize; - int minGridSize; - int gridSize; - size_t dynamicSMemUsage = 0; - - cudaEvent_t start; - cudaEvent_t end; - - float elapsedTime; - - double potentialOccupancy; - - checkCudaErrors(cudaEventCreate(&start)); - checkCudaErrors(cudaEventCreate(&end)); - - if (automatic) { - checkCudaErrors( - cudaOccupancyMaxPotentialBlockSize(&minGridSize, &blockSize, (void *)square, dynamicSMemUsage, arrayCount)); - - std::cout << "Suggested block size: " << blockSize << std::endl - << "Minimum grid size for maximum occupancy: " << minGridSize << std::endl; - } - else { - // This block size is too small. Given limited number of - // active blocks per multiprocessor, the number of active - // threads will be limited, and thus unable to achieve maximum - // occupancy. - // - blockSize = manualBlockSize; - } - - // Round up - // - gridSize = (arrayCount + blockSize - 1) / blockSize; - - // Launch and profile - // - checkCudaErrors(cudaEventRecord(start)); - square<<>>(array, arrayCount); - checkCudaErrors(cudaEventRecord(end)); - - checkCudaErrors(cudaDeviceSynchronize()); - - // Calculate occupancy - // - potentialOccupancy = reportPotentialOccupancy((void *)square, blockSize, dynamicSMemUsage); - - std::cout << "Potential occupancy: " << potentialOccupancy * 100 << "%" << std::endl; - - // Report elapsed time - // - checkCudaErrors(cudaEventElapsedTime(&elapsedTime, start, end)); - std::cout << "Elapsed time: " << elapsedTime << "ms" << std::endl; - - return 0; -} - -//////////////////////////////////////////////////////////////////////////////// -// The test -// -// The test generates an array and squares it with a CUDA kernel, then -// verifies the result. -//////////////////////////////////////////////////////////////////////////////// -static int test(bool automaticLaunchConfig, const int count = 1000000) -{ - uint32_t *array; - uint32_t *dArray; - int size = count * sizeof(uint32_t); - - array = new uint32_t[count]; - - for (uint32_t i = 0; i < count; i += 1) { - array[i] = i; - } - - checkCudaErrors(cudaMalloc(&dArray, size)); - checkCudaErrors(cudaMemcpy(dArray, array, size, cudaMemcpyHostToDevice)); - - for (uint32_t i = 0; i < count; i += 1) { - array[i] = 0; - } - - launchConfig(dArray, count, automaticLaunchConfig); - - checkCudaErrors(cudaMemcpy(array, dArray, size, cudaMemcpyDeviceToHost)); - checkCudaErrors(cudaFree(dArray)); - - // Verify the return data - // Both GPU and CPU use uint32_t * uint32_t, which has well-defined overflow behavior (modulo 2^32) - // - for (uint32_t i = 0; i < count; i += 1) { - if (array[i] != i * i) { - std::cout << "element " << i << " expected " << i * i << " actual " << array[i] << std::endl; - return 1; - } - } - delete[] array; - - return 0; -} - -//////////////////////////////////////////////////////////////////////////////// -// Sample Main -// -// The sample runs the test with manually configured launch and -// automatically configured launch, and reports the occupancy and -// performance. -//////////////////////////////////////////////////////////////////////////////// -int main() -{ - int status; - - std::cout << "starting Simple Occupancy" << std::endl << std::endl; - - std::cout << "[ Manual configuration with " << manualBlockSize << " threads per block ]" << std::endl; - - status = test(false); - if (status) { - std::cerr << "Test failed\n" << std::endl; - return -1; - } - - std::cout << std::endl; - - std::cout << "[ Automatic, occupancy-based configuration ]" << std::endl; - status = test(true); - if (status) { - std::cerr << "Test failed\n" << std::endl; - return -1; - } - - std::cout << std::endl; - std::cout << "Test PASSED\n" << std::endl; - - return 0; -} diff --git a/cpp/0_Introduction/simpleP2P/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleP2P/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleP2P/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleP2P/.vscode/extensions.json b/cpp/0_Introduction/simpleP2P/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleP2P/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleP2P/CMakeLists.txt b/cpp/0_Introduction/simpleP2P/CMakeLists.txt index ddfe6c11..90d17c19 100644 --- a/cpp/0_Introduction/simpleP2P/CMakeLists.txt +++ b/cpp/0_Introduction/simpleP2P/CMakeLists.txt @@ -1,26 +1,20 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleP2P LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleP2P if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample simpleP2P - not supported on aarch64") else() @@ -30,9 +24,7 @@ else() target_compile_features(simpleP2P PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(simpleP2P PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simplePitchLinearTexture/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simplePitchLinearTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simplePitchLinearTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simplePitchLinearTexture/.vscode/extensions.json b/cpp/0_Introduction/simplePitchLinearTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simplePitchLinearTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simplePitchLinearTexture/CMakeLists.txt b/cpp/0_Introduction/simplePitchLinearTexture/CMakeLists.txt index 7a72745c..44567a0f 100644 --- a/cpp/0_Introduction/simplePitchLinearTexture/CMakeLists.txt +++ b/cpp/0_Introduction/simplePitchLinearTexture/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simplePitchLinearTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simplePitchLinearTexture add_executable(simplePitchLinearTexture simplePitchLinearTexture.cu) target_compile_options(simplePitchLinearTexture PRIVATE $<$:--extended-lambda>) target_compile_features(simplePitchLinearTexture PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simplePitchLinearTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simplePrintf/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simplePrintf/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simplePrintf/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simplePrintf/.vscode/extensions.json b/cpp/0_Introduction/simplePrintf/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simplePrintf/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simplePrintf/CMakeLists.txt b/cpp/0_Introduction/simplePrintf/CMakeLists.txt index 8f9ca7d7..0a5e77f6 100644 --- a/cpp/0_Introduction/simplePrintf/CMakeLists.txt +++ b/cpp/0_Introduction/simplePrintf/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simplePrintf LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simplePrintf add_executable(simplePrintf simplePrintf.cu) target_compile_options(simplePrintf PRIVATE $<$:--extended-lambda>) target_compile_features(simplePrintf PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simplePrintf PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simplePrintf/README.md b/cpp/0_Introduction/simplePrintf/README.md index ff00a268..4e820c37 100644 --- a/cpp/0_Introduction/simplePrintf/README.md +++ b/cpp/0_Introduction/simplePrintf/README.md @@ -1,32 +1,77 @@ -# simplePrintf - simplePrintf +# Sample: Device-Side printf (simplePrintf) ## Description -This basic CUDA Runtime API sample demonstrates how to use the printf function in the device code. +A first-look CUDA sample: call C's `printf` **from inside a CUDA kernel**. Every thread in a 2D grid of 3D blocks prints its own block and thread identifiers along with a shared value passed from the host. The CUDA runtime buffers the device-side output and flushes it to the host's standard output when the kernel completes. + +This sample is the canonical demonstration of device-side `printf`, the simplest debugging and tracing tool available inside kernel code. It also shows how multidimensional `blockIdx`/`threadIdx` coordinates are flattened into linear indices. + +## What You'll Learn + +- Calling `printf` directly from device code (`__global__` kernel) +- Launching a kernel with a **2D grid** (`dim3 dimGrid(2, 2)`) of **3D blocks** (`dim3 dimBlock(2, 2, 2)`) +- Flattening multidimensional block and thread indices into a single linear index +- Flushing device-side `printf` output to the host with `cudaDeviceSynchronize` +- Querying the active device with `cudaGetDevice` / `cudaGetDeviceProperties` ## Key Concepts -Debugging +- **Device-side `printf`** — formatted output from within a kernel; output is buffered per launch and flushed at a synchronization point +- **Multidimensional launch geometry** — `gridDim`, `blockIdx`, `blockDim`, and `threadIdx` are `dim3` values with `.x`, `.y`, `.z` components +- **Index linearization** — converting `(x, y, z)` coordinates into a flat index: + - block: `blockIdx.y * gridDim.x + blockIdx.x` + - thread: `threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x` -## Supported SM Architectures +## Key APIs -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +### CUDA Runtime +- `cudaSetDevice` — select the active GPU +- `cudaDeviceGetAttribute` — query compute capability (major, minor) and SM count +- `cudaDeviceSynchronize` — block the host until the kernel finishes, which also flushes the device `printf` buffer -## Supported OSes +### Device +- `printf` — standard C formatted output, callable from `__global__`/`__device__` code (requires Compute Capability 2.0 or higher) -Linux, Windows +## Requirements -## Supported CPU Architecture +### Hardware +- NVIDIA GPU with Compute Capability 2.0 or higher (device-side `printf` is unavailable on earlier architectures) -x86_64, armv7l +### Software +- CMake 3.20 or newer +- A C++17-capable host compiler -## CUDA APIs involved +## How to Build -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaGetDeviceProperties, cudaDeviceSynchronize, cudaGetDevice +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. -## Prerequisites +## How to Run -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +```bash +./simplePrintf +``` -## References (for more details) +## Expected Output + +The launch uses a 2×2 grid of 2×2×2 blocks = 4 blocks × 8 threads = 32 lines. Each thread prints its linear block index, its linear thread index, and the value `10`. Because blocks and threads run concurrently, **the order of the lines will vary between runs**: + +```text +GPU Device 0: with compute capability X.Y and Number of SMs +printf() is called. Output: + +[0, 0]: Value is:10 +[0, 1]: Value is:10 +[0, 2]: Value is:10 +... +[3, 7]: Value is:10 +``` + +## Files + +- `simplePrintf.cu` — device-side `printf` kernel + host driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA C++ Programming Guide — printf()](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-support.html#printf) diff --git a/cpp/0_Introduction/simplePrintf/simplePrintf.cu b/cpp/0_Introduction/simplePrintf/simplePrintf.cu index 694fa21d..29aa6318 100644 --- a/cpp/0_Introduction/simplePrintf/simplePrintf.cu +++ b/cpp/0_Introduction/simplePrintf/simplePrintf.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -32,34 +32,35 @@ // CUDA runtime #include -// helper functions and utilities to work with CUDA -#include -#include - #ifndef MAX #define MAX(a, b) (a > b ? a : b) #endif -__global__ void testKernel(int val) +// Each thread calls device-side printf, printing its linear block index, its +// linear thread index, and val. Output is flushed on host sync; line order is +// not deterministic since blocks/threads run concurrently. +__global__ void simplePrintfKernel(int val) { printf("[%d, %d]:\t\tValue is:%d\n", - blockIdx.y * gridDim.x + blockIdx.x, - threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x, + blockIdx.y * gridDim.x + blockIdx.x, // linear block index + threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x, // linear thread index val); } int main(int argc, char **argv) { - int devID; - cudaDeviceProp props; + // Select device 0 as the active GPU + int devID = 0; + cudaSetDevice(devID); - // This will pick the best possible CUDA capable device - devID = findCudaDevice(argc, (const char **)argv); + // Query compute capability (major.minor) and number of SMs on the device + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); - // Get GPU information - checkCudaErrors(cudaGetDevice(&devID)); - checkCudaErrors(cudaGetDeviceProperties(&props, devID)); - printf("Device %d: \"%s\" with Compute capability %d.%d\n", devID, props.name, props.major, props.minor); + // Print device info + printf("GPU Device %d: with compute capability %d.%d and Number of SMs %d\n\n", devID, major, minor, smCount); printf("printf() is called. Output:\n\n"); @@ -67,7 +68,7 @@ int main(int argc, char **argv) // three-dimensional blocks are configured. dim3 dimGrid(2, 2); dim3 dimBlock(2, 2, 2); - testKernel<<>>(10); + simplePrintfKernel<<>>(10); cudaDeviceSynchronize(); return EXIT_SUCCESS; diff --git a/cpp/0_Introduction/simpleStreams/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleStreams/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleStreams/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleStreams/.vscode/extensions.json b/cpp/0_Introduction/simpleStreams/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleStreams/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleStreams/CMakeLists.txt b/cpp/0_Introduction/simpleStreams/CMakeLists.txt index 8e4be29e..5b94c813 100644 --- a/cpp/0_Introduction/simpleStreams/CMakeLists.txt +++ b/cpp/0_Introduction/simpleStreams/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleStreams LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleStreams add_executable(simpleStreams simpleStreams.cu) target_compile_options(simpleStreams PRIVATE $<$:--extended-lambda>) target_compile_features(simpleStreams PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleStreams PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleStreams/README.md b/cpp/0_Introduction/simpleStreams/README.md index 80f609b5..36135546 100644 --- a/cpp/0_Introduction/simpleStreams/README.md +++ b/cpp/0_Introduction/simpleStreams/README.md @@ -1,32 +1,125 @@ -# simpleStreams - simpleStreams +# Sample: simpleStreams ## Description -This sample uses CUDA streams to overlap kernel executions with memory copies between the host and a GPU device. This sample uses a new CUDA 4.0 feature that supports pinning of generic host memory. Requires Compute Capability 2.0 or higher. +A simple benchmark that shows how CUDA streams hide memory-transfer latency behind kernel execution. +The GPU computes an element-wise square of a 64 MB float array. It is run twice — once in a single +stream and once split across 4 streams — so you can directly compare the speedup from overlapping +H2D copies, kernel execution, and D2H copies. + +## What You'll Learn + +- How CUDA streams create independent, in-order queues that enable H2D + kernel + D2H overlap +- How to split a large buffer across N streams so each stream owns its own chunk +- Why pinned (`cudaMallocHost`) host memory is required for `cudaMemcpyAsync` to be truly async +- How to create and use CUDA events to time GPU work with ~0.5 µs precision +- How to query GPU properties (compute capability, SM count) with `cudaDeviceGetAttribute` ## Key Concepts -Asynchronous Data Transfers, CUDA Streams and Events +### CUDA Streams -## Supported SM Architectures +A stream is an ordered queue of GPU commands. Commands in the **same** stream execute in order; +commands in **different** streams may overlap if the hardware has capacity. This sample uses 4 +streams. Each stream owns one quarter of the array and runs its own H2D → kernel → D2H pipeline: -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +``` +Stream 0: [H2D chunk 0][kernel 0][D2H chunk 0] +Stream 1: [H2D chunk 1][kernel 1][D2H chunk 1] +Stream 2: [H2D chunk 2][kernel 2][D2H chunk 2] +Stream 3: [H2D chunk 3][kernel 3][D2H chunk 3] + |----------------------------------------------> wall clock +``` -## Supported OSes +D2H copy N waits for kernel N (same stream), but overlaps with the H2D copy and kernel of stream N+1. -Linux, Windows +### The Kernel: `square_kernel` -## Supported CPU Architecture +```cu +out[idx] = in[idx] * in[idx]; +``` -x86_64, armv7l +Each thread computes the square of one float element. Simple and fast — the bottleneck is the +memory transfer, which streams are designed to hide. -## CUDA APIs involved +### Pinned Host Memory -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaSetDeviceFlags, cudaSetDevice, cudaEventDestroy, cudaStreamCreate, cudaMallocHost, cudaEventCreateWithFlags, cudaFreeHost, cudaMemcpyAsync, cudaGetDeviceCount, cudaStreamDestroy, cudaMemset, cudaEventElapsedTime, cudaHostAlloc, cudaFree, cudaHostRegister, cudaEventSynchronize, cudaEventRecord, cudaMalloc, cudaGetDeviceProperties, cudaHostUnregister +`cudaMemcpyAsync` requires pinned (page-locked) host memory so the OS cannot swap out the pages +during the transfer. This sample always uses `cudaMallocHost` — the simplest and most portable +pinning strategy. -## Prerequisites +### Single-stream vs Multi-stream -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +| Mode | What happens | +|---|---| +| Single stream | H2D → kernel → D2H run sequentially for the full 64 MB array | +| Multi-stream | 4 chunks pipeline concurrently; copy cost approaches 1/4 of the single-stream baseline | -## References (for more details) +## Key APIs + +### CUDA Runtime + +- `cudaStreamCreate` / `cudaStreamDestroy` — create and destroy an independent command queue +- `cudaMemcpyAsync` — non-blocking H2D or D2H transfer; requires pinned host memory +- `cudaMemcpy` — blocking transfer used in the single-stream reference benchmark +- `cudaDeviceSynchronize` — wait for all streams to finish before recording the stop event +- `cudaEventCreate` / `cudaEventDestroy` — create and release timing events +- `cudaEventRecord` — insert a timestamp into a stream +- `cudaEventSynchronize` — block the host until the event is recorded +- `cudaEventElapsedTime` — compute milliseconds between two recorded events +- `cudaMallocHost` / `cudaFreeHost` — allocate and free pinned host memory +- `cudaMalloc` / `cudaFree` — standard device memory management +- `cudaDeviceGetAttribute` — query GPU properties such as compute capability and SM count + +## Requirements + +### Hardware + +- NVIDIA GPU with Compute Capability 7.5 or higher + +### Software + +- CUDA Toolkit (any version supporting the target GPU) +- CMake 3.20 or newer +- A C++17-capable host compiler + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +```bash +./simpleStreams +``` + +No arguments needed. + +## Expected Output + +```text +[ CUDA Sample: Streams ] + +GPU Device 0: with compute capability X.Y and Number of SMs + +Single stream = 8.243 ms +Multi-stream = 3.167 ms +Speedup = 2.60x +``` + +**Reading the numbers:** +- `Single stream` — full 64 MB processed sequentially (H2D + kernel + D2H back-to-back) +- `Multi-stream` — same work split across 4 streams with overlap; typically 2–3x faster +- `Speedup` — ratio of single-stream time to multi-stream time; higher means more overlap achieved + +## Files + +- `simpleStreams.cu` — kernel, `run_single_stream`, `run_multi_stream`, and main driver +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA C++ Programming Guide — CUDA Streams](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#cuda-streams) +- [CUDA C++ Programming Guide — Creating and Destroying CUDA Streams](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#creating-and-destroying-cuda-streams) +- [CUDA C++ Best Practices Guide — CUDA Events](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/asynchronous-execution.html#cuda-events) diff --git a/cpp/0_Introduction/simpleStreams/simpleStreams.cu b/cpp/0_Introduction/simpleStreams/simpleStreams.cu index eb92b55c..0c162c4c 100644 --- a/cpp/0_Introduction/simpleStreams/simpleStreams.cu +++ b/cpp/0_Introduction/simpleStreams/simpleStreams.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -27,396 +27,200 @@ /* * This sample illustrates the usage of CUDA streams for overlapping - * kernel execution with device/host memcopies. The kernel is used to - * initialize an array to a specific value, after which the array is - * copied to the host (CPU) memory. To increase performance, multiple + * kernel execution with device/host memcopies. The kernel computes an + * element-wise square of a float array, after which the result is copied + * back to host (CPU) memory. To increase performance, multiple * kernel/memcopy pairs are launched asynchronously, each pair in its - * own stream. Devices with Compute Capability 1.1 can overlap a kernel - * and a memcopy as long as they are issued in different streams. Kernels - * are serialized. Thus, if n pairs are launched, streamed approach - * can reduce the memcopy cost to the (1/n)th of a single copy of the entire - * data set. + * own stream. A GPU can overlap a kernel and a memcopy as long as they + * are issued in different streams. Thus, if n pairs are launched, the + * streamed approach can reduce the memcopy cost to the (1/n)th of a + * single copy of the entire data set. * * Additionally, this sample uses CUDA events to measure elapsed time for * CUDA calls. Events are a part of CUDA API and provide a system independent * way to measure execution times on CUDA devices with approximately 0.5 * microsecond precision. * - * Elapsed times are averaged over nreps repetitions (10 by default). - * */ -const char *sSDKsample = "simpleStreams"; - -const char *sEventSyncMethod[] = {"cudaEventDefault", "cudaEventBlockingSync", "cudaEventDisableTiming", NULL}; - -const char *sDeviceSyncMethod[] = {"cudaDeviceScheduleAuto", - "cudaDeviceScheduleSpin", - "cudaDeviceScheduleYield", - "INVALID", - "cudaDeviceScheduleBlockingSync", - NULL}; - // System includes -#include #include // CUDA runtime #include -// helper functions and utilities to work with CUDA -#include -#include +#define N (1 << 24) // ~16M elements +#define BLOCK 256 // threads per block +#define NUM_STREAMS 4 // number of concurrent streams -#ifndef WIN32 -#include // for mmap() / munmap() -#endif - -// Macro to aligned up to the memory size in question -#define MEMORY_ALIGNMENT 4096 -#define ALIGN_UP(x, size) (((size_t)x + (size - 1)) & (~(size - 1))) - -__global__ void init_array(int *g_data, int *factor, int num_iterations) -{ +// Kernel: computes element-wise square of the input array +__global__ void square_kernel(const float* in, float* out, int n){ int idx = blockIdx.x * blockDim.x + threadIdx.x; - - for (int i = 0; i < num_iterations; i++) { - g_data[idx] += *factor; // non-coalesced on purpose, to burn time + if (idx < n){ + out[idx] = in[idx] * in[idx]; } } -bool correct_data(int *a, const int n, const int c) -{ - for (int i = 0; i < n; i++) { - if (a[i] != c) { - printf("%d: %d %d\n", i, a[i], c); - return false; - } - } - - return true; -} - -inline void AllocateHostMemory(bool bPinGenericMemory, int **pp_a, int **ppAligned_a, int nbytes) -{ -#if CUDART_VERSION >= 4000 -#if !defined(__arm__) && !defined(__aarch64__) - if (bPinGenericMemory) { -// allocate a generic page-aligned chunk of system memory -#ifdef WIN32 - printf("> VirtualAlloc() allocating %4.2f Mbytes of (generic page-aligned " - "system memory)\n", - (float)nbytes / 1048576.0f); - *pp_a = (int *)VirtualAlloc(NULL, (nbytes + MEMORY_ALIGNMENT), MEM_RESERVE | MEM_COMMIT, PAGE_READWRITE); -#else - printf("> mmap() allocating %4.2f Mbytes (generic page-aligned system " - "memory)\n", - (float)nbytes / 1048576.0f); - *pp_a = (int *)mmap(NULL, (nbytes + MEMORY_ALIGNMENT), PROT_READ | PROT_WRITE, MAP_PRIVATE | MAP_ANON, -1, 0); -#endif - - *ppAligned_a = (int *)ALIGN_UP(*pp_a, MEMORY_ALIGNMENT); - - printf("> cudaHostRegister() registering %4.2f Mbytes of generic allocated " - "system memory\n", - (float)nbytes / 1048576.0f); - // pin allocate memory - checkCudaErrors(cudaHostRegister(*ppAligned_a, nbytes, cudaHostRegisterMapped)); - } - else -#endif -#endif - { - printf("> cudaMallocHost() allocating %4.2f Mbytes of system memory\n", (float)nbytes / 1048576.0f); - // allocate host memory (pinned is required for achieve asynchronicity) - checkCudaErrors(cudaMallocHost((void **)pp_a, nbytes)); - *ppAligned_a = *pp_a; - } -} - -inline void FreeHostMemory(bool bPinGenericMemory, int **pp_a, int **ppAligned_a, int nbytes) -{ -#if CUDART_VERSION >= 4000 -#if !defined(__arm__) && !defined(__aarch64__) - // CUDA 4.0 support pinning of generic host memory - if (bPinGenericMemory) { - // unpin and delete host memory - checkCudaErrors(cudaHostUnregister(*ppAligned_a)); -#ifdef WIN32 - VirtualFree(*pp_a, 0, MEM_RELEASE); -#else - munmap(*pp_a, nbytes); -#endif - } - else -#endif -#endif - { - cudaFreeHost(*pp_a); - } -} - -static const char *sSyncMethod[] = {"0 (Automatic Blocking)", - "1 (Spin Blocking)", - "2 (Yield Blocking)", - "3 (Undefined Blocking Method)", - "4 (Blocking Sync Event) = low CPU utilization", - NULL}; - -void printHelp() -{ - printf("Usage: %s [options below]\n", sSDKsample); - printf("\t--sync_method=n for CPU/GPU synchronization\n"); - printf("\t n=%s\n", sSyncMethod[0]); - printf("\t n=%s\n", sSyncMethod[1]); - printf("\t n=%s\n", sSyncMethod[2]); - printf("\t n=%s\n", sSyncMethod[4]); - printf("\t--use_generic_memory (default) use generic page-aligned for system " - "memory\n"); - printf("\t--use_cuda_malloc_host (optional) use cudaMallocHost to allocate " - "system memory\n"); -} - -#if defined(__APPLE__) || defined(MACOSX) -#define DEFAULT_PINNED_GENERIC_MEMORY false -#else -#define DEFAULT_PINNED_GENERIC_MEMORY true -#endif - -int main(int argc, char **argv) -{ - int cuda_device = 0; - int nstreams = 4; // number of streams for CUDA calls - int nreps = 10; // number of times each experiment is repeated - int n = 16 * 1024 * 1024; // number of ints in the data set - int nbytes = n * sizeof(int); // number of data bytes - dim3 threads, blocks; // kernel launch configuration - float elapsed_time, time_memcpy, time_kernel; // timing variables - float scale_factor = 1.0f; - - // allocate generic memory and pin it laster instead of using cudaHostAlloc() - - bool bPinGenericMemory = DEFAULT_PINNED_GENERIC_MEMORY; // we want this to be the default behavior - int device_sync_method = cudaDeviceBlockingSync; // by default we use BlockingSync - - int niterations; // number of iterations for the loop inside the kernel - - printf("[ %s ]\n\n", sSDKsample); - - if (checkCmdLineFlag(argc, (const char **)argv, "help")) { - printHelp(); - return EXIT_SUCCESS; - } - - if ((device_sync_method = getCmdLineArgumentInt(argc, (const char **)argv, "sync_method")) >= 0) { - if (device_sync_method == 0 || device_sync_method == 1 || device_sync_method == 2 || device_sync_method == 4) { - printf("Device synchronization method set to = %s\n", sSyncMethod[device_sync_method]); - printf("Setting reps to 100 to demonstrate steady state\n"); - nreps = 100; - } - else { - printf("Invalid command line option sync_method=\"%d\"\n", device_sync_method); - return EXIT_FAILURE; - } - } - else { - printHelp(); - return EXIT_SUCCESS; - } - - if (checkCmdLineFlag(argc, (const char **)argv, "use_generic_memory")) { -#if defined(__APPLE__) || defined(MACOSX) - bPinGenericMemory = false; // Generic Pinning of System Paged memory not - // currently supported on Mac OSX -#else - bPinGenericMemory = true; -#endif - } - - if (checkCmdLineFlag(argc, (const char **)argv, "use_cuda_malloc_host")) { - bPinGenericMemory = false; - } - - printf("\n> "); - cuda_device = findCudaDevice(argc, (const char **)argv); - - // check the compute capability of the device - int num_devices = 0; - checkCudaErrors(cudaGetDeviceCount(&num_devices)); - - if (0 == num_devices) { - printf("your system does not have a CUDA capable device, waiving test...\n"); - return EXIT_WAIVED; - } - - // check if the command-line chosen device ID is within range, exit if not - if (cuda_device >= num_devices) { - printf("cuda_device=%d is invalid, must choose device ID between 0 and %d\n", cuda_device, num_devices - 1); - return EXIT_FAILURE; - } - - checkCudaErrors(cudaSetDevice(cuda_device)); - - // Checking for compute capabilities - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, cuda_device)); - - niterations = 5; - - // Check if GPU can map host memory (Generic Method), if not then we override - // bPinGenericMemory to be false - if (bPinGenericMemory) { - printf("Device: <%s> canMapHostMemory: %s\n", deviceProp.name, deviceProp.canMapHostMemory ? "Yes" : "No"); - - if (deviceProp.canMapHostMemory == 0) { - printf("Using cudaMallocHost, CUDA device does not support mapping of " - "generic host memory\n"); - bPinGenericMemory = false; - } - } - - // Anything that is less than 32 Cores will have scaled down workload - scale_factor = - max((32.0f / (_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) * (float)deviceProp.multiProcessorCount)), - 1.0f); - n = (int)rint((float)n / scale_factor); - - printf("> CUDA Capable: SM %d.%d hardware\n", deviceProp.major, deviceProp.minor); - printf("> %d Multiprocessor(s) x %d (Cores/Multiprocessor) = %d (Cores)\n", - deviceProp.multiProcessorCount, - _ConvertSMVer2Cores(deviceProp.major, deviceProp.minor), - _ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) * deviceProp.multiProcessorCount); - - printf("> scale_factor = %1.4f\n", 1.0f / scale_factor); - printf("> array_size = %d\n\n", n); - - // enable use of blocking sync, to reduce CPU usage - printf("> Using CPU/GPU Device Synchronization method (%s)\n", sDeviceSyncMethod[device_sync_method]); - checkCudaErrors(cudaSetDeviceFlags(device_sync_method | (bPinGenericMemory ? cudaDeviceMapHost : 0))); - - // allocate host memory - int c = 5; // value to which the array will be initialized - int *h_a = 0; // pointer to the array data in host memory - int *hAligned_a = 0; // pointer to the array data in host memory (aligned to - // MEMORY_ALIGNMENT) - - // Allocate Host memory (could be using cudaMallocHost or VirtualAlloc/mmap if - // using the new CUDA 4.0 features - AllocateHostMemory(bPinGenericMemory, &h_a, &hAligned_a, nbytes); +// Run the full H2D memcopy + kernel + D2H memcopy in a single default stream. +// Returns elapsed time in milliseconds. +float run_default_stream(float* h_in, float* h_out, int n){ + float *d_in, *d_out; // device input and output buffers + size_t bytes = n * sizeof(float); // allocate device memory - int *d_a = 0, - *d_c = 0; // pointers to data and init value in the device memory - checkCudaErrors(cudaMalloc((void **)&d_a, nbytes)); - checkCudaErrors(cudaMemset(d_a, 0x0, nbytes)); - checkCudaErrors(cudaMalloc((void **)&d_c, sizeof(int))); - checkCudaErrors(cudaMemcpy(d_c, &c, sizeof(int), cudaMemcpyHostToDevice)); + cudaMalloc(&d_in, bytes); + cudaMalloc(&d_out, bytes); - printf("\nStarting Test\n"); + // create CUDA event handles for timing + cudaEvent_t start, stop; + cudaEventCreate(&start); + cudaEventCreate(&stop); + + cudaEventRecord(start); + + // copy input to device, run kernel, copy result back + cudaMemcpy(d_in, h_in, bytes, cudaMemcpyHostToDevice); + int grid = (n + BLOCK - 1) / BLOCK; + square_kernel<<>>(d_in, d_out, n); + cudaMemcpy(h_out, d_out, bytes, cudaMemcpyDeviceToHost); + + cudaEventRecord(stop); + cudaEventSynchronize(stop); // block until the event is actually recorded + + float ms; + cudaEventElapsedTime(&ms, start, stop); + + // release device resources + cudaFree(d_in); + cudaFree(d_out); + cudaEventDestroy(start); + cudaEventDestroy(stop); + + return ms; +} + +// Run the same workload split across 4 streams so that H2D memcopies, +// kernel execution, and D2H memcopies can overlap on the device. +// Returns elapsed time in milliseconds. +float run_multi_stream(float* h_in, float* h_out, int n){ + int chunk = n/NUM_STREAMS; // number of elements per stream + size_t chunk_bytes = chunk * sizeof(float); + + float *d_in[NUM_STREAMS], *d_out[NUM_STREAMS]; // per-stream device buffers + cudaStream_t streams[NUM_STREAMS]; // allocate and initialize an array of stream handles - cudaStream_t *streams = (cudaStream_t *)malloc(nstreams * sizeof(cudaStream_t)); - - for (int i = 0; i < nstreams; i++) { - checkCudaErrors(cudaStreamCreate(&(streams[i]))); + for(int i = 0; i < NUM_STREAMS; i++){ + cudaStreamCreateWithFlags(&streams[i], cudaStreamNonBlocking); + cudaMalloc(&d_in[i], chunk_bytes); + cudaMalloc(&d_out[i], chunk_bytes); } - // create CUDA event handles - // use blocking sync - cudaEvent_t start_event, stop_event; - int eventflags = ((device_sync_method == cudaDeviceBlockingSync) ? cudaEventBlockingSync : cudaEventDefault); + // create CUDA event handles for timing + cudaEvent_t start, stop; + cudaEventCreate(&start); + cudaEventCreate(&stop); - checkCudaErrors(cudaEventCreateWithFlags(&start_event, eventflags)); - checkCudaErrors(cudaEventCreateWithFlags(&stop_event, eventflags)); + cudaEventRecord(start, streams[0]); - // time memcopy from device - checkCudaErrors(cudaEventRecord(start_event, 0)); // record in stream-0, to - // ensure that all previous - // CUDA calls have - // completed - checkCudaErrors(cudaMemcpyAsync(hAligned_a, d_a, nbytes, cudaMemcpyDeviceToHost, streams[0])); - checkCudaErrors(cudaEventRecord(stop_event, 0)); - checkCudaErrors(cudaEventSynchronize(stop_event)); // block until the event is actually recorded - checkCudaErrors(cudaEventElapsedTime(&time_memcpy, start_event, stop_event)); - printf("memcopy:\t%.2f\n", time_memcpy); + // asynchronously launch 4 copies + kernels + copies, each in its own + // stream so they can overlap on the device + for(int i = 0; i < NUM_STREAMS; i++){ + int offset = i * chunk; // element offset into the host arrays for this stream - // time kernel - threads = dim3(512, 1); - blocks = dim3(n / threads.x, 1); - checkCudaErrors(cudaEventRecord(start_event, 0)); - init_array<<>>(d_a, d_c, niterations); - checkCudaErrors(cudaEventRecord(stop_event, 0)); - checkCudaErrors(cudaEventSynchronize(stop_event)); - checkCudaErrors(cudaEventElapsedTime(&time_kernel, start_event, stop_event)); - printf("kernel:\t\t%.2f\n", time_kernel); + // H2D: copy this stream's chunk to device (non-blocking on host) + cudaMemcpyAsync( + d_in[i], + h_in + offset, + chunk_bytes, + cudaMemcpyHostToDevice, + streams[i] + ); - ////////////////////////////////////////////////////////////////////// - // time non-streamed execution for reference - threads = dim3(512, 1); - blocks = dim3(n / threads.x, 1); - checkCudaErrors(cudaEventRecord(start_event, 0)); + // kernel: will only start after the H2D copy in this stream completes + int grid = (chunk + BLOCK - 1) / BLOCK; + square_kernel<<>>( + d_in[i], d_out[i], chunk + ); - for (int k = 0; k < nreps; k++) { - init_array<<>>(d_a, d_c, niterations); - checkCudaErrors(cudaMemcpy(hAligned_a, d_a, nbytes, cudaMemcpyDeviceToHost)); + // D2H: copy result back; starts after the kernel in this stream finishes + cudaMemcpyAsync( + h_out + offset, + d_out[i], + chunk_bytes, + cudaMemcpyDeviceToHost, + streams[i] + ); } - checkCudaErrors(cudaEventRecord(stop_event, 0)); - checkCudaErrors(cudaEventSynchronize(stop_event)); - checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event)); - printf("non-streamed:\t%.2f\n", elapsed_time / nreps); + // Wait for all streams to complete their work + for(int i=0; i < NUM_STREAMS; i++) { + cudaStreamSynchronize(streams[i]); + } + cudaEventRecord(stop, streams[0]); - ////////////////////////////////////////////////////////////////////// - // time execution with nstreams streams - threads = dim3(512, 1); - blocks = dim3(n / (nstreams * threads.x), 1); - memset(hAligned_a, 255, - nbytes); // set host memory bits to all 1s, for testing correctness - checkCudaErrors(cudaMemset(d_a, 0, nbytes)); // set device memory to all 0s, for testing correctness - checkCudaErrors(cudaEventRecord(start_event, 0)); + // Sync with stream 0, which only completes once all other work + // is done and the stop event is recorded + cudaStreamSynchronize(streams[0]); - for (int k = 0; k < nreps; k++) { - // asynchronously launch nstreams kernels, each operating on its own portion - // of data - for (int i = 0; i < nstreams; i++) { - init_array<<>>(d_a + i * n / nstreams, d_c, niterations); - } + float ms; + cudaEventElapsedTime(&ms, start, stop); - // asynchronously launch nstreams memcopies. Note that memcopy in stream x - // will only - // commence executing when all previous CUDA calls in stream x have - // completed - for (int i = 0; i < nstreams; i++) { - checkCudaErrors(cudaMemcpyAsync(hAligned_a + i * n / nstreams, - d_a + i * n / nstreams, - nbytes / nstreams, - cudaMemcpyDeviceToHost, - streams[i])); - } + // release per-stream resources + for(int i=0; i < NUM_STREAMS; i++){ + cudaFree(d_in[i]); + cudaFree(d_out[i]); + cudaStreamDestroy(streams[i]); } - checkCudaErrors(cudaEventRecord(stop_event, 0)); - checkCudaErrors(cudaEventSynchronize(stop_event)); - checkCudaErrors(cudaEventElapsedTime(&elapsed_time, start_event, stop_event)); - printf("%d streams:\t%.2f\n", nstreams, elapsed_time / nreps); + cudaEventDestroy(start); + cudaEventDestroy(stop); - // check whether the output is correct - printf("-------------------------------\n"); - bool bResults = correct_data(hAligned_a, n, c * nreps * niterations); - - // release resources - for (int i = 0; i < nstreams; i++) { - checkCudaErrors(cudaStreamDestroy(streams[i])); - } - - checkCudaErrors(cudaEventDestroy(start_event)); - checkCudaErrors(cudaEventDestroy(stop_event)); - - // Free cudaMallocHost or Generic Host allocated memory (from CUDA 4.0) - FreeHostMemory(bPinGenericMemory, &h_a, &hAligned_a, nbytes); - - checkCudaErrors(cudaFree(d_a)); - checkCudaErrors(cudaFree(d_c)); - - return bResults ? EXIT_SUCCESS : EXIT_FAILURE; + return ms; +} + +int main(){ + + printf("[ CUDA Sample: Streams ]\n\n"); + + // Query compute capability and number of SMs on device 0 + int cuda_device = 0; + int major = 0, minor = 0, smCount = 0; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, cuda_device); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, cuda_device); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, cuda_device); + printf("GPU Device %d: with compute capability %d.%d and Number of SMs %d\n\n", + cuda_device, major, minor, smCount); + + size_t bytes = N * sizeof(float); + + // allocate pinned host memory: the OS normally moves RAM pages around freely, + // pinning locks these pages in place so the GPU can read them directly + // without the data being moved away mid-transfer + float *h_in = 0; // pointer to input data in host memory + float *h_out = 0; // pointer to output data in host memory + cudaMallocHost(&h_in, bytes); + cudaMallocHost(&h_out, bytes); + + // initialize input array + for (int i = 0; i < N; i++) + h_in[i] = (float)i; + + ////////////////////////////////////////////////////////////////////// + // time single-stream execution for reference + float t1 = run_default_stream(h_in, h_out, N); + + ////////////////////////////////////////////////////////////////////// + // time execution with 4 streams + float t2 = run_multi_stream(h_in, h_out, N); + + printf("Single stream = %.3f ms\n", t1); + printf("Multi-stream = %.3f ms\n", t2); + printf("Speedup = %.2fx\n", t1 / t2); + + // release pinned host memory + cudaFreeHost(h_in); + cudaFreeHost(h_out); + + return 0; } diff --git a/cpp/0_Introduction/simpleSurfaceWrite/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleSurfaceWrite/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleSurfaceWrite/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleSurfaceWrite/.vscode/extensions.json b/cpp/0_Introduction/simpleSurfaceWrite/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleSurfaceWrite/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleSurfaceWrite/CMakeLists.txt b/cpp/0_Introduction/simpleSurfaceWrite/CMakeLists.txt index f7c5d5fc..efc34660 100644 --- a/cpp/0_Introduction/simpleSurfaceWrite/CMakeLists.txt +++ b/cpp/0_Introduction/simpleSurfaceWrite/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleSurfaceWrite LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleSurfaceWrite add_executable(simpleSurfaceWrite simpleSurfaceWrite.cu) target_compile_options(simpleSurfaceWrite PRIVATE $<$:--extended-lambda>) target_compile_features(simpleSurfaceWrite PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleSurfaceWrite PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - # Copy data to the output directory add_custom_command(TARGET simpleSurfaceWrite POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory @@ -36,6 +28,5 @@ add_custom_command(TARGET simpleSurfaceWrite POST_BUILD ${CMAKE_CURRENT_BINARY_DIR} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleTemplates/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleTemplates/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleTemplates/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleTemplates/.vscode/extensions.json b/cpp/0_Introduction/simpleTemplates/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleTemplates/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleTemplates/README.md b/cpp/0_Introduction/simpleTemplates/README.md deleted file mode 100644 index 22a0d8dc..00000000 --- a/cpp/0_Introduction/simpleTemplates/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# simpleTemplates - Simple Templates - -## Description - -This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays. - -## Key Concepts - -C++ Templates - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaMemcpy, cudaGetDeviceProperties, cudaFree - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) diff --git a/cpp/0_Introduction/simpleTemplates/sharedmem.cuh b/cpp/0_Introduction/simpleTemplates/sharedmem.cuh deleted file mode 100644 index 1293c788..00000000 --- a/cpp/0_Introduction/simpleTemplates/sharedmem.cuh +++ /dev/null @@ -1,185 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef _SHAREDMEM_H_ -#define _SHAREDMEM_H_ - -//**************************************************************************** -// Because dynamically sized shared memory arrays are declared "extern", -// we can't templatize them directly. To get around this, we declare a -// simple wrapper struct that will declare the extern array with a different -// name depending on the type. This avoids compiler errors about duplicate -// definitions. -// -// To use dynamically allocated shared memory in a templatized __global__ or -// __device__ function, just replace code like this: -// -// -// template -// __global__ void -// foo( T* g_idata, T* g_odata) -// { -// // Shared mem size is determined by the host app at run time -// extern __shared__ T sdata[]; -// ... -// doStuff(sdata); -// ... -// } -// -// With this -// template -// __global__ void -// foo( T* g_idata, T* g_odata) -// { -// // Shared mem size is determined by the host app at run time -// SharedMemory smem; -// T* sdata = smem.getPointer(); -// ... -// doStuff(sdata); -// ... -// } -//**************************************************************************** - -// This is the un-specialized struct. Note that we prevent instantiation of -// this -// struct by putting an undefined symbol in the function body so it won't -// compile. -template struct SharedMemory -{ - // Ensure that we won't compile any un-specialized types - __device__ T *getPointer() - { - extern __device__ void error(void); - error(); - return NULL; - } -}; - -// Following are the specializations for the following types. -// int, uint, char, uchar, short, ushort, long, ulong, bool, float, and double -// One could also specialize it for user-defined types. - -template <> struct SharedMemory -{ - __device__ int *getPointer() - { - extern __shared__ int s_int[]; - return s_int; - } -}; - -template <> struct SharedMemory -{ - __device__ unsigned int *getPointer() - { - extern __shared__ unsigned int s_uint[]; - return s_uint; - } -}; - -template <> struct SharedMemory -{ - __device__ char *getPointer() - { - extern __shared__ char s_char[]; - return s_char; - } -}; - -template <> struct SharedMemory -{ - __device__ unsigned char *getPointer() - { - extern __shared__ unsigned char s_uchar[]; - return s_uchar; - } -}; - -template <> struct SharedMemory -{ - __device__ short *getPointer() - { - extern __shared__ short s_short[]; - return s_short; - } -}; - -template <> struct SharedMemory -{ - __device__ unsigned short *getPointer() - { - extern __shared__ unsigned short s_ushort[]; - return s_ushort; - } -}; - -template <> struct SharedMemory -{ - __device__ long *getPointer() - { - extern __shared__ long s_long[]; - return s_long; - } -}; - -template <> struct SharedMemory -{ - __device__ unsigned long *getPointer() - { - extern __shared__ unsigned long s_ulong[]; - return s_ulong; - } -}; - -template <> struct SharedMemory -{ - __device__ bool *getPointer() - { - extern __shared__ bool s_bool[]; - return s_bool; - } -}; - -template <> struct SharedMemory -{ - __device__ float *getPointer() - { - extern __shared__ float s_float[]; - return s_float; - } -}; - -template <> struct SharedMemory -{ - __device__ double *getPointer() - { - extern __shared__ double s_double[]; - return s_double; - } -}; - -#endif //_SHAREDMEM_H_ diff --git a/cpp/0_Introduction/simpleTemplates/simpleTemplates.cu b/cpp/0_Introduction/simpleTemplates/simpleTemplates.cu deleted file mode 100644 index 8a7dae30..00000000 --- a/cpp/0_Introduction/simpleTemplates/simpleTemplates.cu +++ /dev/null @@ -1,265 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* This sample is a templatized version of the template project. - * It also shows how to correctly templatize dynamically allocated shared - * memory arrays. - * Host code. - */ - -// System includes -#include -#include -#include -#include - -// CUDA runtime -#include - -// helper functions and utilities to work with CUDA -#include -#include - -#ifndef MAX -#define MAX(a, b) (a > b ? a : b) -#endif - -// includes, kernels -#include "sharedmem.cuh" - -int g_TotalFailures = 0; - -//////////////////////////////////////////////////////////////////////////////// -//! Simple test kernel for device functionality -//! @param g_idata input data in global memory -//! @param g_odata output data in global memory -//////////////////////////////////////////////////////////////////////////////// -template __global__ void testKernel(T *g_idata, T *g_odata) -{ - // Shared mem size is determined by the host app at run time - SharedMemory smem; - T *sdata = smem.getPointer(); - - // access thread id - const unsigned int tid = threadIdx.x; - // access number of threads in this block - const unsigned int num_threads = blockDim.x; - - // read in input data from global memory - sdata[tid] = g_idata[tid]; - __syncthreads(); - - // perform some computations - sdata[tid] = (T)num_threads * sdata[tid]; - __syncthreads(); - - // write data to global memory - g_odata[tid] = sdata[tid]; -} - -//////////////////////////////////////////////////////////////////////////////// -// declaration, forward -template void runTest(int argc, char **argv, int len); - -template void computeGold(T *reference, T *idata, const unsigned int len) -{ - const T T_len = static_cast(len); - - for (unsigned int i = 0; i < len; ++i) { - reference[i] = idata[i] * T_len; - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - printf("> runTest\n"); - runTest(argc, argv, 32); - printf("> runTest\n"); - runTest(argc, argv, 64); - - printf("\n[simpleTemplates] -> Test Results: %d Failures\n", g_TotalFailures); - - exit(g_TotalFailures == 0 ? EXIT_SUCCESS : EXIT_FAILURE); -} - -// To completely templatize runTest (below) with cutil, we need to use -// template specialization to wrap up CUTIL's array comparison and file writing -// functions for different types. - -// Here's the generic wrapper for cutCompare* -template class ArrayComparator -{ -public: - bool compare(const T *reference, T *data, unsigned int len) - { - fprintf(stderr, "Error: no comparison function implemented for this type\n"); - return false; - } -}; - -// Here's the specialization for ints: -template <> class ArrayComparator -{ -public: - bool compare(const int *reference, int *data, unsigned int len) - { - return compareData(reference, data, len, 0.15f, 0.0f); - } -}; - -// Here's the specialization for floats: -template <> class ArrayComparator -{ -public: - bool compare(const float *reference, float *data, unsigned int len) - { - return compareData(reference, data, len, 0.15f, 0.15f); - } -}; - -// Here's the generic wrapper for cutWriteFile* -template class ArrayFileWriter -{ -public: - bool write(const char *filename, T *data, unsigned int len, float epsilon) - { - fprintf(stderr, "Error: no file write function implemented for this type\n"); - return false; - } -}; - -// Here's the specialization for ints: -template <> class ArrayFileWriter -{ -public: - bool write(const char *filename, int *data, unsigned int len, float epsilon) - { - return sdkWriteFile(filename, data, len, epsilon, false); - } -}; - -// Here's the specialization for floats: -template <> class ArrayFileWriter -{ -public: - bool write(const char *filename, float *data, unsigned int len, float epsilon) - { - return sdkWriteFile(filename, data, len, epsilon, false); - } -}; - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUDA -//////////////////////////////////////////////////////////////////////////////// -template void runTest(int argc, char **argv, int len) -{ - int devID; - cudaDeviceProp deviceProps; - - devID = findCudaDevice(argc, (const char **)argv); - - // get number of SMs on this GPU - checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID)); - printf("CUDA device [%s] has %d Multi-Processors\n", deviceProps.name, deviceProps.multiProcessorCount); - - // create and start timer - StopWatchInterface *timer = NULL; - sdkCreateTimer(&timer); - - // start the timer - sdkStartTimer(&timer); - - unsigned int num_threads = len; - unsigned int mem_size = sizeof(float) * num_threads; - - // allocate host memory - T *h_idata = (T *)malloc(mem_size); - - // initialize the memory - for (unsigned int i = 0; i < num_threads; ++i) { - h_idata[i] = (T)i; - } - - // allocate device memory - T *d_idata; - checkCudaErrors(cudaMalloc((void **)&d_idata, mem_size)); - // copy host memory to device - checkCudaErrors(cudaMemcpy(d_idata, h_idata, mem_size, cudaMemcpyHostToDevice)); - - // allocate device memory for result - T *d_odata; - checkCudaErrors(cudaMalloc((void **)&d_odata, mem_size)); - - // setup execution parameters - dim3 grid(1, 1, 1); - dim3 threads(num_threads, 1, 1); - - // execute the kernel - testKernel<<>>(d_idata, d_odata); - - // check if kernel execution generated and error - getLastCudaError("Kernel execution failed"); - - // allocate mem for the result on host side - T *h_odata = (T *)malloc(mem_size); - // copy result from device to host - checkCudaErrors(cudaMemcpy(h_odata, d_odata, sizeof(T) * num_threads, cudaMemcpyDeviceToHost)); - - sdkStopTimer(&timer); - printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer)); - sdkDeleteTimer(&timer); - - // compute reference solution - T *reference = (T *)malloc(mem_size); - computeGold(reference, h_idata, num_threads); - - ArrayComparator comparator; - ArrayFileWriter writer; - - // check result - if (checkCmdLineFlag(argc, (const char **)argv, "regression")) { - // write file for regression test - writer.write("./data/regression.dat", h_odata, num_threads, 0.0f); - } - else { - // custom output handling when no regression test running - // in this case check if the result is equivalent to the expected solution - bool res = comparator.compare(reference, h_odata, num_threads); - printf("Compare %s\n\n", (1 == res) ? "OK" : "MISMATCH"); - g_TotalFailures += (1 != res); - } - - // cleanup memory - free(h_idata); - free(h_odata); - free(reference); - checkCudaErrors(cudaFree(d_idata)); - checkCudaErrors(cudaFree(d_odata)); -} diff --git a/cpp/0_Introduction/simpleTexture/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleTexture/.vscode/extensions.json b/cpp/0_Introduction/simpleTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleTexture/CMakeLists.txt b/cpp/0_Introduction/simpleTexture/CMakeLists.txt index d6a37b65..1b19150e 100644 --- a/cpp/0_Introduction/simpleTexture/CMakeLists.txt +++ b/cpp/0_Introduction/simpleTexture/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleTexture add_executable(simpleTexture simpleTexture.cu) target_compile_options(simpleTexture PRIVATE $<$:--extended-lambda>) target_compile_features(simpleTexture PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - # Copy data files to output directory add_custom_command(TARGET simpleTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -43,6 +35,5 @@ add_custom_command(TARGET simpleTexture POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/ ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleTexture3D/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleTexture3D/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleTexture3D/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleTexture3D/.vscode/extensions.json b/cpp/0_Introduction/simpleTexture3D/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleTexture3D/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleTexture3D/CMakeLists.txt b/cpp/0_Introduction/simpleTexture3D/CMakeLists.txt index f6baaf28..a9306d75 100644 --- a/cpp/0_Introduction/simpleTexture3D/CMakeLists.txt +++ b/cpp/0_Introduction/simpleTexture3D/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleTexture3D LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for simpleTexture3D add_executable(simpleTexture3D simpleTexture3D_kernel.cu simpleTexture3D.cpp) - target_compile_options(simpleTexture3D PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleTexture3D PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleTexture3D PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleTexture3D PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(simpleTexture3D ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to output directory add_custom_command(TARGET simpleTexture3D POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR} ) - - if(WIN32) + if(WIN32) target_link_libraries(simpleTexture3D ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET simpleTexture3D POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET simpleTexture3D POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'simpleTexture3D'") endif() else() message(STATUS "OpenGL not found - will not build sample 'simpleTexture3D'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/simpleTextureDrv/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleTextureDrv/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleTextureDrv/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleTextureDrv/.vscode/extensions.json b/cpp/0_Introduction/simpleTextureDrv/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleTextureDrv/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleTextureDrv/CMakeLists.txt b/cpp/0_Introduction/simpleTextureDrv/CMakeLists.txt index e9e36b02..589d76a9 100644 --- a/cpp/0_Introduction/simpleTextureDrv/CMakeLists.txt +++ b/cpp/0_Introduction/simpleTextureDrv/CMakeLists.txt @@ -1,33 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleTextureDrv LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleTextureDrv add_executable(simpleTextureDrv simpleTextureDrv.cpp) target_compile_options(simpleTextureDrv PRIVATE $<$:--extended-lambda>) target_compile_features(simpleTextureDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleTextureDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) target_include_directories(simpleTextureDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -55,7 +48,7 @@ endif() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -80,6 +73,5 @@ add_custom_target(generate_fatbin_textureDrv ALL DEPENDS ${CUDA_FATBIN_FILE}) # Ensure simpleTextureDrv depends on the fatbin add_dependencies(simpleTextureDrv generate_fatbin_textureDrv) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/extensions.json b/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleVoteIntrinsics/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleVoteIntrinsics/CMakeLists.txt b/cpp/0_Introduction/simpleVoteIntrinsics/CMakeLists.txt index 11fa110f..bbba4248 100644 --- a/cpp/0_Introduction/simpleVoteIntrinsics/CMakeLists.txt +++ b/cpp/0_Introduction/simpleVoteIntrinsics/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleVoteIntrinsics LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleVoteIntrinsics add_executable(simpleVoteIntrinsics simpleVoteIntrinsics.cu) target_compile_options(simpleVoteIntrinsics PRIVATE $<$:--extended-lambda>) target_compile_features(simpleVoteIntrinsics PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleVoteIntrinsics PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleZeroCopy/.vscode/c_cpp_properties.json b/cpp/0_Introduction/simpleZeroCopy/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/simpleZeroCopy/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/simpleZeroCopy/.vscode/extensions.json b/cpp/0_Introduction/simpleZeroCopy/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/simpleZeroCopy/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/simpleZeroCopy/CMakeLists.txt b/cpp/0_Introduction/simpleZeroCopy/CMakeLists.txt index 536271db..21a75057 100644 --- a/cpp/0_Introduction/simpleZeroCopy/CMakeLists.txt +++ b/cpp/0_Introduction/simpleZeroCopy/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleZeroCopy LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for simpleZeroCopy add_executable(simpleZeroCopy simpleZeroCopy.cu) target_compile_options(simpleZeroCopy PRIVATE $<$:--extended-lambda>) target_compile_features(simpleZeroCopy PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleZeroCopy PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/systemWideAtomics/.vscode/c_cpp_properties.json b/cpp/0_Introduction/systemWideAtomics/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/systemWideAtomics/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/systemWideAtomics/.vscode/extensions.json b/cpp/0_Introduction/systemWideAtomics/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/systemWideAtomics/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/systemWideAtomics/CMakeLists.txt b/cpp/0_Introduction/systemWideAtomics/CMakeLists.txt index 7e37ff90..cbd45e48 100644 --- a/cpp/0_Introduction/systemWideAtomics/CMakeLists.txt +++ b/cpp/0_Introduction/systemWideAtomics/CMakeLists.txt @@ -1,42 +1,40 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(systemWideAtomics LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) if(ENABLE_CUDA_DEBUG) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() -# Include directories and libraries include_directories(../../../Common) if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample systemWideAtomics - not supported on aarch64") else() if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file - # Add target for systemWideAtomics add_executable(systemWideAtomics systemWideAtomics.cu) target_compile_options(systemWideAtomics PRIVATE $<$:--extended-lambda>) target_compile_features(systemWideAtomics PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(systemWideAtomics PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - else() message(STATUS "Will not build sample systemWideAtomics - requires Linux OS") endif() endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/template/.vscode/c_cpp_properties.json b/cpp/0_Introduction/template/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/template/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/template/.vscode/extensions.json b/cpp/0_Introduction/template/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/template/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/template/CMakeLists.txt b/cpp/0_Introduction/template/CMakeLists.txt deleted file mode 100644 index 42e614e9..00000000 --- a/cpp/0_Introduction/template/CMakeLists.txt +++ /dev/null @@ -1,35 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(template LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for template -add_executable(template template.cu template_cpu.cpp) - -target_compile_options(template PRIVATE $<$:--extended-lambda>) - -target_compile_features(template PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(template PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/0_Introduction/template/README.md b/cpp/0_Introduction/template/README.md deleted file mode 100644 index 4b747926..00000000 --- a/cpp/0_Introduction/template/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# template - Template - -## Description - -A trivial template project that can be used as a starting point to create new CUDA projects. - -## Key Concepts - -Device Memory Allocation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaMemcpy, cudaFree - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) diff --git a/cpp/0_Introduction/template/template.cu b/cpp/0_Introduction/template/template.cu deleted file mode 100644 index ba0164f5..00000000 --- a/cpp/0_Introduction/template/template.cu +++ /dev/null @@ -1,165 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* Template project which demonstrates the basics on how to setup a project - * example application. - * Host code. - */ - -// includes, system -#include -#include -#include -#include - -// includes CUDA -#include - -// includes, project -#include -#include // helper functions for SDK examples - -//////////////////////////////////////////////////////////////////////////////// -// declaration, forward -void runTest(int argc, char **argv); - -extern "C" void computeGold(float *reference, float *idata, const unsigned int len); - -//////////////////////////////////////////////////////////////////////////////// -//! Simple test kernel for device functionality -//! @param g_idata input data in global memory -//! @param g_odata output data in global memory -//////////////////////////////////////////////////////////////////////////////// -__global__ void testKernel(float *g_idata, float *g_odata) -{ - // shared memory - // the size is determined by the host application - extern __shared__ float sdata[]; - - // access thread id - const unsigned int tid = threadIdx.x; - // access number of threads in this block - const unsigned int num_threads = blockDim.x; - - // read in input data from global memory - sdata[tid] = g_idata[tid]; - __syncthreads(); - - // perform some computations - sdata[tid] = (float)num_threads * sdata[tid]; - __syncthreads(); - - // write data to global memory - g_odata[tid] = sdata[tid]; -} - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) { runTest(argc, argv); } - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUDA -//////////////////////////////////////////////////////////////////////////////// -void runTest(int argc, char **argv) -{ - bool bTestResult = true; - - printf("%s Starting...\n\n", argv[0]); - - // use command-line specified CUDA device, otherwise use device with highest - // Gflops/s - int devID = findCudaDevice(argc, (const char **)argv); - - StopWatchInterface *timer = 0; - sdkCreateTimer(&timer); - sdkStartTimer(&timer); - - unsigned int num_threads = 32; - unsigned int mem_size = sizeof(float) * num_threads; - - // allocate host memory - float *h_idata = (float *)malloc(mem_size); - - // initalize the memory - for (unsigned int i = 0; i < num_threads; ++i) { - h_idata[i] = (float)i; - } - - // allocate device memory - float *d_idata; - checkCudaErrors(cudaMalloc((void **)&d_idata, mem_size)); - // copy host memory to device - checkCudaErrors(cudaMemcpy(d_idata, h_idata, mem_size, cudaMemcpyHostToDevice)); - - // allocate device memory for result - float *d_odata; - checkCudaErrors(cudaMalloc((void **)&d_odata, mem_size)); - - // setup execution parameters - dim3 grid(1, 1, 1); - dim3 threads(num_threads, 1, 1); - - // execute the kernel - testKernel<<>>(d_idata, d_odata); - - // check if kernel execution generated and error - getLastCudaError("Kernel execution failed"); - - // allocate mem for the result on host side - float *h_odata = (float *)malloc(mem_size); - // copy result from device to host - checkCudaErrors(cudaMemcpy(h_odata, d_odata, sizeof(float) * num_threads, cudaMemcpyDeviceToHost)); - - sdkStopTimer(&timer); - printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer)); - sdkDeleteTimer(&timer); - - // compute reference solution - float *reference = (float *)malloc(mem_size); - computeGold(reference, h_idata, num_threads); - - // check result - if (checkCmdLineFlag(argc, (const char **)argv, "regression")) { - // write file for regression test - sdkWriteFile("./data/regression.dat", h_odata, num_threads, 0.0f, false); - } - else { - // custom output handling when no regression test running - // in this case check if the result is equivalent to the expected solution - bTestResult = compareData(reference, h_odata, num_threads, 0.0f, 0.0f); - } - - // cleanup memory - free(h_idata); - free(h_odata); - free(reference); - checkCudaErrors(cudaFree(d_idata)); - checkCudaErrors(cudaFree(d_odata)); - - exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/0_Introduction/template/template_cpu.cpp b/cpp/0_Introduction/template/template_cpu.cpp deleted file mode 100644 index 2546d02d..00000000 --- a/cpp/0_Introduction/template/template_cpu.cpp +++ /dev/null @@ -1,45 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// export C interface -extern "C" void computeGold(float *reference, float *idata, const unsigned int len); - -//////////////////////////////////////////////////////////////////////////////// -//! Compute reference data set -//! Each element is multiplied with the number of threads / array length -//! @param reference reference data, computed but preallocated -//! @param idata input data as provided to device -//! @param len number of elements in reference / idata -//////////////////////////////////////////////////////////////////////////////// -void computeGold(float *reference, float *idata, const unsigned int len) -{ - const float f_len = static_cast(len); - - for (unsigned int i = 0; i < len; ++i) { - reference[i] = idata[i] * f_len; - } -} diff --git a/cpp/0_Introduction/vectorAdd/.vscode/c_cpp_properties.json b/cpp/0_Introduction/vectorAdd/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/vectorAdd/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/vectorAdd/.vscode/extensions.json b/cpp/0_Introduction/vectorAdd/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/vectorAdd/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/vectorAdd/CMakeLists.txt b/cpp/0_Introduction/vectorAdd/CMakeLists.txt index b3f03225..a0a4925d 100644 --- a/cpp/0_Introduction/vectorAdd/CMakeLists.txt +++ b/cpp/0_Introduction/vectorAdd/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(vectorAdd LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for vectorAdd add_executable(vectorAdd vectorAdd.cu) target_compile_options(vectorAdd PRIVATE $<$:--extended-lambda>) target_compile_features(vectorAdd PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(vectorAdd PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/vectorAdd/README.md b/cpp/0_Introduction/vectorAdd/README.md index 625adb34..23c2e040 100644 --- a/cpp/0_Introduction/vectorAdd/README.md +++ b/cpp/0_Introduction/vectorAdd/README.md @@ -1,32 +1,82 @@ -# vectorAdd - Vector Addition +# Sample: Vector Addition (Unified Memory) ## Description -This CUDA Runtime API sample is a very basic sample that implements element by element vector addition. It is the same as the sample illustrating Chapter 3 of the programming guide with some additions like error checking. +A first-look CUDA sample: add two float vectors element-wise on the GPU. This version uses **Unified Memory** (`cudaMallocManaged`) so the same pointer is accessible from both host and device, eliminating the need for explicit `cudaMemcpy` calls. The GPU result is verified against a CPU reference computed on the same buffers. + +The sample mirrors the corresponding Unified Memory example in the CUDA Programming Guide and uses the `//unified-memory-begin` / `//unified-memory-end` markers so the guide can extract the snippet directly. + +## What You'll Learn + +- Allocating memory accessible to both host and device with `cudaMallocManaged` +- Launching a 1D CUDA kernel that operates on a Unified Memory buffer with no explicit transfers +- Computing grid dimensions with `cuda::ceil_div` (from ``) +- Synchronizing host execution with device work via `cudaDeviceSynchronize` +- Validating GPU output against a serial CPU reference ## Key Concepts -CUDA Runtime API, Vector Addition +- **Unified Memory** — one pointer for both CPU and GPU; the CUDA runtime migrates pages on demand +- **1D thread indexing** — `threadIdx.x + blockIdx.x * blockDim.x` +- **Bounds checking** in the kernel so the vector length need not be a multiple of the block size -## Supported SM Architectures +## Key APIs -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) +### CUDA Runtime +- `cudaMallocManaged` — allocate Unified Memory accessible to both host and device +- `cudaDeviceSynchronize` — block the host until all submitted device work completes +- `cudaFree` — release a Unified Memory allocation -## Supported OSes +### libcudacxx +- `cuda::ceil_div` — `ceil(a / b)` used to compute the number of blocks; from `` -Linux, Windows +## Requirements -## Supported CPU Architecture +### Hardware +- NVIDIA GPU with Compute Capability 7.5 or higher -x86_64, armv7l +### Software +- CMake 3.20 or newer +- A C++17-capable host compiler -## CUDA APIs involved +## How to Build -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaGetErrorString, cudaFree, cudaGetLastError, cudaMalloc +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. -## Prerequisites +## How to Run -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +### Basic usage (default vector length = 1024) +```bash +./vectorAdd +``` + +### Custom vector length +```bash +./vectorAdd 1000000 +``` + +## Expected Output + +On success: + +```text +Unified Memory: CPU and GPU answers match +``` + +On failure, the first mismatching index and the two diverging values are printed, followed by an error line: + +```text +Index 0 mismatch: 0.123456 != 0.123400 +Unified Memory: Error - CPU and GPU answers do not match +``` + +## Files + +- `vectorAdd.cu` — Unified Memory vector addition implementation (kernel + host driver) +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA C++ Programming Guide — vectorAdd example with Unified Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/intro-to-cuda-cpp.html) -## References (for more details) diff --git a/cpp/0_Introduction/vectorAdd/vectorAdd.cu b/cpp/0_Introduction/vectorAdd/vectorAdd.cu index 38b043ef..b5392c33 100644 --- a/cpp/0_Introduction/vectorAdd/vectorAdd.cu +++ b/cpp/0_Introduction/vectorAdd/vectorAdd.cu @@ -1,4 +1,4 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions @@ -33,164 +33,112 @@ * of the programming guide with some additions like error checking. */ +#include +#include +#include +#include #include - -// For the CUDA runtime routines (prefixed with "cuda_") -#include -#include +#include /** * CUDA Kernel Device code * * Computes the vector addition of A and B into C. The 3 vectors have the same - * number of elements numElements. + * number of elements. This exmple shows Vector addition using Unified memory. */ -__global__ void vectorAdd(const float *A, const float *B, float *C, int numElements) -{ - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < numElements) { - C[i] = A[i] + B[i] + 0.0f; + +__global__ void vecAdd(float* A, float* B, float* C, int vectorLength) +{ + int workIndex = threadIdx.x + blockIdx.x*blockDim.x; + if(workIndex < vectorLength) + { + C[workIndex] = A[workIndex] + B[workIndex]; } } -/** - * Host main routine - */ -int main(void) +void initArray(float* A, int length) { - // Error code to check return values for CUDA calls - cudaError_t err = cudaSuccess; - - // Print the vector length to be used, and compute its size - int numElements = 50000; - size_t size = numElements * sizeof(float); - printf("[Vector addition of %d elements]\n", numElements); - - // Allocate the host input vector A - float *h_A = (float *)malloc(size); - - // Allocate the host input vector B - float *h_B = (float *)malloc(size); - - // Allocate the host output vector C - float *h_C = (float *)malloc(size); - - // Verify that allocations succeeded - if (h_A == NULL || h_B == NULL || h_C == NULL) { - fprintf(stderr, "Failed to allocate host vectors!\n"); - exit(EXIT_FAILURE); + std::srand(std::time({})); + for(int i=0; i>>(d_A, d_B, d_C, numElements); - err = cudaGetLastError(); - - if (err != cudaSuccess) { - fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err)); - exit(EXIT_FAILURE); - } - - // Copy the device result vector in device memory to the host result vector - // in host memory. - printf("Copy output data from the CUDA device to the host memory\n"); - err = cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost); - - if (err != cudaSuccess) { - fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!\n", cudaGetErrorString(err)); - exit(EXIT_FAILURE); - } - - // Verify that the result vector is correct - for (int i = 0; i < numElements; ++i) { - if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5) { - fprintf(stderr, "Result verification failed at element %d!\n", i); - exit(EXIT_FAILURE); +bool vectorApproximatelyEqual(float* A, float* B, int length, float epsilon=0.00001) +{ + for(int i=0; i epsilon) + { + printf("Index %d mismatch: %f != %f", i, A[i], B[i]); + return false; } } + return true; +} - printf("Test PASSED\n"); - - // Free device global memory - err = cudaFree(d_A); - - if (err != cudaSuccess) { - fprintf(stderr, "Failed to free device vector A (error code %s)!\n", cudaGetErrorString(err)); - exit(EXIT_FAILURE); +int main(int argc, char** argv) +{ + int vectorLength = 1024; + if(argc >=2) + { + vectorLength = std::atoi(argv[1]); } - err = cudaFree(d_B); + //unified-memory-example-begin - if (err != cudaSuccess) { - fprintf(stderr, "Failed to free device vector B (error code %s)!\n", cudaGetErrorString(err)); - exit(EXIT_FAILURE); + // Pointers to memory vectors + float* A = nullptr; + float* B = nullptr; + float* C = nullptr; + float* comparisonResult = (float*)malloc(vectorLength*sizeof(float)); + + // Use unified memory to allocate buffers + cudaMallocManaged(&A, vectorLength*sizeof(float)); + cudaMallocManaged(&B, vectorLength*sizeof(float)); + cudaMallocManaged(&C, vectorLength*sizeof(float)); + + // Initialize vectors on the host + initArray(A, vectorLength); + initArray(B, vectorLength); + + // Launch the kernel. Unified memory will make sure A, B, and C are + // accessible to the GPU + int threads = 256; + int blocks = cuda::ceil_div(vectorLength, threads); + vecAdd<<>>(A, B, C, vectorLength); + // Wait for the kernel to complete execution + cudaDeviceSynchronize(); + + // Perform computation serially on CPU for comparison + serialVecAdd(A, B, comparisonResult, vectorLength); + + // Confirm that CPU and GPU got the same answer + if(vectorApproximatelyEqual(C, comparisonResult, vectorLength)) + { + printf("Unified Memory: CPU and GPU answers match\n"); + } + else + { + printf("Unified Memory: Error - CPU and GPU answers do not match\n"); } - err = cudaFree(d_C); + // Clean Up + cudaFree(A); + cudaFree(B); + cudaFree(C); + free(comparisonResult); - if (err != cudaSuccess) { - fprintf(stderr, "Failed to free device vector C (error code %s)!\n", cudaGetErrorString(err)); - exit(EXIT_FAILURE); - } + //unified-memory-example-end - // Free host memory - free(h_A); - free(h_B); - free(h_C); - - printf("Done\n"); return 0; } diff --git a/cpp/0_Introduction/vectorAddDrv/.vscode/c_cpp_properties.json b/cpp/0_Introduction/vectorAddDrv/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/vectorAddDrv/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/vectorAddDrv/.vscode/extensions.json b/cpp/0_Introduction/vectorAddDrv/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/vectorAddDrv/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/vectorAddDrv/CMakeLists.txt b/cpp/0_Introduction/vectorAddDrv/CMakeLists.txt index a08c02c9..8c9bac4c 100644 --- a/cpp/0_Introduction/vectorAddDrv/CMakeLists.txt +++ b/cpp/0_Introduction/vectorAddDrv/CMakeLists.txt @@ -1,33 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(vectorAddDrv LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for vectorAddDrv add_executable(vectorAddDrv vectorAddDrv.cpp) target_compile_options(vectorAddDrv PRIVATE $<$:--extended-lambda>) target_compile_features(vectorAddDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(vectorAddDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) target_include_directories(vectorAddDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -55,7 +48,7 @@ endif() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -66,6 +59,5 @@ add_custom_target(generate_fatbin_vectorAdd ALL DEPENDS ${CUDA_FATBIN_FILE}) # Ensure matrixMulDrv depends on the fatbin add_dependencies(vectorAddDrv generate_fatbin_vectorAdd) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/vectorAddMMAP/.vscode/c_cpp_properties.json b/cpp/0_Introduction/vectorAddMMAP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/vectorAddMMAP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/vectorAddMMAP/.vscode/extensions.json b/cpp/0_Introduction/vectorAddMMAP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/vectorAddMMAP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/vectorAddMMAP/CMakeLists.txt b/cpp/0_Introduction/vectorAddMMAP/CMakeLists.txt index 8989d1c3..851bcd84 100644 --- a/cpp/0_Introduction/vectorAddMMAP/CMakeLists.txt +++ b/cpp/0_Introduction/vectorAddMMAP/CMakeLists.txt @@ -1,26 +1,20 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(vectorAddMMAP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for vectorAddMMAP if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample vectorAddMMAP - not supported on aarch64") else() @@ -30,7 +24,6 @@ else() target_compile_features(vectorAddMMAP PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(vectorAddMMAP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) target_include_directories(vectorAddMMAP PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -50,7 +43,7 @@ else() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -62,6 +55,5 @@ else() add_dependencies(vectorAddMMAP generate_fatbin_vectorAddMMAP) endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/c_cpp_properties.json b/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/extensions.json b/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/0_Introduction/vectorAdd_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/0_Introduction/vectorAdd_nvrtc/CMakeLists.txt b/cpp/0_Introduction/vectorAdd_nvrtc/CMakeLists.txt index 75350c96..a2e3bbf5 100644 --- a/cpp/0_Introduction/vectorAdd_nvrtc/CMakeLists.txt +++ b/cpp/0_Introduction/vectorAdd_nvrtc/CMakeLists.txt @@ -1,25 +1,20 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(vectorAdd_nvrtc LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file # Add sample target executable add_executable(vectorAdd_nvrtc vectorAdd.cpp) @@ -38,6 +33,5 @@ add_custom_command(TARGET vectorAdd_nvrtc POST_BUILD ${CMAKE_CURRENT_SOURCE_DIR}/vectorAdd_kernel.cu ${CMAKE_CURRENT_BINARY_DIR} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/1_Utilities/deviceQuery/.vscode/c_cpp_properties.json b/cpp/1_Utilities/deviceQuery/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/1_Utilities/deviceQuery/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/1_Utilities/deviceQuery/.vscode/extensions.json b/cpp/1_Utilities/deviceQuery/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/1_Utilities/deviceQuery/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/1_Utilities/deviceQuery/CMakeLists.txt b/cpp/1_Utilities/deviceQuery/CMakeLists.txt index 110d95cd..36979d80 100644 --- a/cpp/1_Utilities/deviceQuery/CMakeLists.txt +++ b/cpp/1_Utilities/deviceQuery/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(deviceQuery LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for deviceQuery add_executable(deviceQuery deviceQuery.cpp) target_compile_options(deviceQuery PRIVATE $<$:--extended-lambda>) target_compile_features(deviceQuery PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(deviceQuery PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(deviceQuery PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -37,6 +29,5 @@ target_link_libraries(deviceQuery PUBLIC CUDA::cudart ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/1_Utilities/deviceQueryDrv/.vscode/c_cpp_properties.json b/cpp/1_Utilities/deviceQueryDrv/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/1_Utilities/deviceQueryDrv/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/1_Utilities/deviceQueryDrv/.vscode/extensions.json b/cpp/1_Utilities/deviceQueryDrv/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/1_Utilities/deviceQueryDrv/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/1_Utilities/deviceQueryDrv/CMakeLists.txt b/cpp/1_Utilities/deviceQueryDrv/CMakeLists.txt index ecb57221..9b9c8f9f 100644 --- a/cpp/1_Utilities/deviceQueryDrv/CMakeLists.txt +++ b/cpp/1_Utilities/deviceQueryDrv/CMakeLists.txt @@ -1,33 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(deviceQueryDrv LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for deviceQueryDrv add_executable(deviceQueryDrv deviceQueryDrv.cpp) target_compile_options(deviceQueryDrv PRIVATE $<$:--extended-lambda>) target_compile_features(deviceQueryDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(deviceQueryDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) target_include_directories(deviceQueryDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -36,6 +29,5 @@ target_link_libraries(deviceQueryDrv PUBLIC CUDA::cuda_driver ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/1_Utilities/topologyQuery/.vscode/c_cpp_properties.json b/cpp/1_Utilities/topologyQuery/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/1_Utilities/topologyQuery/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/1_Utilities/topologyQuery/.vscode/extensions.json b/cpp/1_Utilities/topologyQuery/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/1_Utilities/topologyQuery/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/1_Utilities/topologyQuery/CMakeLists.txt b/cpp/1_Utilities/topologyQuery/CMakeLists.txt index 7d83b778..d7c59e81 100644 --- a/cpp/1_Utilities/topologyQuery/CMakeLists.txt +++ b/cpp/1_Utilities/topologyQuery/CMakeLists.txt @@ -1,38 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(topologyQuery LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample topologyQuery - not supported on aarch64") else() - # Source file - # Add target for topologyQuery add_executable(topologyQuery topologyQuery.cu) target_compile_options(topologyQuery PRIVATE $<$:--extended-lambda>) target_compile_features(topologyQuery PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(topologyQuery PROPERTIES CUDA_SEPARABLE_COMPILATION ON) endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/CMakeLists.txt index 63cceff7..5e8c5b96 100644 --- a/cpp/2_Concepts_and_Techniques/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/CMakeLists.txt @@ -14,7 +14,6 @@ add_subdirectory(eigenvalues) add_subdirectory(histogram) add_subdirectory(imageDenoising) add_subdirectory(inlinePTX) -add_subdirectory(inlinePTX_nvrtc) add_subdirectory(interval) add_subdirectory(particles) add_subdirectory(radixSortThrust) diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/CMakeLists.txt index 4db89334..e41969a2 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/CMakeLists.txt @@ -1,54 +1,40 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(EGLStream_CUDA_CrossGPU LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(EGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${EGL_FOUND}) - # Add target for EGLStream_CUDA_CrossGPU - add_executable(EGLStream_CUDA_CrossGPU cuda_consumer.cpp cuda_producer.cpp eglstrm_common.cpp kernel.cu main.cpp) - - target_compile_options(EGLStream_CUDA_CrossGPU PRIVATE $<$:--extended-lambda>) - - target_compile_features(EGLStream_CUDA_CrossGPU PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(EGLStream_CUDA_CrossGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(EGLStream_CUDA_CrossGPU PUBLIC - ${EGL_INCLUDE_DIR} - ${CUDAToolkit_INCLUDE_DIRS} - ) - - target_link_libraries(EGLStream_CUDA_CrossGPU - ${EGL_LIBRARY} - CUDA::cuda_driver - ) + add_executable(EGLStream_CUDA_CrossGPU cuda_consumer.cpp cuda_producer.cpp eglstrm_common.cpp kernel.cu main.cpp) + target_compile_options(EGLStream_CUDA_CrossGPU PRIVATE $<$:--extended-lambda>) + target_compile_features(EGLStream_CUDA_CrossGPU PRIVATE cxx_std_17 cuda_std_17) + target_include_directories(EGLStream_CUDA_CrossGPU PUBLIC + ${EGL_INCLUDE_DIR} + ${CUDAToolkit_INCLUDE_DIRS} + ) + target_link_libraries(EGLStream_CUDA_CrossGPU + ${EGL_LIBRARY} + CUDA::cuda_driver + ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "EGL not found - will not build sample 'EGLStream_CUDA_CrossGPU'") endif() else() message(STATUS "Will not build sample EGLStream_CUDA_CrossGPU - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.cpp b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.cpp index 329d41cb..0a3d7a76 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.cpp +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.cpp @@ -114,7 +114,7 @@ int EGLStreamInit(bool isCrossDevice, int isConsumer, EGLNativeFileDescriptorKHR if (egl_device_id >= numDevices) { printf("No CUDA Capable EGL Device found.. Waiving execution\n"); - goto Done; + exit(2); } g_consumerEglDisplay = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, (void *)devices[egl_device_id], NULL); @@ -126,6 +126,16 @@ int EGLStreamInit(bool isCrossDevice, int isConsumer, EGLNativeFileDescriptorKHR eglStatus = eglInitialize(g_consumerEglDisplay, 0, 0); if (!eglStatus) { + // A GPU built without graphics support is CUDA capable and is offered + // as an EGL device, but has no DRM device for EGL to reach it through. + // Any other reason is a real failure. + if (eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_DEVICE_FILE_EXT) == NULL + && eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_RENDER_NODE_FILE_EXT) == NULL) { + printf("This GPU has no graphics support, EGL is unavailable.. Waiving execution\n"); + // 2 reports an unmet hardware requirement, which run_tests.py does + // not count as a failure. + exit(2); + } printf("EGL failed to initialize. \n"); eglStatus = EGL_FALSE; goto Done; @@ -191,7 +201,7 @@ int EGLStreamInit(bool isCrossDevice, int isConsumer, EGLNativeFileDescriptorKHR if (egl_device_id >= numDevices) { printf("No CUDA Capable EGL Device found.. Waiving execution\n"); - goto Done; + exit(2); } g_producerEglDisplay = eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, (void *)devices[egl_device_id], NULL); @@ -203,6 +213,16 @@ int EGLStreamInit(bool isCrossDevice, int isConsumer, EGLNativeFileDescriptorKHR eglStatus = eglInitialize(g_producerEglDisplay, 0, 0); if (!eglStatus) { + // A GPU built without graphics support is CUDA capable and is offered + // as an EGL device, but has no DRM device for EGL to reach it through. + // Any other reason is a real failure. + if (eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_DEVICE_FILE_EXT) == NULL + && eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_RENDER_NODE_FILE_EXT) == NULL) { + printf("This GPU has no graphics support, EGL is unavailable.. Waiving execution\n"); + // 2 reports an unmet hardware requirement, which run_tests.py does + // not count as a failure. + exit(2); + } printf("EGL failed to initialize. \n"); eglStatus = EGL_FALSE; goto Done; diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.h b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.h index 46442d1b..46c8c28b 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.h +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_CrossGPU/eglstrm_common.h @@ -71,6 +71,7 @@ extern bool verbose; T(PFNEGLGETPLATFORMDISPLAYEXTPROC, eglGetPlatformDisplayEXT) \ T(PFNEGLGETSTREAMFILEDESCRIPTORKHRPROC, eglGetStreamFileDescriptorKHR) \ T(PFNEGLQUERYDEVICEATTRIBEXTPROC, eglQueryDeviceAttribEXT) \ + T(PFNEGLQUERYDEVICESTRINGEXTPROC, eglQueryDeviceStringEXT) \ T(PFNEGLCREATESTREAMFROMFILEDESCRIPTORKHRPROC, eglCreateStreamFromFileDescriptorKHR) #define EXTLST_DECL(tx, x) tx x = NULL; diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/CMakeLists.txt index b02abc04..7657d949 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/CMakeLists.txt @@ -1,55 +1,40 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(EGLStream_CUDA_Interop LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(EGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${EGL_FOUND}) - # Add target for EGLStream_CUDA_Interop add_executable(EGLStream_CUDA_Interop cuda_consumer.cpp cuda_producer.cpp eglstrm_common.cpp main.cpp) - target_compile_options(EGLStream_CUDA_Interop PRIVATE $<$:--extended-lambda>) - target_compile_features(EGLStream_CUDA_Interop PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(EGLStream_CUDA_Interop PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(EGLStream_CUDA_Interop PUBLIC ${EGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(EGLStream_CUDA_Interop ${EGL_LIBRARY} CUDA::cuda_driver ) - + include(InstallSamples) + setup_samples_install() else() message(STATUS "EGL not found - will not build sample 'EGLStream_CUDA_Interop'") endif() else() message(STATUS "Will not build sample EGLStream_CUDA_Interop - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.cpp b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.cpp index ac96dfa0..c7b371b3 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.cpp +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.cpp @@ -102,6 +102,14 @@ int EGLStreamInit(int *cuda_device) eglStatus = eglInitialize(g_display, 0, 0); if (!eglStatus) { + // A GPU built without graphics support is CUDA capable and is offered as an + // EGL device, but has no DRM device for EGL to reach it through. Any other + // reason for EGL not to initialize is a real failure. + if (eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_DEVICE_FILE_EXT) == NULL + && eglQueryDeviceStringEXT(devices[egl_device_id], EGL_DRM_RENDER_NODE_FILE_EXT) == NULL) { + printf("This GPU has no graphics support, EGL is unavailable.. Waiving execution\n"); + exit(EXIT_WAIVED); + } printf("EGL failed to initialize. \n"); eglStatus = EGL_FALSE; exit(EXIT_FAILURE); diff --git a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.h b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.h index a86a7b63..1414c72b 100644 --- a/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.h +++ b/cpp/2_Concepts_and_Techniques/EGLStream_CUDA_Interop/eglstrm_common.h @@ -59,6 +59,7 @@ T(PFNEGLQUERYDEVICESEXTPROC, eglQueryDevicesEXT) \ T(PFNEGLGETPLATFORMDISPLAYEXTPROC, eglGetPlatformDisplayEXT) \ T(PFNEGLQUERYDEVICEATTRIBEXTPROC, eglQueryDeviceAttribEXT) \ + T(PFNEGLQUERYDEVICESTRINGEXTPROC, eglQueryDeviceStringEXT) \ T(PFNEGLCREATESTREAMFROMFILEDESCRIPTORKHRPROC, eglCreateStreamFromFileDescriptorKHR) #define eglCreateStreamKHR my_eglCreateStreamKHR @@ -75,6 +76,7 @@ #define eglQueryDevicesEXT my_eglQueryDevicesEXT #define eglGetPlatformDisplayEXT my_eglGetPlatformDisplayEXT #define eglQueryDeviceAttribEXT my_eglQueryDeviceAttribEXT +#define eglQueryDeviceStringEXT my_eglQueryDeviceStringEXT #define EXTLST_DECL(tx, x) tx my_##x = NULL; #define EXTLST_EXTERN(tx, x) extern tx my_##x; diff --git a/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/FunctionPointers/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/FunctionPointers/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/FunctionPointers/CMakeLists.txt index be26e2dc..679d0083 100644 --- a/cpp/2_Concepts_and_Techniques/FunctionPointers/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/FunctionPointers/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(FunctionPointers LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for FunctionPointers add_executable(FunctionPointers FunctionPointers.cpp FunctionPointers_kernels.cu) - target_compile_options(FunctionPointers PRIVATE $<$:--extended-lambda>) - target_compile_features(FunctionPointers PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(FunctionPointers PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(FunctionPointers PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(FunctionPointers ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to output directory add_custom_command(TARGET FunctionPointers POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(FunctionPointers ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET FunctionPointers POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET FunctionPointers POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'FunctionPointers'") endif() else() message(STATUS "OpenGL not found - will not build sample 'FunctionPointers'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/CMakeLists.txt index 191b97ba..4a8cdbf0 100644 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineP/CMakeLists.txt @@ -1,39 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MC_EstimatePiInlineP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MC_EstimatePiInlineP add_executable(MC_EstimatePiInlineP src/main.cpp src/piestimator.cu src/test.cpp) target_compile_options(MC_EstimatePiInlineP PRIVATE $<$:--extended-lambda>) target_compile_features(MC_EstimatePiInlineP PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MC_EstimatePiInlineP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MC_EstimatePiInlineP PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/inc ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/CMakeLists.txt index da2e1cac..20166fa2 100644 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/MC_EstimatePiInlineQ/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MC_EstimatePiInlineQ LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MC_EstimatePiInlineQ add_executable(MC_EstimatePiInlineQ src/main.cpp src/piestimator.cu src/test.cpp) target_compile_options(MC_EstimatePiInlineQ PRIVATE $<$:--extended-lambda>) target_compile_features(MC_EstimatePiInlineQ PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MC_EstimatePiInlineQ PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MC_EstimatePiInlineQ PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/inc ${CUDAToolkit_INCLUDE_DIRS} @@ -38,6 +30,5 @@ target_link_libraries(MC_EstimatePiInlineQ PUBLIC CUDA::curand ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/CMakeLists.txt index 66ff5979..c87dcbce 100644 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/MC_EstimatePiP/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MC_EstimatePiP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MC_EstimatePiP add_executable(MC_EstimatePiP src/main.cpp src/piestimator.cu src/test.cpp) target_compile_options(MC_EstimatePiP PRIVATE $<$:--extended-lambda>) target_compile_features(MC_EstimatePiP PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MC_EstimatePiP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MC_EstimatePiP PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/inc ${CUDAToolkit_INCLUDE_DIRS} @@ -38,6 +30,5 @@ target_link_libraries(MC_EstimatePiP PUBLIC CUDA::curand ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/CMakeLists.txt index bc06b8db..1175c952 100644 --- a/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/MC_EstimatePiQ/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MC_EstimatePiQ LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MC_EstimatePiQ add_executable(MC_EstimatePiQ src/main.cpp src/piestimator.cu src/test.cpp) target_compile_options(MC_EstimatePiQ PRIVATE $<$:--extended-lambda>) target_compile_features(MC_EstimatePiQ PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MC_EstimatePiQ PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MC_EstimatePiQ PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/inc ${CUDAToolkit_INCLUDE_DIRS} @@ -38,6 +30,5 @@ target_link_libraries(MC_EstimatePiQ PUBLIC CUDA::curand ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/CMakeLists.txt index e1d497a3..bd2946a2 100644 --- a/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/MC_SingleAsianOptionP/CMakeLists.txt @@ -1,39 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MC_SingleAsianOptionP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MC_SingleAsianOptionP add_executable(MC_SingleAsianOptionP src/main.cpp src/pricingengine.cu src/test.cpp) target_compile_options(MC_SingleAsianOptionP PRIVATE $<$:--extended-lambda>) target_compile_features(MC_SingleAsianOptionP PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MC_SingleAsianOptionP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MC_SingleAsianOptionP PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/inc ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/README.md b/cpp/2_Concepts_and_Techniques/README.md index 6d3596ca..cc9b34e9 100644 --- a/cpp/2_Concepts_and_Techniques/README.md +++ b/cpp/2_Concepts_and_Techniques/README.md @@ -34,9 +34,6 @@ This sample demonstrates two adaptive image denoising techniques: KNN and NLM, b ### [inlinePTX](./inlinePTX) A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel. -### [inlinePTX_nvrtc](./inlinePTX_nvrtc) -A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel. - ### [interval](./interval) Interval arithmetic operators example. Uses various C++ features (templates and recursion). The recursive mode requires Compute SM 2.0 capabilities. diff --git a/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/boxFilter/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/boxFilter/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/boxFilter/CMakeLists.txt index 8273fa7f..2fcd2735 100644 --- a/cpp/2_Concepts_and_Techniques/boxFilter/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/boxFilter/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(boxFilter LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for boxFilter add_executable(boxFilter boxFilter.cpp boxFilter_cpu.cpp boxFilter_kernel.cu) - target_compile_options(boxFilter PRIVATE $<$:--extended-lambda>) - target_compile_features(boxFilter PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(boxFilter PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(boxFilter PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(boxFilter ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to output directory add_custom_command(TARGET boxFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(boxFilter ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET boxFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET boxFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'boxFilter'") endif() else() message(STATUS "OpenGL not found - will not build sample 'boxFilter'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/convolutionSeparable/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/convolutionSeparable/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/convolutionSeparable/CMakeLists.txt index 5fe32b58..a99cbc50 100644 --- a/cpp/2_Concepts_and_Techniques/convolutionSeparable/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/convolutionSeparable/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(convolutionSeparable LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for convolutionSeparable add_executable(convolutionSeparable convolutionSeparable.cu convolutionSeparable_gold.cpp main.cpp) target_compile_options(convolutionSeparable PRIVATE $<$:--extended-lambda>) target_compile_features(convolutionSeparable PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(convolutionSeparable PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(convolutionSeparable PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/convolutionTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/convolutionTexture/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/convolutionTexture/CMakeLists.txt index a9589288..1f9e1426 100644 --- a/cpp/2_Concepts_and_Techniques/convolutionTexture/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/convolutionTexture/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(convolutionTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for convolutionTexture add_executable(convolutionTexture convolutionTexture.cu convolutionTexture_gold.cpp main.cpp) target_compile_options(convolutionTexture PRIVATE $<$:--extended-lambda>) target_compile_features(convolutionTexture PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(convolutionTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(convolutionTexture PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/dct8x8/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/dct8x8/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/dct8x8/CMakeLists.txt index 4bd93758..2c3fb954 100644 --- a/cpp/2_Concepts_and_Techniques/dct8x8/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/dct8x8/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(dct8x8 LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for dct8x8 add_executable(dct8x8 dct8x8.cu BmpUtil.cpp DCT8x8_Gold.cpp) target_compile_options(dct8x8 PRIVATE $<$:--extended-lambda>) target_compile_features(dct8x8 PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(dct8x8 PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(dct8x8 PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) @@ -54,6 +46,5 @@ add_custom_command(TARGET dct8x8 POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/ ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/eigenvalues/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/eigenvalues/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/eigenvalues/CMakeLists.txt index af6f0355..9994e7c0 100644 --- a/cpp/2_Concepts_and_Techniques/eigenvalues/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/eigenvalues/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(eigenvalues LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for eigenvalues add_executable(eigenvalues bisect_large.cu bisect_small.cu bisect_util.cu gerschgorin.cpp main.cu matlab.cpp) target_compile_options(eigenvalues PRIVATE $<$:--extended-lambda>) target_compile_features(eigenvalues PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(eigenvalues PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - # Copy data files to output directory add_custom_command(TARGET eigenvalues POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -50,6 +42,5 @@ add_custom_command(TARGET eigenvalues POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/ ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/histogram/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/histogram/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/histogram/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/histogram/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/histogram/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/histogram/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/histogram/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/histogram/CMakeLists.txt index e646362a..20c40135 100644 --- a/cpp/2_Concepts_and_Techniques/histogram/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/histogram/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(histogram LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for histogram add_executable(histogram histogram256.cu histogram64.cu histogram_gold.cpp main.cpp) target_compile_options(histogram PRIVATE $<$:--extended-lambda>) target_compile_features(histogram PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(histogram PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(histogram PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/imageDenoising/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/imageDenoising/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/imageDenoising/CMakeLists.txt index aeab370b..7457c377 100644 --- a/cpp/2_Concepts_and_Techniques/imageDenoising/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/imageDenoising/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(imageDenoising LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -30,53 +26,40 @@ if(WIN32) endif() endif() - find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for imageDenoising add_executable(imageDenoising bmploader.cpp imageDenoising.cu imageDenoisingGL.cpp) - target_compile_options(imageDenoising PRIVATE $<$:--extended-lambda>) - target_compile_features(imageDenoising PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(imageDenoising PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(imageDenoising PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(imageDenoising ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to the output directory add_custom_command(TARGET imageDenoising POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(imageDenoising ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET imageDenoising POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET imageDenoising POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -84,14 +67,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'imageDenoising'") endif() else() message(STATUS "OpenGL not found - will not build sample 'imageDenoising'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/inlinePTX/CMakeLists.txt index 5ae41446..278e3a62 100644 --- a/cpp/2_Concepts_and_Techniques/inlinePTX/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/inlinePTX/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(inlinePTX LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for inlinePTX add_executable(inlinePTX inlinePTX.cu) target_compile_options(inlinePTX PRIVATE $<$:--extended-lambda>) target_compile_features(inlinePTX PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(inlinePTX PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/CMakeLists.txt deleted file mode 100644 index 0a429aa1..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,45 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(inlinePTX_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for inlinePTX_nvrtc -add_executable(inlinePTX_nvrtc inlinePTX.cpp) - -target_compile_options(inlinePTX_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(inlinePTX_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(inlinePTX_nvrtc PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(inlinePTX_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy clock_kernel.cu to the output directory -add_custom_command(TARGET inlinePTX_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/inlinePTX_kernel.cu ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/README.md b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/README.md deleted file mode 100644 index 593012ef..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# inlinePTX_nvrtc - Using Inline PTX with libNVRTC - -## Description - -A simple test application that demonstrates a new CUDA 4.0 ability to embed PTX in a CUDA kernel. - -## Key Concepts - -Performance Strategies, PTX Assembly, CUDA Driver API, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuCtxSynchronize, cuMemAlloc, cuMemFree, cuModuleGetFunction - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaBlockSize, cudaGridSize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX.cpp b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX.cpp deleted file mode 100644 index 45ab7af8..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX.cpp +++ /dev/null @@ -1,108 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * Demonstration of inline PTX (assembly language) usage in CUDA kernels - */ - -// System includes -#include -#include - -// CUDA runtime -#include -#include - -// helper functions and utilities to work with CUDA -#include - -void sequence_cpu(int *h_ptr, int length) -{ - for (int elemID = 0; elemID < length; elemID++) { - h_ptr[elemID] = elemID % 32; - } -} - -int main(int argc, char **argv) -{ - printf("CUDA inline PTX assembler sample\n"); - - char *cubin, *kernel_file; - size_t cubinSize; - - kernel_file = sdkFindFilePath("inlinePTX_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - - CUmodule module = loadCUBIN(cubin, argc, argv); - - CUfunction kernel_addr; - - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "sequence_gpu")); - - const int N = 1000; - int *h_ptr = (int *)malloc(N * sizeof(int)); - - dim3 cudaBlockSize(256, 1, 1); - dim3 cudaGridSize((N + cudaBlockSize.x - 1) / cudaBlockSize.x, 1, 1); - - CUdeviceptr d_ptr; - checkCudaErrors(cuMemAlloc(&d_ptr, N * sizeof(int))); - - void *arr[] = {(void *)&d_ptr, (void *)&N}; - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - cudaBlockSize.x, - cudaBlockSize.y, - cudaBlockSize.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &arr[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); - - sequence_cpu(h_ptr, N); - - int *h_d_ptr = (int *)malloc(N * sizeof(int)); - checkCudaErrors(cuMemcpyDtoH(h_d_ptr, d_ptr, N * sizeof(int))); - - bool bValid = true; - - for (int i = 0; i < N && bValid; i++) { - if (h_ptr[i] != h_d_ptr[i]) { - bValid = false; - } - } - - printf("Test %s.\n", bValid ? "Successful" : "Failed"); - - checkCudaErrors(cuMemFree(d_ptr)); - - return bValid ? EXIT_SUCCESS : EXIT_FAILURE; -} diff --git a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX_kernel.cu b/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX_kernel.cu deleted file mode 100644 index bbb2a905..00000000 --- a/cpp/2_Concepts_and_Techniques/inlinePTX_nvrtc/inlinePTX_kernel.cu +++ /dev/null @@ -1,40 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -extern "C" __global__ void sequence_gpu(int *d_ptr, int length) -{ - int elemID = blockIdx.x * blockDim.x + threadIdx.x; - - if (elemID < length) { - unsigned int laneid; - - // This command gets the lane ID within the current warp - asm("mov.u32 %0, %%laneid;" : "=r"(laneid)); - - d_ptr[elemID] = laneid; - } -} diff --git a/cpp/2_Concepts_and_Techniques/interval/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/interval/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/interval/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/interval/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/interval/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/interval/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/interval/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/interval/CMakeLists.txt index d0d7e097..f284a2b6 100644 --- a/cpp/2_Concepts_and_Techniques/interval/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/interval/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(interval LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for interval add_executable(interval interval.cu) target_compile_options(interval PRIVATE $<$:--extended-lambda>) target_compile_features(interval PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(interval PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(interval PUBLIC ${CMAKE_CURRENT_SOURCE_DIR} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/particles/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/particles/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/particles/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/particles/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/particles/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/particles/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/particles/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/particles/CMakeLists.txt index f81cb309..8cb54b68 100644 --- a/cpp/2_Concepts_and_Techniques/particles/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/particles/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(particles LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for particles add_executable(particles particleSystem.cpp particleSystem_cuda.cu particles.cpp render_particles.cpp shaders.cpp) - -target_compile_options(particles PRIVATE $<$:--extended-lambda>) - -target_compile_features(particles PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(particles PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(particles PRIVATE $<$:--extended-lambda>) + target_compile_features(particles PRIVATE cxx_std_17 cuda_std_17) target_include_directories(particles PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(particles ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - - # Copy clock_kernel.cu to the output directory + # Copy the data directory to the output directory add_custom_command(TARGET particles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(particles ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET particles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET particles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ target_compile_features(particles PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'particles'") endif() else() message(STATUS "OpenGL not found - will not build sample 'particles'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/particles/particleSystem.cpp b/cpp/2_Concepts_and_Techniques/particles/particleSystem.cpp index 6d88ab35..b8a6d529 100644 --- a/cpp/2_Concepts_and_Techniques/particles/particleSystem.cpp +++ b/cpp/2_Concepts_and_Techniques/particles/particleSystem.cpp @@ -91,7 +91,11 @@ uint ParticleSystem::createVBO(uint size) return vbo; } +// std::lerp (C++20) is pulled into the global namespace by GCC's ; +// only define our own lerp when std::lerp is unavailable to avoid a clash. +#if !defined(__cplusplus) || (__cplusplus < 202002L) inline float lerp(float a, float b, float t) { return a + t * (b - a); } +#endif // create a color ramp void colorRamp(float t, float *r) diff --git a/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/radixSortThrust/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/radixSortThrust/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/radixSortThrust/CMakeLists.txt index 291522c0..7a07d9a6 100644 --- a/cpp/2_Concepts_and_Techniques/radixSortThrust/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/radixSortThrust/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(radixSortThrust LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for radixSortThrust add_executable(radixSortThrust radixSortThrust.cu) target_compile_options(radixSortThrust PRIVATE $<$:--extended-lambda>) target_compile_features(radixSortThrust PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(radixSortThrust PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/reduction/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/reduction/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/reduction/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/reduction/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/reduction/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/reduction/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/reduction/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/reduction/CMakeLists.txt index 4f2c1fa3..7934be63 100644 --- a/cpp/2_Concepts_and_Techniques/reduction/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/reduction/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(reduction LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for reduction add_executable(reduction reduction.cpp reduction_kernel.cu) target_compile_options(reduction PRIVATE $<$:--extended-lambda>) target_compile_features(reduction PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(reduction PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(reduction PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/CMakeLists.txt index 8be01c85..f3a4daf8 100644 --- a/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/reductionMultiBlockCG/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(reductionMultiBlockCG LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for reductionMultiBlockCG add_executable(reductionMultiBlockCG reductionMultiBlockCG.cu) target_compile_options(reductionMultiBlockCG PRIVATE $<$:--extended-lambda>) target_compile_features(reductionMultiBlockCG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(reductionMultiBlockCG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/scalarProd/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/scalarProd/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/scalarProd/CMakeLists.txt index 9defe3d1..02892072 100644 --- a/cpp/2_Concepts_and_Techniques/scalarProd/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/scalarProd/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(scalarProd LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for scalarProd add_executable(scalarProd scalarProd_cpu.cpp scalarProd.cu) target_compile_options(scalarProd PRIVATE $<$:--extended-lambda>) target_compile_features(scalarProd PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(scalarProd PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/scan/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/scan/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/scan/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/scan/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/scan/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/scan/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/scan/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/scan/CMakeLists.txt index a2de5f22..36f16967 100644 --- a/cpp/2_Concepts_and_Techniques/scan/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/scan/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(scan LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for scan add_executable(scan main.cpp scan_gold.cpp scan.cu) target_compile_options(scan PRIVATE $<$:--extended-lambda>) target_compile_features(scan PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(scan PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(scan PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/CMakeLists.txt index 35279fee..d0daa9c3 100644 --- a/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/segmentationTreeThrust/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(segmentationTreeThrust LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for segmentationTreeThrust add_executable(segmentationTreeThrust segmentationTree.cu) target_compile_options(segmentationTreeThrust PRIVATE $<$:--extended-lambda>) target_compile_features(segmentationTreeThrust PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(segmentationTreeThrust PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - # Copy data files to output directory add_custom_command(TARGET segmentationTreeThrust POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -50,6 +42,5 @@ add_custom_command(TARGET segmentationTreeThrust POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/ ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/shfl_scan/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/shfl_scan/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/shfl_scan/CMakeLists.txt index 7292f91c..5224a946 100644 --- a/cpp/2_Concepts_and_Techniques/shfl_scan/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/shfl_scan/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(shfl_scan LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for shfl_scan add_executable(shfl_scan shfl_scan.cu) target_compile_options(shfl_scan PRIVATE $<$:--extended-lambda>) target_compile_features(shfl_scan PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(shfl_scan PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/sortingNetworks/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/sortingNetworks/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/sortingNetworks/CMakeLists.txt index fbefb053..1ab680a5 100644 --- a/cpp/2_Concepts_and_Techniques/sortingNetworks/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/sortingNetworks/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(sortingNetworks LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for sortingNetworks add_executable(sortingNetworks main.cpp bitonicSort.cu sortingNetworks_validate.cpp) target_compile_options(sortingNetworks PRIVATE $<$:--extended-lambda>) target_compile_features(sortingNetworks PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(sortingNetworks PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(sortingNetworks PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/CMakeLists.txt index d57efb15..a3b31afb 100644 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/streamOrderedAllocation/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(streamOrderedAllocation LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for streamOrderedAllocation add_executable(streamOrderedAllocation streamOrderedAllocation.cu) target_compile_options(streamOrderedAllocation PRIVATE $<$:--extended-lambda>) target_compile_features(streamOrderedAllocation PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(streamOrderedAllocation PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/CMakeLists.txt index 7cd4788c..fab170af 100644 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationIPC/CMakeLists.txt @@ -1,27 +1,21 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(streamOrderedAllocationIPC LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") -# Source file -# Add target for streamOrderedAllocationIPC if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample streamOrderedAllocationIPC - not supported on aarch64") else() @@ -31,8 +25,6 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") target_compile_features(streamOrderedAllocationIPC PRIVATE cxx_std_17 cuda_std_17) - set_target_properties(streamOrderedAllocationIPC PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(streamOrderedAllocationIPC PUBLIC CUDA::cuda_driver ) @@ -41,6 +33,5 @@ else() message(STATUS "Will not build sample streamOrderedAllocationIPC - requires Linux OS") endif() -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/CMakeLists.txt index 6aec4f59..c340cf0f 100644 --- a/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/streamOrderedAllocationP2P/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(streamOrderedAllocationP2P LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for streamOrderedAllocationP2P add_executable(streamOrderedAllocationP2P streamOrderedAllocationP2P.cu) target_compile_options(streamOrderedAllocationP2P PRIVATE $<$:--extended-lambda>) target_compile_features(streamOrderedAllocationP2P PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(streamOrderedAllocationP2P PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/threadFenceReduction/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/threadFenceReduction/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/threadFenceReduction/CMakeLists.txt index 8d25b6b8..00dfa8a2 100644 --- a/cpp/2_Concepts_and_Techniques/threadFenceReduction/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/threadFenceReduction/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(threadFenceReduction LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for threadFenceReduction add_executable(threadFenceReduction threadFenceReduction.cu) target_compile_options(threadFenceReduction PRIVATE $<$:--extended-lambda>) target_compile_features(threadFenceReduction PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(threadFenceReduction PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/c_cpp_properties.json b/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/extensions.json b/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/2_Concepts_and_Techniques/threadMigration/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/2_Concepts_and_Techniques/threadMigration/CMakeLists.txt b/cpp/2_Concepts_and_Techniques/threadMigration/CMakeLists.txt index 81b626ff..92e00051 100644 --- a/cpp/2_Concepts_and_Techniques/threadMigration/CMakeLists.txt +++ b/cpp/2_Concepts_and_Techniques/threadMigration/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(threadMigration LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for threadMigration add_executable(threadMigration threadMigration.cpp) target_compile_options(threadMigration PRIVATE $<$:--extended-lambda>) target_compile_features(threadMigration PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(threadMigration PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(threadMigration PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) @@ -60,7 +52,7 @@ endif() add_custom_command( OUTPUT ${CUDA_FATBIN_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets ${GENCODE_FLAGS} -o ${CUDA_FATBIN_FILE} -fatbin ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA fatbin: ${CUDA_FATBIN_FILE}" ) @@ -71,6 +63,5 @@ add_custom_target(generate_fatbin_threadMigration ALL DEPENDS ${CUDA_FATBIN_FILE # Ensure matrixMulDrv depends on the fatbin add_dependencies(threadMigration generate_fatbin_threadMigration) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/CMakeLists.txt b/cpp/3_CUDA_Features/CMakeLists.txt index d7416b24..2cfad1ff 100644 --- a/cpp/3_CUDA_Features/CMakeLists.txt +++ b/cpp/3_CUDA_Features/CMakeLists.txt @@ -9,6 +9,7 @@ add_subdirectory(cdpSimplePrint) add_subdirectory(cdpSimpleQuicksort) add_subdirectory(cudaCompressibleMemory) add_subdirectory(cudaTensorCoreGemm) +add_subdirectory(dmabufInterop) add_subdirectory(dmmaTensorCoreGemm) add_subdirectory(globalToShmemAsyncCopy) add_subdirectory(graphConditionalNodes) @@ -16,6 +17,8 @@ add_subdirectory(graphMemoryFootprint) add_subdirectory(graphMemoryNodes) add_subdirectory(immaTensorCoreGemm) add_subdirectory(jacobiCudaGraphs) +add_subdirectory(localityDomains) +add_subdirectory(localityDomainsDrv) add_subdirectory(memMapIPCDrv) add_subdirectory(newdelete) add_subdirectory(ptxjit) diff --git a/cpp/3_CUDA_Features/README.md b/cpp/3_CUDA_Features/README.md index 74278321..b4c7cc71 100644 --- a/cpp/3_CUDA_Features/README.md +++ b/cpp/3_CUDA_Features/README.md @@ -53,6 +53,12 @@ CUDA sample demonstrating a integer GEMM computation using the Warp Matrix Multi ### [jacobiCudaGraphs](./jacobiCudaGraphs) Demonstrates Instantiated CUDA Graph Update with Jacobi Iterative Method using cudaGraphExecKernelNodeSetParams() and cudaGraphExecUpdate() approach. +### [localityDomains](./localityDomains) +This CUDA Runtime API sample demonstrates APIs for creating localized green contexts and localized memory pool allocations. + +### [localityDomainsDrv](./localityDomainsDrv) +This CUDA Driver API sample demonstrates APIs for creating localized green contexts and creating localized memory allocations. + ### [memMapIPCDrv](./memMapIPCDrv) This CUDA Driver API sample is a very basic sample that demonstrates Inter Process Communication using cuMemMap APIs with one process per GPU for computation. Requires Compute Capability 3.0 or higher and a Linux Operating System, or a Windows Operating System diff --git a/cpp/3_CUDA_Features/StreamPriorities/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/StreamPriorities/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/StreamPriorities/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/StreamPriorities/.vscode/extensions.json b/cpp/3_CUDA_Features/StreamPriorities/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/StreamPriorities/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/StreamPriorities/CMakeLists.txt b/cpp/3_CUDA_Features/StreamPriorities/CMakeLists.txt index d2b347bb..46106f0e 100644 --- a/cpp/3_CUDA_Features/StreamPriorities/CMakeLists.txt +++ b/cpp/3_CUDA_Features/StreamPriorities/CMakeLists.txt @@ -1,42 +1,30 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(StreamPriorities LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file - # Add target for StreamPriorities if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") message(STATUS "Will not build sample streamPriorities - not supported on aarch64") else() add_executable(StreamPriorities StreamPriorities.cu) - target_compile_options(StreamPriorities PRIVATE $<$:--extended-lambda>) - target_compile_features(StreamPriorities PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(StreamPriorities PROPERTIES CUDA_SEPARABLE_COMPILATION ON) + include(InstallSamples) + setup_samples_install() endif() else() message(STATUS "Will not build sample StreamPriorities - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/extensions.json b/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/bf16TensorCoreGemm/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/bf16TensorCoreGemm/CMakeLists.txt b/cpp/3_CUDA_Features/bf16TensorCoreGemm/CMakeLists.txt index 4bec1151..24e56e02 100644 --- a/cpp/3_CUDA_Features/bf16TensorCoreGemm/CMakeLists.txt +++ b/cpp/3_CUDA_Features/bf16TensorCoreGemm/CMakeLists.txt @@ -1,21 +1,24 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(bf16TensorCoreGemm LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) if(ENABLE_CUDA_DEBUG) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -24,16 +27,11 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for bf16TensorCoreGemm add_executable(bf16TensorCoreGemm bf16TensorCoreGemm.cu) target_compile_options(bf16TensorCoreGemm PRIVATE $<$:--extended-lambda>) target_compile_features(bf16TensorCoreGemm PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(bf16TensorCoreGemm PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/extensions.json b/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/binaryPartitionCG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/binaryPartitionCG/CMakeLists.txt b/cpp/3_CUDA_Features/binaryPartitionCG/CMakeLists.txt index 25c46b45..4d6afc53 100644 --- a/cpp/3_CUDA_Features/binaryPartitionCG/CMakeLists.txt +++ b/cpp/3_CUDA_Features/binaryPartitionCG/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(binaryPartitionCG LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for binaryPartitionCG add_executable(binaryPartitionCG binaryPartitionCG.cu) target_compile_options(binaryPartitionCG PRIVATE $<$:--extended-lambda>) target_compile_features(binaryPartitionCG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(binaryPartitionCG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/bindlessTexture/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/bindlessTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/bindlessTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/bindlessTexture/.vscode/extensions.json b/cpp/3_CUDA_Features/bindlessTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/bindlessTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/bindlessTexture/CMakeLists.txt b/cpp/3_CUDA_Features/bindlessTexture/CMakeLists.txt index ee742504..89d36153 100644 --- a/cpp/3_CUDA_Features/bindlessTexture/CMakeLists.txt +++ b/cpp/3_CUDA_Features/bindlessTexture/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(bindlessTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for bindlessTexture add_executable(bindlessTexture bindlessTexture.cpp bindlessTexture_kernel.cu) - -target_compile_options(bindlessTexture PRIVATE $<$:--extended-lambda>) - -target_compile_features(bindlessTexture PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(bindlessTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(bindlessTexture PRIVATE $<$:--extended-lambda>) + target_compile_features(bindlessTexture PRIVATE cxx_std_17 cuda_std_17) target_include_directories(bindlessTexture PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(bindlessTexture ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy clock_kernel.cu to the output directory add_custom_command(TARGET bindlessTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(bindlessTexture ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET bindlessTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET bindlessTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ target_compile_features(bindlessTexture PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'bindlessTexture'") endif() else() message(STATUS "OpenGL not found - will not build sample 'bindlessTexture'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/extensions.json b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/CMakeLists.txt b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/CMakeLists.txt index 54de78a3..4c390b95 100644 --- a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/CMakeLists.txt @@ -1,36 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +# CDP device-links the CUDA device runtime (cudadevrt), which the DriveOS Tegra +# toolkit ships only for the native Tegra archs; drop the desktop archs there. +get_filename_component(_ctk_root "${CMAKE_CUDA_COMPILER}" DIRECTORY) +get_filename_component(_ctk_root "${_ctk_root}" DIRECTORY) +if(EXISTS "${_ctk_root}/targets/aarch64-linux" OR EXISTS "${_ctk_root}/targets/aarch64-qnx") + set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) +endif() +unset(_ctk_root) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cdpAdvancedQuicksort LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -# The aarch64/sbsa_aarch64 CUDA toolkit are support on Tegra since 13.0, so need to check which version of the toolkit is installed -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-linux" _aarch64_linux_ctk) -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-qnx" _aarch64_qnx_ctk) -if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64" AND (NOT _aarch64_linux_ctk EQUAL -1 OR NOT _aarch64_qnx_ctk EQUAL -1)) - set(CMAKE_CUDA_ARCHITECTURES 87 110) -else() - set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 110 120) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -maxrregcount=64") # Limit register usage to 64 for the 'big_bitonicsort kernel -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cdpAdvancedQuicksort add_executable(cdpAdvancedQuicksort cdpAdvancedQuicksort.cu cdpBitonicSort.cu) target_compile_options(cdpAdvancedQuicksort PRIVATE $<$:--extended-lambda>) @@ -39,6 +31,5 @@ target_compile_features(cdpAdvancedQuicksort PRIVATE cxx_std_17 cuda_std_17) set_target_properties(cdpAdvancedQuicksort PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpAdvancedQuicksort.cu b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpAdvancedQuicksort.cu index e18cca90..e1962150 100644 --- a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpAdvancedQuicksort.cu +++ b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpAdvancedQuicksort.cu @@ -137,7 +137,9 @@ template static __device__ void ringbufFree(qsortRingbuf *ringbuf, // and cover the instruction overhead. // //////////////////////////////////////////////////////////////////////////////// -__global__ void qsort_warp(unsigned *indata, +// __launch_bounds__ caps registers to the launch block size so -G (device-debug) builds stay +// within the per-block register limit (big register usage under -G overflows it on Blackwell sm_110+). +__launch_bounds__(QSORT_BLOCKSIZE) __global__ void qsort_warp(unsigned *indata, unsigned *outdata, unsigned int offset, unsigned int len, diff --git a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpBitonicSort.cu b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpBitonicSort.cu index b54c39cb..995be2ca 100644 --- a/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpBitonicSort.cu +++ b/cpp/3_CUDA_Features/cdpAdvancedQuicksort/cdpBitonicSort.cu @@ -272,14 +272,16 @@ static __device__ __forceinline__ void big_bitonicsort_kernel(unsigned *i // KERNELS //////////////////////////////////////////////////////////////////////////////// -__global__ void bitonicsort(unsigned *indata, unsigned *outdata, unsigned int offset, unsigned int len) +// __launch_bounds__ caps registers to the launch block size so -G builds stay within the per-block register limit (Blackwell sm_110+). +__launch_bounds__(BITONICSORT_LEN) __global__ void bitonicsort(unsigned *indata, unsigned *outdata, unsigned int offset, unsigned int len) { // Handle to thread block group cg::thread_block cta = cg::this_thread_block(); bitonicsort_kernel(indata, outdata, offset, len, cta); } -__global__ void +// __launch_bounds__ caps registers to the launch block size so -G builds stay within the per-block register limit (Blackwell sm_110+). +__launch_bounds__(BITONICSORT_LEN) __global__ void big_bitonicsort(unsigned *indata, unsigned *outdata, unsigned *backbuf, unsigned int offset, unsigned int len) { // Handle to thread block group diff --git a/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/extensions.json b/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cdpBezierTessellation/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cdpBezierTessellation/CMakeLists.txt b/cpp/3_CUDA_Features/cdpBezierTessellation/CMakeLists.txt index 3eba90fa..4e54b530 100644 --- a/cpp/3_CUDA_Features/cdpBezierTessellation/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cdpBezierTessellation/CMakeLists.txt @@ -1,35 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +# CDP device-links the CUDA device runtime (cudadevrt), which the DriveOS Tegra +# toolkit ships only for the native Tegra archs; drop the desktop archs there. +get_filename_component(_ctk_root "${CMAKE_CUDA_COMPILER}" DIRECTORY) +get_filename_component(_ctk_root "${_ctk_root}" DIRECTORY) +if(EXISTS "${_ctk_root}/targets/aarch64-linux" OR EXISTS "${_ctk_root}/targets/aarch64-qnx") + set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) +endif() +unset(_ctk_root) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cdpBezierTessellation LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -# The aarch64/sbsa_aarch64 CUDA toolkit are support on Tegra since 13.0, so need to check which version of the toolkit is installed -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-linux" _aarch64_linux_ctk) -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-qnx" _aarch64_qnx_ctk) -if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64" AND (NOT _aarch64_linux_ctk EQUAL -1 OR NOT _aarch64_qnx_ctk EQUAL -1)) - set(CMAKE_CUDA_ARCHITECTURES 87 110) -else() - set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 110 120) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cdpBezierTessellation add_executable(cdpBezierTessellation BezierLineCDP.cu) target_compile_options(cdpBezierTessellation PRIVATE $<$:--extended-lambda>) @@ -38,6 +31,5 @@ target_compile_features(cdpBezierTessellation PRIVATE cxx_std_17 cuda_std_17) set_target_properties(cdpBezierTessellation PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cdpQuadtree/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cdpQuadtree/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cdpQuadtree/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cdpQuadtree/.vscode/extensions.json b/cpp/3_CUDA_Features/cdpQuadtree/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cdpQuadtree/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cdpQuadtree/CMakeLists.txt b/cpp/3_CUDA_Features/cdpQuadtree/CMakeLists.txt index 1ecbfac1..6fb3a0d4 100644 --- a/cpp/3_CUDA_Features/cdpQuadtree/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cdpQuadtree/CMakeLists.txt @@ -1,35 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +# CDP device-links the CUDA device runtime (cudadevrt), which the DriveOS Tegra +# toolkit ships only for the native Tegra archs; drop the desktop archs there. +get_filename_component(_ctk_root "${CMAKE_CUDA_COMPILER}" DIRECTORY) +get_filename_component(_ctk_root "${_ctk_root}" DIRECTORY) +if(EXISTS "${_ctk_root}/targets/aarch64-linux" OR EXISTS "${_ctk_root}/targets/aarch64-qnx") + set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) +endif() +unset(_ctk_root) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cdpQuadtree LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -# The aarch64/sbsa_aarch64 CUDA toolkit are support on Tegra since 13.0, so need to check which version of the toolkit is installed -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-linux" _aarch64_linux_ctk) -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-qnx" _aarch64_qnx_ctk) -if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64" AND (NOT _aarch64_linux_ctk EQUAL -1 OR NOT _aarch64_qnx_ctk EQUAL -1)) - set(CMAKE_CUDA_ARCHITECTURES 87 110) -else() - set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 110 120) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cdpQuadtree add_executable(cdpQuadtree cdpQuadtree.cu) target_compile_options(cdpQuadtree PRIVATE $<$:--extended-lambda>) @@ -38,6 +31,5 @@ target_compile_features(cdpQuadtree PRIVATE cxx_std_17 cuda_std_17) set_target_properties(cdpQuadtree PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cdpQuadtree/cdpQuadtree.cu b/cpp/3_CUDA_Features/cdpQuadtree/cdpQuadtree.cu index 804d2550..e599295c 100644 --- a/cpp/3_CUDA_Features/cdpQuadtree/cdpQuadtree.cu +++ b/cpp/3_CUDA_Features/cdpQuadtree/cdpQuadtree.cu @@ -25,6 +25,8 @@ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ +#include + #include #include #include diff --git a/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/extensions.json b/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cdpSimplePrint/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cdpSimplePrint/CMakeLists.txt b/cpp/3_CUDA_Features/cdpSimplePrint/CMakeLists.txt index 00c42182..dac85278 100644 --- a/cpp/3_CUDA_Features/cdpSimplePrint/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cdpSimplePrint/CMakeLists.txt @@ -1,35 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +# CDP device-links the CUDA device runtime (cudadevrt), which the DriveOS Tegra +# toolkit ships only for the native Tegra archs; drop the desktop archs there. +get_filename_component(_ctk_root "${CMAKE_CUDA_COMPILER}" DIRECTORY) +get_filename_component(_ctk_root "${_ctk_root}" DIRECTORY) +if(EXISTS "${_ctk_root}/targets/aarch64-linux" OR EXISTS "${_ctk_root}/targets/aarch64-qnx") + set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) +endif() +unset(_ctk_root) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cdpSimplePrint LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -# The aarch64/sbsa_aarch64 CUDA toolkit are support on Tegra since 13.0, so need to check which version of the toolkit is installed -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-linux" _aarch64_linux_ctk) -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-qnx" _aarch64_qnx_ctk) -if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64" AND (NOT _aarch64_linux_ctk EQUAL -1 OR NOT _aarch64_qnx_ctk EQUAL -1)) - set(CMAKE_CUDA_ARCHITECTURES 87 110) -else() - set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 110 120) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cdpSimplePrint add_executable(cdpSimplePrint cdpSimplePrint.cu) target_compile_options(cdpSimplePrint PRIVATE $<$:--extended-lambda>) @@ -38,6 +31,5 @@ target_compile_features(cdpSimplePrint PRIVATE cxx_std_17 cuda_std_17) set_target_properties(cdpSimplePrint PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/extensions.json b/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cdpSimpleQuicksort/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cdpSimpleQuicksort/CMakeLists.txt b/cpp/3_CUDA_Features/cdpSimpleQuicksort/CMakeLists.txt index 07ab4d6e..1e8a1412 100644 --- a/cpp/3_CUDA_Features/cdpSimpleQuicksort/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cdpSimpleQuicksort/CMakeLists.txt @@ -1,35 +1,28 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +# CDP device-links the CUDA device runtime (cudadevrt), which the DriveOS Tegra +# toolkit ships only for the native Tegra archs; drop the desktop archs there. +get_filename_component(_ctk_root "${CMAKE_CUDA_COMPILER}" DIRECTORY) +get_filename_component(_ctk_root "${_ctk_root}" DIRECTORY) +if(EXISTS "${_ctk_root}/targets/aarch64-linux" OR EXISTS "${_ctk_root}/targets/aarch64-qnx") + set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) +endif() +unset(_ctk_root) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cdpSimpleQuicksort LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -# The aarch64/sbsa_aarch64 CUDA toolkit are support on Tegra since 13.0, so need to check which version of the toolkit is installed -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-linux" _aarch64_linux_ctk) -string(FIND "${CUDAToolkit_INCLUDE_DIRS}" "aarch64-qnx" _aarch64_qnx_ctk) -if(CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64" AND (NOT _aarch64_linux_ctk EQUAL -1 OR NOT _aarch64_qnx_ctk EQUAL -1)) - set(CMAKE_CUDA_ARCHITECTURES 87 110) -else() - set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 110 120) -endif() - -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cdpSimpleQuicksort add_executable(cdpSimpleQuicksort cdpSimpleQuicksort.cu) target_compile_options(cdpSimpleQuicksort PRIVATE $<$:--extended-lambda>) @@ -38,6 +31,5 @@ target_compile_features(cdpSimpleQuicksort PRIVATE cxx_std_17 cuda_std_17) set_target_properties(cdpSimpleQuicksort PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/extensions.json b/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cudaCompressibleMemory/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cudaCompressibleMemory/CMakeLists.txt b/cpp/3_CUDA_Features/cudaCompressibleMemory/CMakeLists.txt index 20416c6f..704d07e4 100644 --- a/cpp/3_CUDA_Features/cudaCompressibleMemory/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cudaCompressibleMemory/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaCompressibleMemory LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cudaCompressibleMemory add_executable(cudaCompressibleMemory compMalloc.cpp saxpy.cu) target_compile_options(cudaCompressibleMemory PRIVATE $<$:--extended-lambda>) target_compile_features(cudaCompressibleMemory PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cudaCompressibleMemory PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cudaCompressibleMemory PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -37,6 +29,5 @@ target_link_libraries(cudaCompressibleMemory PRIVATE CUDA::cuda_driver ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/extensions.json b/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/cudaTensorCoreGemm/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/cudaTensorCoreGemm/CMakeLists.txt b/cpp/3_CUDA_Features/cudaTensorCoreGemm/CMakeLists.txt index fc5c0894..a4eac4be 100644 --- a/cpp/3_CUDA_Features/cudaTensorCoreGemm/CMakeLists.txt +++ b/cpp/3_CUDA_Features/cudaTensorCoreGemm/CMakeLists.txt @@ -1,35 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaTensorCoreGemm LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cudaTensorCoreGemm add_executable(cudaTensorCoreGemm cudaTensorCoreGemm.cu) target_compile_options(cudaTensorCoreGemm PRIVATE $<$:--extended-lambda>) target_compile_features(cudaTensorCoreGemm PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cudaTensorCoreGemm PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/dmabufInterop/CMakeLists.txt b/cpp/3_CUDA_Features/dmabufInterop/CMakeLists.txt new file mode 100644 index 00000000..de4f1789 --- /dev/null +++ b/cpp/3_CUDA_Features/dmabufInterop/CMakeLists.txt @@ -0,0 +1,36 @@ +cmake_minimum_required(VERSION 3.20) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + +project(dmabufInterop LANGUAGES C CXX CUDA) + +# dma-buf is a Linux-only kernel facility. Skip the sample on all other platforms. +if(NOT (CMAKE_SYSTEM_NAME STREQUAL "Linux")) + message(STATUS "Skipping dmabufInterop: requires Linux (dma-buf is Linux-only). " + "Found ${CMAKE_SYSTEM_NAME}.") + return() +endif() + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + +find_package(CUDAToolkit REQUIRED) + +include_directories(../../../Common) + +add_executable(dmabufInterop dmabufInterop.cu) + +target_compile_options(dmabufInterop PRIVATE $<$:--extended-lambda>) + +target_compile_features(dmabufInterop PRIVATE cxx_std_17 cuda_std_17) + +target_link_libraries(dmabufInterop PRIVATE + CUDA::cuda_driver + CUDA::cudart +) + +include(InstallSamples) +setup_samples_install() diff --git a/cpp/3_CUDA_Features/dmabufInterop/README.md b/cpp/3_CUDA_Features/dmabufInterop/README.md new file mode 100644 index 00000000..263cc59d --- /dev/null +++ b/cpp/3_CUDA_Features/dmabufInterop/README.md @@ -0,0 +1,124 @@ +# Sample: CUDA dma-buf Interoperability + +## Description + +Linux **dma-buf** is the kernel's standard mechanism for sharing a buffer between subsystems by representing it as a file descriptor. This sample shows CUDA acting as both a dma-buf **producer** (taking one of its own allocations and obtaining a dma-buf fd for it with `cuMemGetHandleForAddressRange`) and a dma-buf **consumer** (taking an existing dma-buf fd and turning it into a CUDA-mappable buffer with `cuImportExternalMemory` + `cuExternalMemoryGetMappedBuffer`). A single binary runs three demos in sequence: + +1. **Same-process round-trip** — one CUDA context exports a host allocation and imports it back as external memory. +2. **Cross-process IPC** — a `fork()`-ed child exports an allocation and passes the fd to the parent over a Unix domain socket using `SCM_RIGHTS`. +3. **Cross-GPU sharing across processes** — the same `fork()` + `SCM_RIGHTS` path as demo 2, but the producer child runs on one GPU and the consumer parent imports the fd into a different GPU's context. The sample auto-detects any pair of GPUs whose driver reports support for the dma-buf capability attributes. This demo self-skips when no qualifying pair is present. + +Producer-side allocations always use `cuMemAllocHost`; using a single allocator keeps the producer code identical across all three demos. + +## What You'll Learn + +- Exporting a CUDA host allocation as a Linux dma-buf file descriptor +- Importing a dma-buf fd into CUDA as external memory and mapping it to a device pointer +- Passing a dma-buf fd between processes via a Unix-domain socket using `SCM_RIGHTS` +- Sharing a single buffer between two GPUs in two different processes, with the producer-completes-before-consumer-reads ordering enforced by the existing fork + socket handshake +- Detecting dma-buf platform support at runtime with CUDA device attributes +- Coordinating fork()ed children so output and CUDA work appear in source order rather than the order processes happen to be scheduled + +## Key Concepts + +- **dma-buf** — kernel-side shareable buffer represented as a file descriptor; usable across CUDA, V4L2, DRM/KMS, Vulkan, and any other subsystem that speaks dma-buf +- **Page-aligned export** — `cuMemGetHandleForAddressRange` requires a page-aligned base, so the producer over-allocates by one page and rounds up +- **fd as IPC primitive** — once CUDA has produced a dma-buf fd, the fd is just a POSIX file descriptor and can be sent across `fork()` boundaries via `SCM_RIGHTS` +- **Capability-gated execution** — `CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED` (producer side) and `CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED` (consumer side) determine which paths run on the current hardware +- **Parent-arbitrated synchronisation** — both fork()ed children block on a one-byte "go" from the parent before doing any CUDA work, so output across the three demos appears in source order even when stdout is fully buffered (e.g., over ssh) + +## Key APIs + +### CUDA Driver API +- `cuMemAllocHost` — allocate page-locked host memory that is also a valid CUDA device pointer +- `cuMemGetHandleForAddressRange` (with `CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD`) — export a CUDA allocation as a Linux dma-buf fd +- `cuImportExternalMemory` (with `CU_EXTERNAL_MEMORY_HANDLE_TYPE_DMABUF_FD`) — bring a dma-buf fd into CUDA as external memory +- `cuExternalMemoryGetMappedBuffer` — obtain a `CUdeviceptr` from an imported external memory object +- `cuDeviceGetAttribute` — runtime capability query for `CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED` and `CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED` + +### CUDA Runtime API +- `cudaSetDevice` — pin the runtime to the producer GPU (child process) or the consumer GPU (parent process); both demos that use multiple GPUs do this on each side after the fork +- `cudaGetDeviceCount` — used inside the cross-GPU pair detection to enumerate the available GPUs +- `cudaDeviceSynchronize` — used on the producer side to make sure the kernel that wrote the buffer has retired before the fd is sent to the consumer + +### Linux / POSIX +- `socketpair` + `fork` + `sendmsg`/`recvmsg` with `SCM_RIGHTS` — pass a file descriptor between processes + +## Requirements + +### Hardware +- An NVIDIA GPU whose `CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED` is `1`. The sample queries this at startup and skips entirely on unsupported devices. +- The cross-GPU section additionally needs at least two GPUs in the system, with one reporting `CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED = 1` (producer) and the other reporting `CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED = 1` (consumer). On systems without a qualifying pair the cross-GPU section prints a skip message and the sample continues. + +### Software +- CUDA Toolkit 13.4 or newer +- CMake 3.20 or newer +- Linux. dma-buf is a Linux kernel facility; this sample does not build on Windows or QNX. + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +The sample takes no arguments: + +```bash +./dmabufInterop +``` + +All three sections run in sequence. The cross-GPU section self-skips when the running system has no qualifying GPU pair, so the same binary is appropriate for a single-GPU workstation, a multi-GPU server, or a heterogeneous-GPU platform. + +## Expected Output + +On a system with a single discrete GPU (the cross-GPU section self-skips): + +```text +Device 0: (Compute Capability X.Y) + +Same-process round-trip + producer: allocated 16384 bytes, exported as dma-buf fd 45 + producer: wrote pattern + consumer: imported dma-buf as device pointer + consumer: verified (0 mismatches) + +Cross-process IPC (fork + SCM_RIGHTS) + child: allocated 16384 bytes, exported as dma-buf fd 45 + child: wrote pattern + child: sent fd to parent + parent: received dma-buf fd 45 from child + parent: imported dma-buf as device pointer + parent: verified (0 mismatches) + +Cross-GPU sharing across processes + no GPU pair satisfies the dma-buf capability attributes. Skipping. + +Done +``` + +On a system with two or more dma-buf-capable GPUs, the cross-GPU section runs to completion: + +```text +Cross-GPU sharing across processes + child: on device 1, allocated 16384 bytes, exported as dma-buf fd 53 + child: wrote pattern + child: sent fd to parent + parent: on device 0, received dma-buf fd 4 from child + parent: imported dma-buf as device pointer + parent: verified (0 mismatches) +``` + +If a verification kernel reports any mismatches, the corresponding line ends with `Error (N mismatches)` and the sample exits with a non-zero status. + +## Files + +- `dmabufInterop.cu` — kernels, CUDA export/import helpers, and the three demo functions. Reading this file end-to-end gives the full CUDA dma-buf flow without needing to open anything else. +- `dmabufInterop_helpers.h` — POSIX-only helpers used by the cross-process demos: `sendFd` / `recvFd` for `SCM_RIGHTS`-based file-descriptor passing, and `reapCrossProcessChild` (a `waitpid` wrapper). Split out so the main `.cu` stays focused on CUDA; open this file only if you want to see the SCM_RIGHTS mechanics. +- `README.md` — this file +- `CMakeLists.txt` — build configuration (Linux-only guard, CUDA driver + runtime link) + +## See Also + +- [Linux kernel dma-buf documentation](https://docs.kernel.org/driver-api/dma-buf.html) +- [CUDA External Resource Interoperability](https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__EXTRES__INTEROP.html) +- [`cuMemGetHandleForAddressRange`](https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__MEM.html) diff --git a/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop.cu b/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop.cu new file mode 100644 index 00000000..cced6fd5 --- /dev/null +++ b/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop.cu @@ -0,0 +1,507 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/** + * CUDA dma-buf interoperability. + * + * Three demos exercise CUDA's ability to export a host allocation as a Linux + * dma-buf file descriptor and to import a dma-buf fd back as a CUDA-mappable + * buffer: (1) same-process round-trip, (2) cross-process IPC via fork() and + * SCM_RIGHTS with both sides on one GPU, and (3) cross-GPU sharing across + * processes -- the same fork+SCM_RIGHTS path as demo 2, but the producer + * child runs on one GPU and the consumer parent imports the fd into a + * different GPU's context. Demo 3 self-skips when the system does not have + * two GPUs that satisfy the dma-buf capability attributes. + * + * Producer allocations are always pinned host memory; using a single + * allocator keeps the producer path identical across all three demos. + * + * See README.md for build instructions, expected output, and the full list + * of APIs the sample exercises. + */ + +#include +#include +#include +#include + +#include +#include + +#include "dmabufInterop_helpers.h" + +#define NUM_ELEMENTS 4096 +#define BLOCK_SIZE 256 +#define PATTERN_BASE 0x1000 + +/* Kernel: writes ptr[i] = base + i. */ +__global__ void writePatternKernel(int *ptr, int base, int count) +{ + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < count) { + ptr[idx] = base + idx; + } +} + +/* Kernel: counts elements that do not match base + i. */ +__global__ void verifyPatternKernel(int *ptr, int base, int count, int *errors) +{ + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < count) { + if (ptr[idx] != base + idx) { + atomicAdd(errors, 1); + } + } +} + +/* Align v up to the next multiple of a. */ +static size_t alignUp(size_t v, size_t a) +{ + size_t r = v % a; + return (r == 0) ? v : v + (a - r); +} + +/* Result of a producer-side export: the allocation base (for cuMemFreeHost), + * the page-aligned pointer usable as both host and device pointer, the + * dma-buf fd, and the aligned size. */ +struct DmabufExport +{ + void *allocBase; + void *alignedPtr; + int fd; + size_t size; +}; + +/* Allocate page-aligned pinned host memory and export it as a dma-buf fd. + * cuMemGetHandleForAddressRange requires a page-aligned base, so we allocate + * an extra page of headroom and round up. */ +static DmabufExport exportHostAllocAsDmabuf(size_t size) +{ + long pageSize = sysconf(_SC_PAGESIZE); + + DmabufExport e = {}; + e.size = alignUp(size, (size_t)pageSize); + + cuMemAllocHost(&e.allocBase, e.size + (size_t)pageSize); + e.alignedPtr = (void *)alignUp((size_t)e.allocBase, (size_t)pageSize); + + cuMemGetHandleForAddressRange(&e.fd, + (CUdeviceptr)e.alignedPtr, + e.size, + CU_MEM_RANGE_HANDLE_TYPE_DMA_BUF_FD, + 0ULL); + return e; +} + +/* Import a dma-buf fd into the current CUDA context and obtain a CUdeviceptr + * that kernels can read and write. */ +static void importDmabuf(int fd, size_t size, CUexternalMemory *outExtMem, CUdeviceptr *outDevPtr) +{ + CUDA_EXTERNAL_MEMORY_HANDLE_DESC memDesc = {}; + memDesc.type = CU_EXTERNAL_MEMORY_HANDLE_TYPE_DMABUF_FD; + memDesc.handle.fd = fd; + memDesc.size = size; + cuImportExternalMemory(outExtMem, &memDesc); + + CUDA_EXTERNAL_MEMORY_BUFFER_DESC bufDesc = {}; + bufDesc.size = size; + cuExternalMemoryGetMappedBuffer(outDevPtr, *outExtMem, &bufDesc); +} + +/* Same-process round-trip: one context exports a host allocation as a + * dma-buf fd and imports it back. Producer writes the pattern; consumer + * reads via the imported mapping and verifies. */ +static int runSameProcess(int device) +{ + cudaSetDevice(device); + + DmabufExport e = exportHostAllocAsDmabuf(NUM_ELEMENTS * sizeof(int)); + printf(" producer: allocated %zu bytes, exported as dma-buf fd %d\n", e.size, e.fd); + + int blocks = (NUM_ELEMENTS + BLOCK_SIZE - 1) / BLOCK_SIZE; + writePatternKernel<<>>((int *)e.alignedPtr, PATTERN_BASE, NUM_ELEMENTS); + cudaDeviceSynchronize(); + printf(" producer: wrote pattern\n"); + + CUexternalMemory extMem = NULL; + CUdeviceptr importedPtr = 0; + importDmabuf(e.fd, e.size, &extMem, &importedPtr); + printf(" consumer: imported dma-buf as device pointer\n"); + + int *errors = NULL; + cudaMallocHost(&errors, sizeof(int)); + *errors = 0; + verifyPatternKernel<<>>( + (int *)importedPtr, PATTERN_BASE, NUM_ELEMENTS, errors); + cudaDeviceSynchronize(); + + int rc = (*errors == 0) ? 0 : -1; + printf(" consumer: %s (%d mismatches)\n", rc == 0 ? "verified" : "Error", *errors); + + /* Free up resources. */ + cudaFreeHost(errors); + cuDestroyExternalMemory(extMem); + close(e.fd); + cuMemFreeHost(e.allocBase); + return rc; +} + +/* Cross-process child: producer. Allocates, exports, writes the pattern, + * sends the fd to the parent, waits for a one-byte ack so it does not free + * the underlying memory before the parent finishes reading. + * + * The child waits for a one-byte "go" from the parent before doing any + * CUDA work, so that the parent can serialise output across the same-process + * demo and the two cross-process demos. If the parent closes the socket + * without writing (because some upstream gate failed), the child exits + * cleanly. */ +static int runCrossProcessChild(int device, int sock) +{ + cuInit(0); + cudaSetDevice(device); + + char go = 0; + if (read(sock, &go, 1) <= 0) return 0; + + DmabufExport e = exportHostAllocAsDmabuf(NUM_ELEMENTS * sizeof(int)); + printf(" child: allocated %zu bytes, exported as dma-buf fd %d\n", e.size, e.fd); + + int blocks = (NUM_ELEMENTS + BLOCK_SIZE - 1) / BLOCK_SIZE; + writePatternKernel<<>>((int *)e.alignedPtr, PATTERN_BASE, NUM_ELEMENTS); + cudaDeviceSynchronize(); + printf(" child: wrote pattern\n"); + + sendFd(sock, e.fd); + printf(" child: sent fd to parent\n"); + + char ack = 0; + [[maybe_unused]] ssize_t r = read(sock, &ack, 1); + + /* Free up resources. */ + close(e.fd); + cuMemFreeHost(e.allocBase); + return 0; +} + +/* Cross-process parent: consumer. Signals the child to start producing, + * receives the fd, imports it, verifies the pattern, and acks the child so + * it can clean up. */ +static int runCrossProcessParent(int device, int sock) +{ + cudaSetDevice(device); + + char go = 'G'; + [[maybe_unused]] ssize_t r_go = write(sock, &go, 1); + + /* fd is the dma-buf that the producer child just wrote to; it refers to + * the same underlying memory the child exported, just a different fd + * number in this process. */ + int fd = recvFd(sock); + printf(" parent: received dma-buf fd %d from child\n", fd); + + const size_t size = alignUp(NUM_ELEMENTS * sizeof(int), (size_t)sysconf(_SC_PAGESIZE)); + + CUexternalMemory extMem = NULL; + CUdeviceptr importedPtr = 0; + importDmabuf(fd, size, &extMem, &importedPtr); + printf(" parent: imported dma-buf as device pointer\n"); + + int *errors = NULL; + cudaMallocHost(&errors, sizeof(int)); + *errors = 0; + int blocks = (NUM_ELEMENTS + BLOCK_SIZE - 1) / BLOCK_SIZE; + verifyPatternKernel<<>>( + (int *)importedPtr, PATTERN_BASE, NUM_ELEMENTS, errors); + cudaDeviceSynchronize(); + + int rc = (*errors == 0) ? 0 : -1; + printf(" parent: %s (%d mismatches)\n", rc == 0 ? "verified" : "Error", *errors); + + char ack = 'A'; + [[maybe_unused]] ssize_t r_ack = write(sock, &ack, 1); + + /* Free up resources. */ + cudaFreeHost(errors); + cuDestroyExternalMemory(extMem); + close(fd); + return rc; +} + +/* Cross-process orchestration. The fork() must happen BEFORE either side + * calls cuInit: the CUDA driver does not survive fork-after-init, so child + * and parent each initialise CUDA independently after the fork. main() + * spawns the child here and drives the parent side later. */ +static pid_t spawnCrossProcessChild(int device, int *outParentSocket) +{ + int sockets[2]; + if (socketpair(AF_UNIX, SOCK_STREAM, 0, sockets) != 0) { + perror("socketpair"); + return -1; + } + + /* Flush stdio before fork() so the child does not inherit a buffer that + * would then be flushed twice. */ + fflush(stdout); + fflush(stderr); + + pid_t pid = fork(); + if (pid < 0) { + perror("fork"); + close(sockets[0]); + close(sockets[1]); + return -1; + } + if (pid == 0) { + close(sockets[1]); + int rc = runCrossProcessChild(device, sockets[0]); + close(sockets[0]); + fflush(stdout); + fflush(stderr); + _exit(rc == 0 ? 0 : 1); + } + + close(sockets[0]); + *outParentSocket = sockets[1]; + return pid; +} + +/* Find any pair of GPUs where one supports producing a dma-buf fd from a + * host allocation and the other supports importing a dma-buf fd. Iteration + * is device-order so the result is deterministic -- child and parent each + * call this independently after the fork and arrive at the same (producer, + * consumer) pair. Both outputs are -1 if no qualifying pair is present. */ +static void findCrossGpuPair(int *outProducer, int *outConsumer) +{ + *outProducer = -1; + *outConsumer = -1; + + int deviceCount = 0; + cudaGetDeviceCount(&deviceCount); + if (deviceCount < 2) return; + + for (int p = 0; p < deviceCount; ++p) { + int hostAllocOk = 0; + cuDeviceGetAttribute(&hostAllocOk, + CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED, p); + if (!hostAllocOk) continue; + for (int c = 0; c < deviceCount; ++c) { + if (c == p) continue; + int importOk = 0; + cuDeviceGetAttribute(&importOk, + CU_DEVICE_ATTRIBUTE_DMA_BUF_SUPPORTED, c); + if (importOk) { *outProducer = p; *outConsumer = c; return; } + } + } +} + +/* Cross-GPU cross-process child: producer on the discovered producer GPU. + * Mirrors runCrossProcessChild but runs on a specific device chosen by + * findCrossGpuPair. The parent is the arbiter for skip decisions: child + * waits for a "go" byte from the parent (or an EOF if the parent decided + * to skip the demo) before allocating anything. */ +static int runCrossProcCrossGpuChild(int sock) +{ + cuInit(0); + + char go = 0; + if (read(sock, &go, 1) <= 0) return 0; + + int producer = -1, consumer = -1; + findCrossGpuPair(&producer, &consumer); + if (producer < 0) return 0; + + cudaSetDevice(producer); + + DmabufExport e = exportHostAllocAsDmabuf(NUM_ELEMENTS * sizeof(int)); + printf(" child: on device %d, allocated %zu bytes, exported as dma-buf fd %d\n", + producer, e.size, e.fd); + + int blocks = (NUM_ELEMENTS + BLOCK_SIZE - 1) / BLOCK_SIZE; + writePatternKernel<<>>((int *)e.alignedPtr, PATTERN_BASE, NUM_ELEMENTS); + cudaDeviceSynchronize(); + printf(" child: wrote pattern\n"); + + sendFd(sock, e.fd); + printf(" child: sent fd to parent\n"); + + char ack = 0; + [[maybe_unused]] ssize_t r = read(sock, &ack, 1); + + /* Free up resources. */ + close(e.fd); + cuMemFreeHost(e.allocBase); + return 0; +} + +/* Cross-GPU cross-process parent: consumer on the discovered consumer GPU. + * Decides whether the demo proceeds: signals "go" to the child if a + * qualifying pair exists, otherwise prints a skip message and returns + * (closing the socket without writing, which the child sees as EOF). */ +static int runCrossProcCrossGpuParent(int sock) +{ + int producer = -1, consumer = -1; + findCrossGpuPair(&producer, &consumer); + if (producer < 0) { + printf(" no GPU pair satisfies the dma-buf capability attributes. Skipping.\n"); + return 0; + } + + cudaSetDevice(consumer); + + char go = 'G'; + [[maybe_unused]] ssize_t r_go = write(sock, &go, 1); + + /* fd is the dma-buf that the producer child just wrote to on its own GPU; + * import it into this parent's context on the consumer GPU below to + * access the same underlying memory. */ + int fd = recvFd(sock); + printf(" parent: on device %d, received dma-buf fd %d from child\n", consumer, fd); + + const size_t size = alignUp(NUM_ELEMENTS * sizeof(int), (size_t)sysconf(_SC_PAGESIZE)); + + CUexternalMemory extMem = NULL; + CUdeviceptr importedPtr = 0; + importDmabuf(fd, size, &extMem, &importedPtr); + printf(" parent: imported dma-buf as device pointer\n"); + + int *errors = NULL; + cudaMallocHost(&errors, sizeof(int)); + *errors = 0; + int blocks = (NUM_ELEMENTS + BLOCK_SIZE - 1) / BLOCK_SIZE; + verifyPatternKernel<<>>( + (int *)importedPtr, PATTERN_BASE, NUM_ELEMENTS, errors); + cudaDeviceSynchronize(); + + int rc = (*errors == 0) ? 0 : -1; + printf(" parent: %s (%d mismatches)\n", rc == 0 ? "verified" : "Error", *errors); + + char ack = 'A'; + [[maybe_unused]] ssize_t r_ack = write(sock, &ack, 1); + + /* Free up resources. */ + cudaFreeHost(errors); + cuDestroyExternalMemory(extMem); + close(fd); + return rc; +} + +/* Cross-GPU cross-process orchestration: spawn the producer child the same + * way as spawnCrossProcessChild. Like the single-GPU IPC demo, this fork + * must happen before any cuInit in the parent process. */ +static pid_t spawnCrossProcCrossGpuChild(int *outParentSocket) +{ + int sockets[2]; + if (socketpair(AF_UNIX, SOCK_STREAM, 0, sockets) != 0) { + perror("socketpair"); + return -1; + } + + fflush(stdout); + fflush(stderr); + + pid_t pid = fork(); + if (pid < 0) { + perror("fork"); + close(sockets[0]); + close(sockets[1]); + return -1; + } + if (pid == 0) { + close(sockets[1]); + int rc = runCrossProcCrossGpuChild(sockets[0]); + close(sockets[0]); + fflush(stdout); + fflush(stderr); + _exit(rc == 0 ? 0 : 1); + } + + close(sockets[0]); + *outParentSocket = sockets[1]; + return pid; +} + +int main() +{ + /* Make stdout line-buffered so printf output from the parent and the + * fork()ed children appears in temporal order even when stdout is + * redirected to a pipe (e.g. via ssh) rather than a terminal. */ + setvbuf(stdout, NULL, _IOLBF, 0); + + const int device = 0; + + /* Spawn both children before any cuInit in this process. Each child runs + * a producer-side role and blocks waiting for its parent to drive the + * import. fork() must precede cuInit -- the CUDA driver does not survive + * fork-after-init, so each child does its own cuInit independently. */ + int sockIpc = -1, sockCrossGpu = -1; + pid_t childIpcPid = spawnCrossProcessChild(device, &sockIpc); + if (childIpcPid < 0) return 1; + pid_t childCrossGpuPid = spawnCrossProcCrossGpuChild(&sockCrossGpu); + if (childCrossGpuPid < 0) { + close(sockIpc); + reapCrossProcessChild(childIpcPid); + return 1; + } + + cuInit(0); + + int dmabufExportSupported = 0; + cuDeviceGetAttribute(&dmabufExportSupported, + CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED, device); + if (!dmabufExportSupported) { + printf("Device %d does not report CU_DEVICE_ATTRIBUTE_HOST_ALLOC_DMA_BUF_SUPPORTED. " + "Skipping sample.\n", device); + close(sockIpc); + close(sockCrossGpu); + reapCrossProcessChild(childIpcPid); + reapCrossProcessChild(childCrossGpuPid); + return 0; + } + + cudaDeviceProp props; + cudaGetDeviceProperties(&props, device); + printf("Device %d: %s (Compute Capability %d.%d)\n\n", + device, props.name, props.major, props.minor); + + printf("Same-process round-trip\n"); + int rc = runSameProcess(device); + + printf("\nCross-process IPC (fork + SCM_RIGHTS)\n"); + int ipcParentRc = (rc == 0) ? runCrossProcessParent(device, sockIpc) : -1; + close(sockIpc); + int ipcChildRc = reapCrossProcessChild(childIpcPid); + if (rc == 0 && (ipcParentRc != 0 || ipcChildRc != 0)) rc = -1; + + printf("\nCross-GPU sharing across processes\n"); + int crossGpuParentRc = (rc == 0) ? runCrossProcCrossGpuParent(sockCrossGpu) : -1; + close(sockCrossGpu); + int crossGpuChildRc = reapCrossProcessChild(childCrossGpuPid); + if (rc == 0 && (crossGpuParentRc != 0 || crossGpuChildRc != 0)) rc = -1; + + if (rc == 0) printf("\nDone\n"); + return rc == 0 ? 0 : 1; +} diff --git a/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop_helpers.h b/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop_helpers.h new file mode 100644 index 00000000..cb788352 --- /dev/null +++ b/cpp/3_CUDA_Features/dmabufInterop/dmabufInterop_helpers.h @@ -0,0 +1,110 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * POSIX helpers used by the cross-process demos in dmabufInterop.cu: + * - sendFd / recvFd: transport a file descriptor between two processes + * over a UNIX domain socket via the SCM_RIGHTS control message. + * - reapCrossProcessChild: waitpid() wrapper that returns 0 iff the child + * exited cleanly with status 0. + * + * These are POSIX-only (no CUDA code) and are separated out so that + * dmabufInterop.cu stays focused on the CUDA dma-buf export/import flow. + */ + +#ifndef DMABUF_INTEROP_HELPERS_H +#define DMABUF_INTEROP_HELPERS_H + +#include +#include +#include +#include +#include +#include +#include + +/* Send a file descriptor over a UNIX domain socket using SCM_RIGHTS. */ +static inline void sendFd(int sock, int fd) +{ + struct msghdr msg = {}; + char dummy = 'F'; + struct iovec iov = { &dummy, 1 }; + msg.msg_iov = &iov; + msg.msg_iovlen = 1; + + char cbuf[CMSG_SPACE(sizeof(int))] = {}; + msg.msg_control = cbuf; + msg.msg_controllen = sizeof(cbuf); + + struct cmsghdr *cmsg = CMSG_FIRSTHDR(&msg); + cmsg->cmsg_level = SOL_SOCKET; + cmsg->cmsg_type = SCM_RIGHTS; + cmsg->cmsg_len = CMSG_LEN(sizeof(int)); + memcpy(CMSG_DATA(cmsg), &fd, sizeof(int)); + + (void)sendmsg(sock, &msg, 0); +} + +/* Receive a file descriptor from a UNIX domain socket. Returns -1 if no + * valid single-fd SCM_RIGHTS control message was received. */ +static inline int recvFd(int sock) +{ + struct msghdr msg = {}; + char dummy = 0; + struct iovec iov = { &dummy, 1 }; + msg.msg_iov = &iov; + msg.msg_iovlen = 1; + + char cbuf[CMSG_SPACE(sizeof(int))] = {}; + msg.msg_control = cbuf; + msg.msg_controllen = sizeof(cbuf); + + (void)recvmsg(sock, &msg, 0); + + struct cmsghdr *cmsg = CMSG_FIRSTHDR(&msg); + /* Validate that recvmsg actually delivered a single-fd SCM_RIGHTS control + * message before dereferencing -- a truncated or absent ancillary buffer + * would otherwise crash on the memcpy below. */ + if (cmsg == NULL || cmsg->cmsg_len != CMSG_LEN(sizeof(int))) return -1; + int fd = -1; + memcpy(&fd, CMSG_DATA(cmsg), sizeof(int)); + return fd; +} + +/* Wait for a child process to exit and return 0 iff it exited cleanly with + * status 0. */ +static inline int reapCrossProcessChild(pid_t pid) +{ + int status = 0; + if (waitpid(pid, &status, 0) < 0) { + perror("waitpid"); + return -1; + } + return (WIFEXITED(status) && WEXITSTATUS(status) == 0) ? 0 : -1; +} + +#endif /* DMABUF_INTEROP_HELPERS_H */ diff --git a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/extensions.json b/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/CMakeLists.txt b/cpp/3_CUDA_Features/dmmaTensorCoreGemm/CMakeLists.txt index d3aa179c..af2ed418 100644 --- a/cpp/3_CUDA_Features/dmmaTensorCoreGemm/CMakeLists.txt +++ b/cpp/3_CUDA_Features/dmmaTensorCoreGemm/CMakeLists.txt @@ -1,41 +1,31 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(dmmaTensorCoreGemm LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # This sample is not supported on QNX if(CMAKE_SYSTEM_NAME STREQUAL "QNX") message(STATUS "Will not build sample ${PROJECT_NAME} - not supported on QNX") return() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for dmmaTensorCoreGemm add_executable(dmmaTensorCoreGemm dmmaTensorCoreGemm.cu) target_compile_options(dmmaTensorCoreGemm PRIVATE $<$:--extended-lambda>) target_compile_features(dmmaTensorCoreGemm PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(dmmaTensorCoreGemm PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/extensions.json b/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/CMakeLists.txt b/cpp/3_CUDA_Features/globalToShmemAsyncCopy/CMakeLists.txt index 47abd5e0..117c0c4c 100644 --- a/cpp/3_CUDA_Features/globalToShmemAsyncCopy/CMakeLists.txt +++ b/cpp/3_CUDA_Features/globalToShmemAsyncCopy/CMakeLists.txt @@ -1,23 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(globalToShmemAsyncCopy LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -26,16 +21,11 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for globalToShmemAsyncCopy add_executable(globalToShmemAsyncCopy globalToShmemAsyncCopy.cu) target_compile_options(globalToShmemAsyncCopy PRIVATE $<$:--extended-lambda>) target_compile_features(globalToShmemAsyncCopy PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(globalToShmemAsyncCopy PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/extensions.json b/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/graphConditionalNodes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/graphConditionalNodes/CMakeLists.txt b/cpp/3_CUDA_Features/graphConditionalNodes/CMakeLists.txt index 5e7cd329..d3cd9fff 100644 --- a/cpp/3_CUDA_Features/graphConditionalNodes/CMakeLists.txt +++ b/cpp/3_CUDA_Features/graphConditionalNodes/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(graphConditionalNodes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for graphConditionalNodes add_executable(graphConditionalNodes graphConditionalNodes.cu) target_compile_options(graphConditionalNodes PRIVATE $<$:--extended-lambda>) target_compile_features(graphConditionalNodes PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(graphConditionalNodes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/extensions.json b/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/graphMemoryFootprint/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/graphMemoryFootprint/CMakeLists.txt b/cpp/3_CUDA_Features/graphMemoryFootprint/CMakeLists.txt index 29d206b9..fc9d5b01 100644 --- a/cpp/3_CUDA_Features/graphMemoryFootprint/CMakeLists.txt +++ b/cpp/3_CUDA_Features/graphMemoryFootprint/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(graphMemoryFootprint LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for graphMemoryFootprint add_executable(graphMemoryFootprint graphMemoryFootprint.cu) target_compile_options(graphMemoryFootprint PRIVATE $<$:--extended-lambda>) target_compile_features(graphMemoryFootprint PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(graphMemoryFootprint PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/extensions.json b/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/graphMemoryNodes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/graphMemoryNodes/CMakeLists.txt b/cpp/3_CUDA_Features/graphMemoryNodes/CMakeLists.txt index aeabc6bc..a1e668cd 100644 --- a/cpp/3_CUDA_Features/graphMemoryNodes/CMakeLists.txt +++ b/cpp/3_CUDA_Features/graphMemoryNodes/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(graphMemoryNodes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for graphMemoryNodes add_executable(graphMemoryNodes graphMemoryNodes.cu) target_compile_options(graphMemoryNodes PRIVATE $<$:--extended-lambda>) target_compile_features(graphMemoryNodes PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(graphMemoryNodes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/extensions.json b/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/immaTensorCoreGemm/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/immaTensorCoreGemm/CMakeLists.txt b/cpp/3_CUDA_Features/immaTensorCoreGemm/CMakeLists.txt index 7d711938..aa5b2ffe 100644 --- a/cpp/3_CUDA_Features/immaTensorCoreGemm/CMakeLists.txt +++ b/cpp/3_CUDA_Features/immaTensorCoreGemm/CMakeLists.txt @@ -1,35 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(immaTensorCoreGemm LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for immaTensorCoreGemm add_executable(immaTensorCoreGemm immaTensorCoreGemm.cu) target_compile_options(immaTensorCoreGemm PRIVATE $<$:--extended-lambda>) target_compile_features(immaTensorCoreGemm PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(immaTensorCoreGemm PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/extensions.json b/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/jacobiCudaGraphs/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/jacobiCudaGraphs/CMakeLists.txt b/cpp/3_CUDA_Features/jacobiCudaGraphs/CMakeLists.txt index a39b81af..bab1120b 100644 --- a/cpp/3_CUDA_Features/jacobiCudaGraphs/CMakeLists.txt +++ b/cpp/3_CUDA_Features/jacobiCudaGraphs/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(jacobiCudaGraphs LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for jacobiCudaGraphs add_executable(jacobiCudaGraphs jacobi.cu main.cpp) target_compile_options(jacobiCudaGraphs PRIVATE $<$:--extended-lambda>) target_compile_features(jacobiCudaGraphs PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(jacobiCudaGraphs PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(jacobiCudaGraphs PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/0_Introduction/simpleTemplates/CMakeLists.txt b/cpp/3_CUDA_Features/localityDomains/CMakeLists.txt similarity index 68% rename from cpp/0_Introduction/simpleTemplates/CMakeLists.txt rename to cpp/3_CUDA_Features/localityDomains/CMakeLists.txt index e4ae61e1..bf3232eb 100644 --- a/cpp/0_Introduction/simpleTemplates/CMakeLists.txt +++ b/cpp/3_CUDA_Features/localityDomains/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.20) list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") -project(simpleTemplates LANGUAGES C CXX CUDA) +project(localityDomains LANGUAGES C CXX CUDA) find_package(CUDAToolkit REQUIRED) @@ -16,18 +16,15 @@ else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() -# Include directories and libraries -include_directories(../../../Common) - # Source file -# Add target for simpleTemplates -add_executable(simpleTemplates simpleTemplates.cu) +# Add target for localityDomains +add_executable(localityDomains localityDomains.cu) -target_compile_options(simpleTemplates PRIVATE $<$:--extended-lambda>) +target_compile_options(localityDomains PRIVATE $<$:--extended-lambda>) -target_compile_features(simpleTemplates PRIVATE cxx_std_17 cuda_std_17) +target_compile_features(localityDomains PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(simpleTemplates PROPERTIES CUDA_SEPARABLE_COMPILATION ON) +set_target_properties(localityDomains PROPERTIES CUDA_SEPARABLE_COMPILATION ON) # Include installation configuration include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) diff --git a/cpp/3_CUDA_Features/localityDomains/README.md b/cpp/3_CUDA_Features/localityDomains/README.md new file mode 100644 index 00000000..cc658a83 --- /dev/null +++ b/cpp/3_CUDA_Features/localityDomains/README.md @@ -0,0 +1,86 @@ +# localityDomains - CUDA Runtime API Locality Domains + +## Description + +This CUDA Runtime API sample demonstrates localized compute and memory resources. + +Compute is localized by splitting a device's SM resources into locality domain partitions and constructing a green context and stream per locality domain. Memory is localized by allocating from localized memory pools. + +## What You'll Learn +How to: +- query a device's locality domain count and SM count per domain +- partition SM resources by locality domain ID +- create green contexts and streams from localized SM partitions +- create localized memory pools and allocate from them asynchronously +- query the locality domain associated with a stream and device pointer + +## Key Concepts + +- **Locality domain** — a portion of a GPU that contains streaming multiprocessors (SMs) and device memory. +**Green context** — an execution context created from a selected subset of device resources. +- **Localized memory pool** — a stream-ordered allocator whose physical memory is placed in a selected locality domain. + +## Key APIs + +### CUDA Runtime + +- `cudaDeviceGetAttribute` — query the number of locality domains and SMs per domain +- `cudaDevSmResourceSplit` — partition the device's SM resources +- `cudaMemPoolCreate` — create a localized memory pool and allocate from it +- `cudaStreamGetDevResource` — inspect stream locality +- `cudaPointerGetAttributes` — inspect allocation locality + +## Requirements + +### Hardware + +- NVIDIA GPU that supports locality domains and stream-ordered memory pools + +### Software + +- CUDA Toolkit and NVIDIA driver with locality domain support +- Linux or QNX on x86_64 or aarch64 +- CMake 3.20 or newer +- A C++17-capable host compiler + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +```bash +./localityDomains +``` + +No command-line arguments are required. The sample always runs on device 0. + +## Expected Output + +The number of locality domains and SMs depends on the GPU. A run has the following form: + +```text +CUDA Runtime API locality domains sample +Locality domain count: +SMs per locality domain: +Device SMs split into partitions + - result[0]: .localityDomainId = 0, .smCount = +... +Green context stream 0 is localized to locality domain 0 +... +allocation 0x
is localized to locality domain 0 +... +``` + +If device 0 does not support memory pools, the sample reports that it is waiving execution and exits with status EXIT_WAIVED. + +## Files + +- `localityDomains.cu` — Runtime API example +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA Runtime API](https://docs.nvidia.com/cuda/cuda-runtime-api/) +- [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-programming-guide/) diff --git a/cpp/3_CUDA_Features/localityDomains/localityDomains.cu b/cpp/3_CUDA_Features/localityDomains/localityDomains.cu new file mode 100644 index 00000000..456532f1 --- /dev/null +++ b/cpp/3_CUDA_Features/localityDomains/localityDomains.cu @@ -0,0 +1,171 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * This sample demonstrates CUDA locality domains using the Runtime API. + * It creates one green context and stream per reported locality domain and + * allocates one localized memory-pool chunk per locality domain, then queries the + * placement back from both the stream resource and the device pointer. + */ + +#include +#include +#include +#include +#include +#include + +int main() +{ + std::cout << "CUDA Runtime API locality domains sample" << std::endl; + + int dev = 0; + cudaSetDevice(dev); + + int memoryPoolsSupported = 0; + cudaDeviceGetAttribute(&memoryPoolsSupported, cudaDevAttrMemoryPoolsSupported, dev); + + if (!memoryPoolsSupported) { + std::cout << "Waiving execution as the device does not support memory pools." << std::endl; + return 0; + } + + // Device information + int localityDomainCount = 0; + { + int localityDomainSmCount = 0; + cudaDeviceGetAttribute(&localityDomainCount, cudaDevAttrLocalityDomainCount, dev); + cudaDeviceGetAttribute(&localityDomainSmCount, cudaDevAttrLocalityDomainMultiprocessorCount, dev); + + std::cout << "Locality domain count: " << localityDomainCount << std::endl; + std::cout << "SMs per locality domain: " << localityDomainSmCount << std::endl; + } + + // Construct localized green contexts and streams + std::vector greenContexts(localityDomainCount); + std::vector streams(localityDomainCount); + { + cudaDevResource smResource = {}; + cudaDeviceGetDevResource(dev, &smResource, cudaDevResourceTypeSm); + + std::vector params(localityDomainCount); + for (size_t i = 0; i < localityDomainCount; i++) { + std::memset(¶ms[i], 0, sizeof(params[i])); + params[i].flags = cudaDevSmResourceGroupLocalityDomainId; + params[i].localityDomainId = static_cast(i); + } + + std::vector result(localityDomainCount); + cudaDevResource remainder = {}; + + cudaDevSmResourceSplit(result.data(), localityDomainCount, &smResource, &remainder, 0, params.data()); + std::cout << "Device SMs split into " << localityDomainCount << " partitions" << std::endl; + for (size_t i = 0; i < localityDomainCount; i++) { + std::cout << " - result[" << i << "]: .localityDomainId = " << result[i].sm.localityDomainId + << ", .smCount = " << result[i].sm.smCount << std::endl; + } + if (remainder.sm.smCount > 0) { + std::cout << " - remainder: .localityDomainId = none, .smCount = " << remainder.sm.smCount << std::endl; + } + + for (size_t i = 0; i < localityDomainCount; i++) { + cudaDevResourceDesc_t desc = nullptr; + cudaDevResourceGenerateDesc(&desc, &result[i], 1); + cudaGreenCtxCreate(&greenContexts[i], desc, dev, 0); + cudaExecutionCtxStreamCreate(&streams[i], greenContexts[i], cudaStreamNonBlocking, 0); + } + } + + // Inspect locality of streams + { + for (size_t i = 0; i < localityDomainCount; i++) { + cudaDevResource streamSmResource = {}; + cudaStreamGetDevResource(streams[i], &streamSmResource, cudaDevResourceTypeSm); + bool isStreamLocalized = (streamSmResource.sm.flags & cudaDevSmResourceGroupLocalityDomainId) != 0; + std::cout << "Green context stream " << i << " "; + if (isStreamLocalized) { + unsigned int streamLocalityDomain = streamSmResource.sm.localityDomainId; + std::cout << "is localized to locality domain " << streamLocalityDomain << std::endl; + } + else { + std::cout << "is not localized" << std::endl; + } + } + } + + // Allocate localized memory + size_t allocationSize = 1 << 20; + std::vector localizedPtrs(localityDomainCount); + std::vector pools(localityDomainCount); + { + for (size_t i = 0; i < localityDomainCount; i++) { + cudaMemPoolProps poolProps = {}; + poolProps.allocType = cudaMemAllocationTypePinned; + poolProps.location.type = cudaMemLocationTypeDeviceLocalityDomain; + poolProps.location.localized.deviceId = static_cast(dev); + poolProps.location.localized.localityDomainId = static_cast(i); + + cudaMemPoolCreate(&pools[i], &poolProps); + cudaMallocFromPoolAsync(&localizedPtrs[i], allocationSize, pools[i], streams[i]); + cudaStreamSynchronize(streams[i]); + } + } + + // Inspect locality of allocations + { + for (void *ptr : localizedPtrs) { + cudaPointerAttributes pointerAttributes = {}; + cudaPointerGetAttributes(&pointerAttributes, ptr); + + std::cout << "allocation 0x" << std::hex << reinterpret_cast(ptr) << std::dec; + if (pointerAttributes.localityDomainOrdinal == -1) { + std::cout << " is not localized." << std::endl; + } + else { + std::cout << " is localized to locality domain " << pointerAttributes.localityDomainOrdinal + << std::endl; + } + } + } + + // Clean up + for (size_t i = 0; i < localizedPtrs.size(); i++) { + cudaFreeAsync(localizedPtrs[i], streams[i]); + } + for (cudaStream_t stream : streams) { + cudaStreamSynchronize(stream); + cudaStreamDestroy(stream); + } + for (cudaExecutionContext_t greenContext : greenContexts) { + cudaExecutionCtxDestroy(greenContext); + } + for (cudaMemPool_t pool : pools) { + cudaMemPoolDestroy(pool); + } + + return 0; +} diff --git a/cpp/0_Introduction/mergeSort/CMakeLists.txt b/cpp/3_CUDA_Features/localityDomainsDrv/CMakeLists.txt similarity index 61% rename from cpp/0_Introduction/mergeSort/CMakeLists.txt rename to cpp/3_CUDA_Features/localityDomainsDrv/CMakeLists.txt index cc8747ca..cbc27d15 100644 --- a/cpp/0_Introduction/mergeSort/CMakeLists.txt +++ b/cpp/3_CUDA_Features/localityDomainsDrv/CMakeLists.txt @@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.20) list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") -project(mergeSort LANGUAGES C CXX CUDA) +project(localityDomainsDrv LANGUAGES C CXX CUDA) find_package(CUDAToolkit REQUIRED) @@ -16,22 +16,24 @@ else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() -# Include directories and libraries -include_directories(../../../Common) - # Source file -# Add target for mergeSort -add_executable(mergeSort mergeSort.cu main.cpp mergeSort_host.cpp mergeSort_validate.cpp) +# Add target for localityDomainsDrv +add_executable(localityDomainsDrv localityDomainsDrv.cpp) -target_compile_options(mergeSort PRIVATE $<$:--extended-lambda>) +target_compile_options(localityDomainsDrv PRIVATE $<$:--extended-lambda>) -target_compile_features(mergeSort PRIVATE cxx_std_17 cuda_std_17) +target_compile_features(localityDomainsDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(mergeSort PROPERTIES CUDA_SEPARABLE_COMPILATION ON) -target_include_directories(mergeSort PRIVATE +set_target_properties(localityDomainsDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) + +target_include_directories(localityDomainsDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) +target_link_libraries(localityDomainsDrv PUBLIC + CUDA::cuda_driver +) + # Include installation configuration include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) setup_samples_install() diff --git a/cpp/3_CUDA_Features/localityDomainsDrv/README.md b/cpp/3_CUDA_Features/localityDomainsDrv/README.md new file mode 100644 index 00000000..c12e92dd --- /dev/null +++ b/cpp/3_CUDA_Features/localityDomainsDrv/README.md @@ -0,0 +1,86 @@ +# localityDomainsDrv - CUDA Driver API Locality Domains + +## Description + +This CUDA Driver API sample demonstrates localized compute and memory resources. + +Compute is localized by splitting a device's SM resources into locality domain partitions and constructing a green context and stream per locality domain. Memory is localized by creating a localized memory region and mapping it. + +## What You'll Learn +How to: +- query a device's locality domain count and SM count per locality domain +- partition SM resources by locality domain ID +- create green contexts and streams from localized SM partitions +- create and map physical allocations in locality domains +- query the locality domain associated with a stream and device pointer + +## Key Concepts + +- **Locality domain** — a portion of a GPU that contains streaming multiprocessors (SMs) and device memory. +**Green context** — an execution context created from a selected subset of device resources. +- **Virtual memory management** — separate control over virtual addresses, physical allocations, mappings, and access permissions. + +## Key APIs + +### CUDA Driver + +- `cuDeviceGetAttribute` — query the number of locality domains and SMs per domain +- `cuDevSmResourceSplit` — partition the device's SM resources +- `cuMemCreate` — create localized physical memory +- `cuStreamGetDevResource` — inspect stream locality +- `cuPointerGetAttribute` — inspect allocation locality + +## Requirements + +### Hardware + +- NVIDIA GPU that supports locality domains + +### Software + +- CUDA Toolkit and NVIDIA driver with locality domain support +- Linux or QNX on x86_64 or aarch64 +- CMake 3.20 or newer +- A C++17-capable host compiler + +## How to Build + +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +```bash +./localityDomainsDrv +``` + +No command-line arguments are required. The sample always runs on device 0. + +## Expected Output + +The number of locality domains and SMs depends on the GPU. A run has the following form: + +```text +CUDA Driver API locality domains sample +Locality domain count: +SMs per locality domain: +Device SMs split into partitions + - result[0]: .localityDomainId = 0, .smCount = +... +Green context stream 0 is localized to locality domain 0 +... +Allocation granularity per chunk: bytes +Reserved VA range size: bytes +allocation 0x
is localized to locality domain 0 +... +``` + +## Files + +- `localityDomainsDrv.cpp` — Driver API example +- `README.md` — this file +- `CMakeLists.txt` — build configuration + +## See Also + +- [CUDA Driver API](https://docs.nvidia.com/cuda/cuda-driver-api/) +- [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-programming-guide/) diff --git a/cpp/3_CUDA_Features/localityDomainsDrv/localityDomainsDrv.cpp b/cpp/3_CUDA_Features/localityDomainsDrv/localityDomainsDrv.cpp new file mode 100644 index 00000000..aa868554 --- /dev/null +++ b/cpp/3_CUDA_Features/localityDomainsDrv/localityDomainsDrv.cpp @@ -0,0 +1,188 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * This sample demonstrates CUDA locality domains using the Driver API. + * It creates one green context and stream per reported locality domain and + * maps one virtual-address chunk to each locality domain, then queries the + * placement back from both the stream resource and the device pointer. + */ + +#include +#include +#include +#include +#include + +int main() +{ + std::cout << "CUDA Driver API locality domains sample" << std::endl; + + cuInit(0); + + CUdevice dev; + cuDeviceGet(&dev, 0); + + CUctxCreateParams ctxCreateParams = {}; + CUcontext context; + cuCtxCreate(&context, &ctxCreateParams, 0, dev); + + // Device information + int localityDomainCount = 0; + { + cuDeviceGetAttribute(&localityDomainCount, CU_DEVICE_ATTRIBUTE_LOCALITY_DOMAIN_COUNT, dev); + int localityDomainSmCount = 0; + cuDeviceGetAttribute(&localityDomainSmCount, CU_DEVICE_ATTRIBUTE_LOCALITY_DOMAIN_MULTIPROCESSOR_COUNT, dev); + + std::cout << "Locality domain count: " << localityDomainCount << std::endl; + std::cout << "SMs per locality domain: " << localityDomainSmCount << std::endl; + } + + // Construct localized green contexts and streams + std::vector greenContexts(localityDomainCount); + std::vector streams(localityDomainCount); + { + CUdevResource smResource; + cuDeviceGetDevResource(dev, &smResource, CU_DEV_RESOURCE_TYPE_SM); + + std::vector params(localityDomainCount); + for (size_t i = 0; i < localityDomainCount; i++) { + std::memset(¶ms[i], 0, sizeof(params[i])); + params[i].flags = CU_DEV_SM_RESOURCE_GROUP_LOCALITY_DOMAIN_ID; + params[i].localityDomainId = static_cast(i); + } + + std::vector result(localityDomainCount); + CUdevResource remainder = {}; + + cuDevSmResourceSplit(result.data(), localityDomainCount, &smResource, &remainder, 0, params.data()); + std::cout << "Device SMs split into " << localityDomainCount << " partitions" << std::endl; + for (size_t i = 0; i < localityDomainCount; i++) { + std::cout << " - result[" << i << "]: .localityDomainId = " << result[i].sm.localityDomainId + << ", .smCount = " << result[i].sm.smCount << std::endl; + } + if (remainder.sm.smCount > 0) { + std::cout << " - remainder: .localityDomainId = none, .smCount = " << remainder.sm.smCount << std::endl; + } + + for (size_t i = 0; i < localityDomainCount; i++) { + CUdevResourceDesc desc; + cuDevResourceGenerateDesc(&desc, &result[i], 1); + cuGreenCtxCreate(&greenContexts[i], desc, dev, CU_GREEN_CTX_DEFAULT_STREAM); + cuGreenCtxStreamCreate(&streams[i], greenContexts[i], CU_STREAM_NON_BLOCKING, 0); + } + } + + // Inspect locality of streams + { + for (size_t i = 0; i < localityDomainCount; i++) { + CUdevResource streamSmResource; + cuStreamGetDevResource(streams[i], &streamSmResource, CU_DEV_RESOURCE_TYPE_SM); + bool isStreamLocalized = (streamSmResource.sm.flags & CU_DEV_SM_RESOURCE_GROUP_LOCALITY_DOMAIN_ID) != 0; + std::cout << "Green context stream " << i << " "; + if (isStreamLocalized) { + unsigned int streamLocalityDomain = streamSmResource.sm.localityDomainId; + std::cout << "is localized to locality domain " << streamLocalityDomain << std::endl; + } + else { + std::cout << "is not localized" << std::endl; + } + } + } + + // Allocate localized memory + CUdeviceptr allocationBase = 0; + size_t allocationGranularity = 0; + size_t allocationSize = 0; + std::vector localizedPtrs(localityDomainCount); + { + { + CUmemAllocationProp prop = {}; + prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE_LOCALITY_DOMAIN; + prop.location.localized.deviceId = static_cast(dev); + prop.location.localized.localityDomainId = 0; + cuMemGetAllocationGranularity(&allocationGranularity, &prop, CU_MEM_ALLOC_GRANULARITY_MINIMUM); + } + std::cout << "Allocation granularity per chunk: " << allocationGranularity << " bytes" << std::endl; + allocationSize = allocationGranularity * localityDomainCount; + + cuMemAddressReserve(&allocationBase, allocationSize, 0, 0, 0); + std::cout << "Reserved VA range size: " << allocationSize << " bytes" << std::endl; + + for (size_t i = 0; i < localityDomainCount; i++) { + localizedPtrs[i] = allocationBase + i * allocationGranularity; + + CUmemGenericAllocationHandle handle; + CUmemAllocationProp prop = {}; + prop.type = CU_MEM_ALLOCATION_TYPE_PINNED; + prop.location.type = CU_MEM_LOCATION_TYPE_DEVICE_LOCALITY_DOMAIN; + prop.location.localized.deviceId = static_cast(dev); + prop.location.localized.localityDomainId = static_cast(i); + cuMemCreate(&handle, allocationGranularity, &prop, 0); + cuMemMap(localizedPtrs[i], allocationGranularity, 0, handle, 0); + + CUmemAccessDesc desc = {}; + desc.location.type = CU_MEM_LOCATION_TYPE_DEVICE_LOCALITY_DOMAIN; + desc.location.localized.deviceId = static_cast(dev); + desc.location.localized.localityDomainId = static_cast(i); + desc.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE; + cuMemSetAccess(localizedPtrs[i], allocationGranularity, &desc, 1); + cuMemRelease(handle); + } + } + + // Inspect locality of allocations + { + for (CUdeviceptr ptr : localizedPtrs) { + unsigned int localityDomainId = 0; + cuPointerGetAttribute(&localityDomainId, CU_POINTER_ATTRIBUTE_LOCALITY_DOMAIN_ORDINAL, ptr); + std::cout << "allocation 0x" << std::hex << ptr << std::dec; + if (localityDomainId == -1) { + std::cout << " is not localized." << std::endl; + } + else { + std::cout << " is localized to locality domain " << localityDomainId << std::endl; + } + } + } + + // Clean up + for (CUstream stream : streams) { + cuStreamDestroy(stream); + } + for (CUgreenCtx greenContext : greenContexts) { + cuGreenCtxDestroy(greenContext); + } + for (CUdeviceptr localizedPtr : localizedPtrs) { + cuMemUnmap(localizedPtr, allocationGranularity); + } + cuMemAddressFree(allocationBase, allocationSize); + cuCtxDestroy(context); + + return 0; +} diff --git a/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/extensions.json b/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/memMapIPCDrv/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/memMapIPCDrv/CMakeLists.txt b/cpp/3_CUDA_Features/memMapIPCDrv/CMakeLists.txt index 9856efbe..81205a8c 100644 --- a/cpp/3_CUDA_Features/memMapIPCDrv/CMakeLists.txt +++ b/cpp/3_CUDA_Features/memMapIPCDrv/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(memMapIPCDrv LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for memMapIPCDrv add_executable(memMapIPCDrv memMapIpc.cpp ../../../Common/helper_multiprocess.cpp) target_compile_options(memMapIPCDrv PRIVATE $<$:--extended-lambda>) target_compile_features(memMapIPCDrv PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(memMapIPCDrv PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(memMapIPCDrv PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -58,18 +50,16 @@ endif() add_custom_command( OUTPUT ${CUDA_PTX_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets -o ${CUDA_PTX_FILE} -ptx ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets -o ${CUDA_PTX_FILE} -ptx ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA PTX: ${CUDA_PTX_FILE}" ) - # Create a dummy target for fatbin generation add_custom_target(generate_memMapIpc_ptx ALL DEPENDS ${CUDA_PTX_FILE}) # Ensure memMapIPCDrv depends on the fatbin add_dependencies(memMapIPCDrv generate_memMapIpc_ptx) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/newdelete/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/newdelete/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/newdelete/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/newdelete/.vscode/extensions.json b/cpp/3_CUDA_Features/newdelete/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/newdelete/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/newdelete/CMakeLists.txt b/cpp/3_CUDA_Features/newdelete/CMakeLists.txt index 4437a6d3..41437a0f 100644 --- a/cpp/3_CUDA_Features/newdelete/CMakeLists.txt +++ b/cpp/3_CUDA_Features/newdelete/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(newdelete LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for newdelete add_executable(newdelete newdelete.cu) target_compile_options(newdelete PRIVATE $<$:--extended-lambda>) target_compile_features(newdelete PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(newdelete PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/ptxjit/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/ptxjit/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/ptxjit/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/ptxjit/.vscode/extensions.json b/cpp/3_CUDA_Features/ptxjit/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/ptxjit/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/ptxjit/CMakeLists.txt b/cpp/3_CUDA_Features/ptxjit/CMakeLists.txt index 15ad74f5..cdb24826 100644 --- a/cpp/3_CUDA_Features/ptxjit/CMakeLists.txt +++ b/cpp/3_CUDA_Features/ptxjit/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(ptxjit LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for ptxjit add_executable(ptxjit ptxjit.cpp) target_compile_options(ptxjit PRIVATE $<$:--extended-lambda>) target_compile_features(ptxjit PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(ptxjit PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(ptxjit PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -51,18 +43,16 @@ endif() add_custom_command( OUTPUT ${CUDA_PTX_FILE} - COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} ${ALL_CCFLAGS} -Wno-deprecated-gpu-targets -o ${CUDA_PTX_FILE} -ptx ${CUDA_KERNEL_SOURCE} + COMMAND ${CMAKE_CUDA_COMPILER} ${INCLUDES} -Wno-deprecated-gpu-targets -o ${CUDA_PTX_FILE} -ptx ${CUDA_KERNEL_SOURCE} DEPENDS ${CUDA_KERNEL_SOURCE} COMMENT "Building CUDA PTX: ${CUDA_PTX_FILE}" ) - # Create a dummy target for fatbin generation add_custom_target(generate_ptxjit_ptx ALL DEPENDS ${CUDA_PTX_FILE}) # Ensure ptxjit depends on the fatbin add_dependencies(ptxjit generate_ptxjit_ptx) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/extensions.json b/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/simpleCudaGraphs/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/CMakeLists.txt b/cpp/3_CUDA_Features/simpleCudaGraphs/CMakeLists.txt index b6a18373..ff2414ce 100644 --- a/cpp/3_CUDA_Features/simpleCudaGraphs/CMakeLists.txt +++ b/cpp/3_CUDA_Features/simpleCudaGraphs/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleCudaGraphs LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) +add_executable(simpleCudaGraphs_explicit simpleCudaGraphs_explicit.cu) +add_executable(simpleCudaGraphs_capture simpleCudaGraphs_capture.cu) -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() +foreach(target simpleCudaGraphs_explicit simpleCudaGraphs_capture) + target_compile_options(${target} PRIVATE $<$:--extended-lambda>) + target_compile_features(${target} PRIVATE cxx_std_17 cuda_std_17) + set_target_properties(${target} PROPERTIES CUDA_SEPARABLE_COMPILATION ON) + target_include_directories(${target} PRIVATE ${CUDAToolkit_INCLUDE_DIRS}/cccl) +endforeach() -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCudaGraphs -add_executable(simpleCudaGraphs simpleCudaGraphs.cu) - -target_compile_options(simpleCudaGraphs PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCudaGraphs PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCudaGraphs PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/README.md b/cpp/3_CUDA_Features/simpleCudaGraphs/README.md index 62c1e746..f0393d82 100644 --- a/cpp/3_CUDA_Features/simpleCudaGraphs/README.md +++ b/cpp/3_CUDA_Features/simpleCudaGraphs/README.md @@ -1,30 +1,126 @@ -# simpleCudaGraphs - Simple CUDA Graphs +# Sample: CUDA Graphs (simpleCudaGraphs) ## Description -A demonstration of CUDA Graphs creation, instantiation and launch using Graphs APIs and Stream Capture APIs. +Demonstrates how to create, instantiate, and launch CUDA Graphs using two different approaches: building the graph explicitly, node by node, with `cudaGraphAddNode`, and building it automatically by recording an existing stream sequence with `cudaStreamBeginCapture` / `cudaStreamEndCapture`. A CUDA Graph records a sequence of GPU operations — memory copies, kernel launches, and host callbacks — as a reusable DAG (Directed Acyclic Graph). Once instantiated, the graph can be submitted to the GPU with a single API call per launch, reducing per-launch CPU overhead compared to issuing each operation individually. + +The workload is a two-pass parallel reduction: 16M `float` values are reduced to per-block partial sums (`reduce` kernel), then to a single `double` result (`reduceFinal` kernel). Both samples use `cub::BlockReduce` for the in-kernel reduction. Each graph is launched several times, and the host input is refilled with new random values before every launch, to show that one instantiated graph can reprocess different data each iteration. + +This sample is split into two standalone executables plus a shared header: + +| File | Role | +|---|---| +| `simpleCudaGraphs_explicit.cu` | Explicit construction via the unified `cudaGraphAddNode` API | +| `simpleCudaGraphs_capture.cu` | Automatic construction via stream capture | +| `simpleCudaGraphs.cuh` | Shared reduction kernels, host callback (and its data type), and input-fill helper used by both | + +## What You'll Learn + +- Building a CUDA Graph node by node using the unified `cudaGraphAddNode` API with a typed `cudaGraphNodeParams` +- Expressing dependencies between nodes so the runtime enforces the correct execution order +- Building the same graph automatically by recording stream operations between `cudaStreamBeginCapture` and `cudaStreamEndCapture` +- Instantiating a graph with `cudaGraphInstantiate` (one-time compilation cost) and launching it repeatedly with `cudaGraphLaunch` +- Cloning a graph with `cudaGraphClone` to produce independent executable instances +- Adding memcpy, kernel, and host-callback nodes through the single polymorphic `cudaGraphAddNode` entry point +- Reusing one instantiated graph across many launches by refilling its input buffer between launches (and synchronizing so the graph's H2D copy consumes the data before the host overwrites it) +- Using `cub::BlockReduce` for efficient block-level reductions ## Key Concepts -CUDA Graphs, Stream Capture +- **CUDA Graph** — a DAG of GPU operations captured once and replayed many times; each replay is a single `cudaGraphLaunch` call regardless of graph size +- **Graph Node** — an individual operation in the graph: memcpy, kernel launch, or host callback +- **Node dependency** — an edge from node A to node B means B cannot start until A completes; expressed as a dependency list passed to `cudaGraphAddNode` +- **Instantiation** — `cudaGraphInstantiate` compiles the graph into an optimized executable form (`cudaGraphExec_t`); this is the one-time setup cost; subsequent launches reuse it +- **Stream Capture** — `cudaStreamBeginCapture` / `cudaStreamEndCapture` records stream operations into a graph automatically; the runtime infers the same node structure as the manually built graph +- **Graph Clone** — `cudaGraphClone` deep-copies the graph structure; each clone can be independently instantiated and launched, useful when multiple CPU threads need to launch the same graph concurrently +- **Graph reuse** — an instantiated graph is a template you launch repeatedly; because this graph begins with an H2D copy from a host buffer, refilling that buffer before each launch feeds new data through the same graph (with a `cudaStreamSynchronize` between launches so the copy reads the data before the host overwrites it) +- **`cub::BlockReduce`** — CUB's block-scoped reduction primitive; all threads in a block contribute their partial sum and thread 0 receives the block total -## Supported SM Architectures +## Key APIs -## Supported OSes +### CUDA Runtime — Explicit Graph Construction +- `cudaGraphCreate` — create an empty graph +- `cudaGraphAddNode` — add a node of any type (memcpy, kernel, host, …) via a `cudaGraphNodeParams` struct +- `cudaGraphNodeParams` — unified node descriptor: a `.type` tag plus a union of per-type parameters +- `cudaGraphGetNodes` — query the number of nodes in a graph -Linux, Windows +### CUDA Runtime — Stream Capture +- `cudaStreamBeginCapture` — put a stream into capture mode; subsequent operations are recorded, not executed +- `cudaStreamEndCapture` — stop recording and return the captured graph +- `cudaLaunchHostFunc` — schedule a CPU callback on a stream (captured as a host node) -## Supported CPU Architecture +### CUDA Runtime — Instantiation and Launch +- `cudaGraphInstantiate` — compile a graph into an executable `cudaGraphExec_t` +- `cudaGraphLaunch` — submit the entire graph to a stream in a single call +- `cudaGraphClone` — deep-copy a graph structure +- `cudaStreamSynchronize` — block until the stream's queued work (including the graph's H2D copy) completes, so the host input buffer can be safely refilled for the next launch +- `cudaGraphExecDestroy` — release an executable graph +- `cudaGraphDestroy` — release a graph -x86_64, armv7l +### CUB +- `cub::BlockReduce::Sum` — reduce all per-thread values to a single block sum; result lands on thread 0 -## CUDA APIs involved +## Requirements -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaGraphClone, cudaExtent, cudaGraphLaunch, cudaStreamCreate, cudaLaunchHostFunc, cudaGraphAddMemcpyNode, cudaMallocHost, cudaPitchedPtr, cudaStreamEndCapture, cudaGraphCreate, cudaFreeHost, cudaGraphGetNodes, cudaMemsetAsync, cudaMemcpyAsync, cudaGraphAddHostNode, cudaGraphInstantiate, cudaStreamDestroy, cudaStreamBeginCapture, cudaStreamWaitEvent, cudaEventCreate, cudaMalloc, cudaFree, cudaPos, cudaGraphAddKernelNode, cudaGraphDestroy, cudaEventRecord, cudaGraphsManual, cudaStreamSynchronize, cudaGraphAddMemsetNode, cudaGraphsUsingStreamCapture, cudaGraphExecDestroy +### Hardware +- NVIDIA GPU with Compute Capability 7.5 or higher -## Prerequisites +### Software +- CMake 3.20 or newer +- A C++17-capable host compiler -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. +## How to Build -## References (for more details) +See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone. + +## How to Run + +**Explicit graph construction:** +```bash +./simpleCudaGraphs_explicit +``` + +**Stream capture:** +```bash +./simpleCudaGraphs_capture +``` + +## Expected Output + +Both executables build the same 5-node graph (`Graph node count: 5`), confirming that stream capture produces the same structure as the explicit construction. Because the host input is refilled with new random values before every launch, **each of the six launches prints a different reduced sum** — showing that one instantiated graph reprocesses fresh data each iteration. The exact values depend on the platform's `rand()` implementation, but they are reproducible run-to-run and both executables print the same sequence. + +**`simpleCudaGraphs_explicit`** +```text +GPU Device 0: compute capability X.Y, SMs + +Reducing 16777216 elements +Threads per block : 512 +Graph launch iterations: 3 + +=== Explicit Graph Construction === +Graph node count: 5 +[cudaGraphsManual] Host callback final reduced sum = 0.996214 +[cudaGraphsManual] Host callback final reduced sum = 0.996187 +[cudaGraphsManual] Host callback final reduced sum = 0.996120 + +Cloned graph: +[cudaGraphsManual] Host callback final reduced sum = 0.996150 +[cudaGraphsManual] Host callback final reduced sum = 0.996184 +[cudaGraphsManual] Host callback final reduced sum = 0.996056 +``` + +**`simpleCudaGraphs_capture`** prints the same six values, each line labeled `[cudaGraphsUsingStreamCapture]` and under a `=== Stream Capture ===` header. + +## Files + +- `simpleCudaGraphs.cuh` — shared code: `THREADS_PER_BLOCK`, the `callBackData_t` type, the `myHostNodeCallback` host callback, the `init_input` host helper, and the two reduction kernels (`reduce`, `reduceFinal`) +- `simpleCudaGraphs_explicit.cu` — explicit graph construction via the unified `cudaGraphAddNode` API +- `simpleCudaGraphs_capture.cu` — graph construction via stream capture +- `CMakeLists.txt` — build configuration +- `README.md` — this file + +## See Also + +- [CUDA Programming Guide — CUDA Graphs](https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/cuda-graphs.html#cuda-graphs) +- [Technical Blog — Getting Started with CUDA Graphs](https://developer.nvidia.com/blog/cuda-graphs/) +- [CUDA Core Compute Libraries — What is CUB?](https://nvidia.github.io/cccl/unstable/cub/index.html#what-is-cub) diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cu b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cu deleted file mode 100644 index fa6eb507..00000000 --- a/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cu +++ /dev/null @@ -1,407 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -namespace cg = cooperative_groups; - -#define THREADS_PER_BLOCK 512 -#define GRAPH_LAUNCH_ITERATIONS 3 - -typedef struct callBackData -{ - const char *fn_name; - double *data; -} callBackData_t; - -__global__ void reduce(float *inputVec, double *outputVec, size_t inputSize, size_t outputSize) -{ - __shared__ double tmp[THREADS_PER_BLOCK]; - - cg::thread_block cta = cg::this_thread_block(); - size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x; - - double temp_sum = 0.0; - for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) { - temp_sum += (double)inputVec[i]; - } - tmp[cta.thread_rank()] = temp_sum; - - cg::sync(cta); - - cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta); - - double beta = temp_sum; - double temp; - - for (int i = tile32.size() / 2; i > 0; i >>= 1) { - if (tile32.thread_rank() < i) { - temp = tmp[cta.thread_rank() + i]; - beta += temp; - tmp[cta.thread_rank()] = beta; - } - cg::sync(tile32); - } - cg::sync(cta); - - if (cta.thread_rank() == 0 && blockIdx.x < outputSize) { - beta = 0.0; - for (int i = 0; i < cta.size(); i += tile32.size()) { - beta += tmp[i]; - } - outputVec[blockIdx.x] = beta; - } -} - -__global__ void reduceFinal(double *inputVec, double *result, size_t inputSize) -{ - __shared__ double tmp[THREADS_PER_BLOCK]; - - cg::thread_block cta = cg::this_thread_block(); - size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x; - - double temp_sum = 0.0; - for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) { - temp_sum += (double)inputVec[i]; - } - tmp[cta.thread_rank()] = temp_sum; - - cg::sync(cta); - - cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta); - - // do reduction in shared mem - if ((blockDim.x >= 512) && (cta.thread_rank() < 256)) { - tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 256]; - } - - cg::sync(cta); - - if ((blockDim.x >= 256) && (cta.thread_rank() < 128)) { - tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 128]; - } - - cg::sync(cta); - - if ((blockDim.x >= 128) && (cta.thread_rank() < 64)) { - tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 64]; - } - - cg::sync(cta); - - if (cta.thread_rank() < 32) { - // Fetch final intermediate sum from 2nd warp - if (blockDim.x >= 64) - temp_sum += tmp[cta.thread_rank() + 32]; - // Reduce final warp using shuffle - for (int offset = tile32.size() / 2; offset > 0; offset /= 2) { - temp_sum += tile32.shfl_down(temp_sum, offset); - } - } - // write result for this block to global mem - if (cta.thread_rank() == 0) - result[0] = temp_sum; -} - -void init_input(float *a, size_t size) -{ - for (size_t i = 0; i < size; i++) - a[i] = (rand() & 0xFF) / (float)RAND_MAX; -} - -void CUDART_CB myHostNodeCallback(void *data) -{ - // Check status of GPU after stream operations are done - callBackData_t *tmp = (callBackData_t *)(data); - // checkCudaErrors(tmp->status); - - double *result = (double *)(tmp->data); - char *function = (char *)(tmp->fn_name); - printf("[%s] Host callback final reduced sum = %lf\n", function, *result); - *result = 0.0; // reset the result -} - -void cudaGraphsManual(float *inputVec_h, - float *inputVec_d, - double *outputVec_d, - double *result_d, - size_t inputSize, - size_t numOfBlocks) -{ - cudaStream_t streamForGraph; - cudaGraph_t graph; - std::vector nodeDependencies; - cudaGraphNode_t memcpyNode, kernelNode, memsetNode; - double result_h = 0.0; - - checkCudaErrors(cudaStreamCreate(&streamForGraph)); - - cudaKernelNodeParams kernelNodeParams = {0}; - cudaMemcpy3DParms memcpyParams = {0}; - cudaMemsetParams memsetParams = {0}; - - memcpyParams.srcArray = NULL; - memcpyParams.srcPos = make_cudaPos(0, 0, 0); - memcpyParams.srcPtr = make_cudaPitchedPtr(inputVec_h, sizeof(float) * inputSize, inputSize, 1); - memcpyParams.dstArray = NULL; - memcpyParams.dstPos = make_cudaPos(0, 0, 0); - memcpyParams.dstPtr = make_cudaPitchedPtr(inputVec_d, sizeof(float) * inputSize, inputSize, 1); - memcpyParams.extent = make_cudaExtent(sizeof(float) * inputSize, 1, 1); - memcpyParams.kind = cudaMemcpyHostToDevice; - - memsetParams.dst = (void *)outputVec_d; - memsetParams.value = 0; - memsetParams.pitch = 0; - memsetParams.elementSize = sizeof(float); // elementSize can be max 4 bytes - memsetParams.width = numOfBlocks * 2; - memsetParams.height = 1; - - checkCudaErrors(cudaGraphCreate(&graph, 0)); - checkCudaErrors(cudaGraphAddMemcpyNode(&memcpyNode, graph, NULL, 0, &memcpyParams)); - checkCudaErrors(cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams)); - - nodeDependencies.push_back(memsetNode); - nodeDependencies.push_back(memcpyNode); - - void *kernelArgs[4] = {(void *)&inputVec_d, (void *)&outputVec_d, &inputSize, &numOfBlocks}; - - kernelNodeParams.func = (void *)reduce; - kernelNodeParams.gridDim = dim3(numOfBlocks, 1, 1); - kernelNodeParams.blockDim = dim3(THREADS_PER_BLOCK, 1, 1); - kernelNodeParams.sharedMemBytes = 0; - kernelNodeParams.kernelParams = (void **)kernelArgs; - kernelNodeParams.extra = NULL; - - checkCudaErrors(cudaGraphAddKernelNode( - &kernelNode, graph, nodeDependencies.data(), nodeDependencies.size(), &kernelNodeParams)); - - nodeDependencies.clear(); - nodeDependencies.push_back(kernelNode); - - memset(&memsetParams, 0, sizeof(memsetParams)); - memsetParams.dst = result_d; - memsetParams.value = 0; - memsetParams.elementSize = sizeof(float); - memsetParams.width = 2; - memsetParams.height = 1; - checkCudaErrors(cudaGraphAddMemsetNode(&memsetNode, graph, NULL, 0, &memsetParams)); - - nodeDependencies.push_back(memsetNode); - - memset(&kernelNodeParams, 0, sizeof(kernelNodeParams)); - kernelNodeParams.func = (void *)reduceFinal; - kernelNodeParams.gridDim = dim3(1, 1, 1); - kernelNodeParams.blockDim = dim3(THREADS_PER_BLOCK, 1, 1); - kernelNodeParams.sharedMemBytes = 0; - void *kernelArgs2[3] = {(void *)&outputVec_d, (void *)&result_d, &numOfBlocks}; - kernelNodeParams.kernelParams = kernelArgs2; - kernelNodeParams.extra = NULL; - - checkCudaErrors(cudaGraphAddKernelNode( - &kernelNode, graph, nodeDependencies.data(), nodeDependencies.size(), &kernelNodeParams)); - nodeDependencies.clear(); - nodeDependencies.push_back(kernelNode); - - memset(&memcpyParams, 0, sizeof(memcpyParams)); - - memcpyParams.srcArray = NULL; - memcpyParams.srcPos = make_cudaPos(0, 0, 0); - memcpyParams.srcPtr = make_cudaPitchedPtr(result_d, sizeof(double), 1, 1); - memcpyParams.dstArray = NULL; - memcpyParams.dstPos = make_cudaPos(0, 0, 0); - memcpyParams.dstPtr = make_cudaPitchedPtr(&result_h, sizeof(double), 1, 1); - memcpyParams.extent = make_cudaExtent(sizeof(double), 1, 1); - memcpyParams.kind = cudaMemcpyDeviceToHost; - checkCudaErrors( - cudaGraphAddMemcpyNode(&memcpyNode, graph, nodeDependencies.data(), nodeDependencies.size(), &memcpyParams)); - nodeDependencies.clear(); - nodeDependencies.push_back(memcpyNode); - - cudaGraphNode_t hostNode; - cudaHostNodeParams hostParams = {0}; - hostParams.fn = myHostNodeCallback; - callBackData_t hostFnData; - hostFnData.data = &result_h; - hostFnData.fn_name = "cudaGraphsManual"; - hostParams.userData = &hostFnData; - - checkCudaErrors( - cudaGraphAddHostNode(&hostNode, graph, nodeDependencies.data(), nodeDependencies.size(), &hostParams)); - - cudaGraphNode_t *nodes = NULL; - size_t numNodes = 0; - checkCudaErrors(cudaGraphGetNodes(graph, nodes, &numNodes)); - printf("\nNum of nodes in the graph created manually = %zu\n", numNodes); - - cudaGraphExec_t graphExec; - checkCudaErrors(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0)); - - cudaGraph_t clonedGraph; - cudaGraphExec_t clonedGraphExec; - checkCudaErrors(cudaGraphClone(&clonedGraph, graph)); - checkCudaErrors(cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0)); - - for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { - checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph)); - } - - checkCudaErrors(cudaStreamSynchronize(streamForGraph)); - - printf("Cloned Graph Output.. \n"); - for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { - checkCudaErrors(cudaGraphLaunch(clonedGraphExec, streamForGraph)); - } - checkCudaErrors(cudaStreamSynchronize(streamForGraph)); - - checkCudaErrors(cudaGraphExecDestroy(graphExec)); - checkCudaErrors(cudaGraphExecDestroy(clonedGraphExec)); - checkCudaErrors(cudaGraphDestroy(graph)); - checkCudaErrors(cudaGraphDestroy(clonedGraph)); - checkCudaErrors(cudaStreamDestroy(streamForGraph)); -} - -void cudaGraphsUsingStreamCapture(float *inputVec_h, - float *inputVec_d, - double *outputVec_d, - double *result_d, - size_t inputSize, - size_t numOfBlocks) -{ - cudaStream_t stream1, stream2, stream3, streamForGraph; - cudaEvent_t forkStreamEvent, memsetEvent1, memsetEvent2; - cudaGraph_t graph; - double result_h = 0.0; - - checkCudaErrors(cudaStreamCreate(&stream1)); - checkCudaErrors(cudaStreamCreate(&stream2)); - checkCudaErrors(cudaStreamCreate(&stream3)); - checkCudaErrors(cudaStreamCreate(&streamForGraph)); - - checkCudaErrors(cudaEventCreate(&forkStreamEvent)); - checkCudaErrors(cudaEventCreate(&memsetEvent1)); - checkCudaErrors(cudaEventCreate(&memsetEvent2)); - - checkCudaErrors(cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal)); - - checkCudaErrors(cudaEventRecord(forkStreamEvent, stream1)); - checkCudaErrors(cudaStreamWaitEvent(stream2, forkStreamEvent, 0)); - checkCudaErrors(cudaStreamWaitEvent(stream3, forkStreamEvent, 0)); - - checkCudaErrors(cudaMemcpyAsync(inputVec_d, inputVec_h, sizeof(float) * inputSize, cudaMemcpyDefault, stream1)); - - checkCudaErrors(cudaMemsetAsync(outputVec_d, 0, sizeof(double) * numOfBlocks, stream2)); - - checkCudaErrors(cudaEventRecord(memsetEvent1, stream2)); - - checkCudaErrors(cudaMemsetAsync(result_d, 0, sizeof(double), stream3)); - checkCudaErrors(cudaEventRecord(memsetEvent2, stream3)); - - checkCudaErrors(cudaStreamWaitEvent(stream1, memsetEvent1, 0)); - - reduce<<>>(inputVec_d, outputVec_d, inputSize, numOfBlocks); - - checkCudaErrors(cudaStreamWaitEvent(stream1, memsetEvent2, 0)); - - reduceFinal<<<1, THREADS_PER_BLOCK, 0, stream1>>>(outputVec_d, result_d, numOfBlocks); - checkCudaErrors(cudaMemcpyAsync(&result_h, result_d, sizeof(double), cudaMemcpyDefault, stream1)); - - callBackData_t hostFnData = {0}; - hostFnData.data = &result_h; - hostFnData.fn_name = "cudaGraphsUsingStreamCapture"; - cudaHostFn_t fn = myHostNodeCallback; - checkCudaErrors(cudaLaunchHostFunc(stream1, fn, &hostFnData)); - checkCudaErrors(cudaStreamEndCapture(stream1, &graph)); - - cudaGraphNode_t *nodes = NULL; - size_t numNodes = 0; - checkCudaErrors(cudaGraphGetNodes(graph, nodes, &numNodes)); - printf("\nNum of nodes in the graph created using stream capture API = %zu\n", numNodes); - - cudaGraphExec_t graphExec; - checkCudaErrors(cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0)); - - cudaGraph_t clonedGraph; - cudaGraphExec_t clonedGraphExec; - checkCudaErrors(cudaGraphClone(&clonedGraph, graph)); - checkCudaErrors(cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0)); - - for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { - checkCudaErrors(cudaGraphLaunch(graphExec, streamForGraph)); - } - - checkCudaErrors(cudaStreamSynchronize(streamForGraph)); - - printf("Cloned Graph Output.. \n"); - for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { - checkCudaErrors(cudaGraphLaunch(clonedGraphExec, streamForGraph)); - } - - checkCudaErrors(cudaStreamSynchronize(streamForGraph)); - - checkCudaErrors(cudaGraphExecDestroy(graphExec)); - checkCudaErrors(cudaGraphExecDestroy(clonedGraphExec)); - checkCudaErrors(cudaGraphDestroy(graph)); - checkCudaErrors(cudaGraphDestroy(clonedGraph)); - checkCudaErrors(cudaStreamDestroy(stream1)); - checkCudaErrors(cudaStreamDestroy(stream2)); - checkCudaErrors(cudaStreamDestroy(streamForGraph)); -} - -int main(int argc, char **argv) -{ - size_t size = 1 << 24; // number of elements to reduce - size_t maxBlocks = 512; - - // This will pick the best possible CUDA capable device - int devID = findCudaDevice(argc, (const char **)argv); - - printf("%zu elements\n", size); - printf("threads per block = %d\n", THREADS_PER_BLOCK); - printf("Graph Launch iterations = %d\n", GRAPH_LAUNCH_ITERATIONS); - - float *inputVec_d = NULL, *inputVec_h = NULL; - double *outputVec_d = NULL, *result_d; - - checkCudaErrors(cudaMallocHost(&inputVec_h, sizeof(float) * size)); - checkCudaErrors(cudaMalloc(&inputVec_d, sizeof(float) * size)); - checkCudaErrors(cudaMalloc(&outputVec_d, sizeof(double) * maxBlocks)); - checkCudaErrors(cudaMalloc(&result_d, sizeof(double))); - - init_input(inputVec_h, size); - - cudaGraphsManual(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks); - cudaGraphsUsingStreamCapture(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks); - - checkCudaErrors(cudaFree(inputVec_d)); - checkCudaErrors(cudaFree(outputVec_d)); - checkCudaErrors(cudaFree(result_d)); - checkCudaErrors(cudaFreeHost(inputVec_h)); - return EXIT_SUCCESS; -} diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cuh b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cuh new file mode 100644 index 00000000..276ae2ac --- /dev/null +++ b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs.cuh @@ -0,0 +1,106 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Shared code for the simpleCudaGraphs sample. Both the explicit-graph and + * stream-capture demos build a graph around the same two-pass reduction, so the + * reduction kernels, the host callback and its data type, and the input-fill + * helper live here to avoid duplicating them across the two .cu files. + */ + +#pragma once + +#include +#include +#include +#include + +#define THREADS_PER_BLOCK 512 + +// Payload passed to the host-callback node: the demo name and the reduced result. +typedef struct callBackData +{ + const char *fn_name; + double *data; +} callBackData_t; + +// Host-callback node body: prints the reduced result, then resets it for the next launch. +inline void CUDART_CB myHostNodeCallback(void *data) +{ + callBackData_t *tmp = (callBackData_t *)(data); + double *result = (double *)(tmp->data); + printf("[%s] Host callback final reduced sum = %lf\n", tmp->fn_name, *result); + *result = 0.0; // reset the result +} + +// Fill the host input buffer with fresh pseudo-random values. Called before each +// graph launch so the same graph processes different data every iteration. +inline void init_input(float *inputVec, size_t size) +{ + for (size_t i = 0; i < size; i++) + inputVec[i] = (rand() & 0xFF) / (float)RAND_MAX; +} + +// Pass 1: reduce the input float vector to one partial sum per block. +__global__ void reduce(float *inputVec, double *outputVec, size_t inputSize, size_t outputSize) +{ + typedef cub::BlockReduce BlockReduceT; + __shared__ typename BlockReduceT::TempStorage temp_storage; + + size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x; + + // Each thread adds its assigned elements into a local partial sum + double temp_sum = 0.0; + for (size_t i = globaltid; i < inputSize; i += (size_t)gridDim.x * blockDim.x) + temp_sum += (double)inputVec[i]; + + // CUB reduces all per-thread partial sums to a single block sum; result lands on thread 0 + double block_sum = BlockReduceT(temp_storage).Sum(temp_sum); + + if (threadIdx.x == 0 && blockIdx.x < outputSize) + outputVec[blockIdx.x] = block_sum; +} + +// Pass 2: reduce the per-block partial sums to a single scalar result. +__global__ void reduceFinal(double *inputVec, double *result, size_t inputSize) +{ + typedef cub::BlockReduce BlockReduceT; + __shared__ typename BlockReduceT::TempStorage temp_storage; + + size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x; + + // Each thread adds its assigned elements into a local partial sum + double temp_sum = 0.0; + for (size_t i = globaltid; i < inputSize; i += (size_t)gridDim.x * blockDim.x) + temp_sum += (double)inputVec[i]; + + // CUB reduces all per-thread partial sums to a single block sum; result lands on thread 0 + double block_sum = BlockReduceT(temp_storage).Sum(temp_sum); + + if (threadIdx.x == 0) + result[0] = block_sum; +} diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_capture.cu b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_capture.cu new file mode 100644 index 00000000..0cb8a830 --- /dev/null +++ b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_capture.cu @@ -0,0 +1,156 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Demonstrates CUDA Graph construction via stream capture. + * + * Operations are issued to a stream between cudaStreamBeginCapture and + * cudaStreamEndCapture. Instead of executing, they are recorded and the + * runtime automatically builds this graph: + * + * memcpy (H2D) -> reduce -> reduceFinal -> memcpy (D2H) -> host callback + * + * The workload is a two-pass reduction: an input float vector is reduced to + * per-block partial sums (reduce), then to a single double result (reduceFinal). + * The graph is instantiated once and launched GRAPH_LAUNCH_ITERATIONS times, + * then cloned and launched again. + */ + +#include "simpleCudaGraphs.cuh" + +#include +#include + +#define GRAPH_LAUNCH_ITERATIONS 3 + +void cudaGraphsUsingStreamCapture(float *inputVec_h, + float *inputVec_d, + double *outputVec_d, + double *result_d, + size_t inputSize, + size_t numOfBlocks) +{ + cudaStream_t stream1, streamForGraph; + cudaGraph_t graph; + double result_h = 0.0; + + cudaStreamCreate(&stream1); + cudaStreamCreate(&streamForGraph); + + // Begin capture: operations issued to stream1 are recorded, not executed. + cudaStreamBeginCapture(stream1, cudaStreamCaptureModeGlobal); + + cudaMemcpyAsync(inputVec_d, inputVec_h, sizeof(float) * inputSize, cudaMemcpyDefault, stream1); + + reduce<<>>(inputVec_d, outputVec_d, inputSize, numOfBlocks); + + reduceFinal<<<1, THREADS_PER_BLOCK, 0, stream1>>>(outputVec_d, result_d, numOfBlocks); + cudaMemcpyAsync(&result_h, result_d, sizeof(double), cudaMemcpyDefault, stream1); + + callBackData_t hostFnData = {0}; + hostFnData.data = &result_h; + hostFnData.fn_name = "cudaGraphsUsingStreamCapture"; + cudaLaunchHostFunc(stream1, myHostNodeCallback, &hostFnData); + + // End capture: the runtime builds a graph from everything recorded above. + // The resulting graph is structurally identical to the one built manually. + cudaStreamEndCapture(stream1, &graph); + + size_t numNodes = 0; + cudaGraphGetNodes(graph, NULL, &numNodes); + printf("Graph node count: %zu\n", numNodes); + + // Instantiate the captured graph into an executable form. + cudaGraphExec_t graphExec; + cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0); + + // Demonstrates cudaGraphClone — in practice, cloning is useful when multiple CPU + // threads need to launch the same graph concurrently, each with its own independent graphExec. + cudaGraph_t clonedGraph; + cudaGraphExec_t clonedGraphExec; + cudaGraphClone(&clonedGraph, graph); + cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0); + + // Refill the host input before each launch so the graph's H2D copy processes + // new data every time — one instantiated graph reused for different data. + // The per-iteration sync ensures that copy finishes before we overwrite the buffer. + for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { + init_input(inputVec_h, inputSize); + cudaGraphLaunch(graphExec, streamForGraph); + cudaStreamSynchronize(streamForGraph); + } + + printf("\nCloned graph:\n"); + for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { + init_input(inputVec_h, inputSize); + cudaGraphLaunch(clonedGraphExec, streamForGraph); + cudaStreamSynchronize(streamForGraph); + } + + cudaGraphExecDestroy(graphExec); + cudaGraphExecDestroy(clonedGraphExec); + cudaGraphDestroy(graph); + cudaGraphDestroy(clonedGraph); + cudaStreamDestroy(stream1); + cudaStreamDestroy(streamForGraph); +} + +int main() +{ + size_t size = 1 << 24; // number of elements to reduce, 16M elements + size_t maxBlocks = 512; + + int devID = 0; + cudaSetDevice(devID); + + int major, minor, smCount; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); + printf("GPU Device %d: compute capability %d.%d, %d SMs\n\n", devID, major, minor, smCount); + + printf("Reducing %zu elements\n", size); + printf("Threads per block : %d\n", THREADS_PER_BLOCK); + printf("Graph launch iterations: %d\n\n", GRAPH_LAUNCH_ITERATIONS); + + float *inputVec_h = NULL, *inputVec_d = NULL; + double *outputVec_d = NULL, *result_d = NULL; + + cudaMallocHost(&inputVec_h, sizeof(float) * size); + cudaMalloc(&inputVec_d, sizeof(float) * size); + cudaMalloc(&outputVec_d, sizeof(double) * maxBlocks); + cudaMalloc(&result_d, sizeof(double)); + + printf("=== Stream Capture ===\n"); + cudaGraphsUsingStreamCapture(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks); + + cudaFree(inputVec_d); + cudaFree(outputVec_d); + cudaFree(result_d); + cudaFreeHost(inputVec_h); + return EXIT_SUCCESS; +} diff --git a/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_explicit.cu b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_explicit.cu new file mode 100644 index 00000000..2ea36ae0 --- /dev/null +++ b/cpp/3_CUDA_Features/simpleCudaGraphs/simpleCudaGraphs_explicit.cu @@ -0,0 +1,236 @@ +/* Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * * Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * * Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * * Neither the name of NVIDIA CORPORATION nor the names of its + * contributors may be used to endorse or promote products derived + * from this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + */ + +/* + * Demonstrates explicit CUDA Graph construction using the unified cudaGraphAddNode API. + * + * Nodes and edges are added one at a time to build this graph: + * + * memcpy (H2D) -> reduce -> reduceFinal -> memcpy (D2H) -> host callback + * + * The workload is a two-pass reduction: an input float vector is reduced to + * per-block partial sums (reduce), then to a single double result (reduceFinal). + * The graph is instantiated once and launched GRAPH_LAUNCH_ITERATIONS times, + * then cloned and launched again. + */ + +#include "simpleCudaGraphs.cuh" + +#include +#include +#include + +#define GRAPH_LAUNCH_ITERATIONS 3 + +void cudaGraphsManual(float *inputVec_h, + float *inputVec_d, + double *outputVec_d, + double *result_d, + size_t inputSize, + size_t numOfBlocks) +{ + cudaStream_t streamForGraph; + cudaGraph_t graph; + std::vector nodeDependencies; + cudaGraphNode_t memcpyNode, kernelNode; + double result_h = 0.0; + + cudaStreamCreate(&streamForGraph); + + // cudaGraphAddNode is the unified node-creation API: one call for every node type, + // driven by a cudaGraphNodeParams struct (a .type tag + a union of per-type params). + // It replaces the previously used cudaGraphAddMemcpyNode/cudaGraphAddKernelNode calls. The struct + // has reserved fields that must be zero, so it is re-zeroed with "= {}" before each node below. + cudaGraphNodeParams nodeParams = {}; + cudaMemcpy3DParms memcpyParams = {0}; + + memcpyParams.srcArray = NULL; + memcpyParams.srcPos = make_cudaPos(0, 0, 0); + memcpyParams.srcPtr = make_cudaPitchedPtr(inputVec_h, sizeof(float) * inputSize, inputSize, 1); + memcpyParams.dstArray = NULL; + memcpyParams.dstPos = make_cudaPos(0, 0, 0); + memcpyParams.dstPtr = make_cudaPitchedPtr(inputVec_d, sizeof(float) * inputSize, inputSize, 1); + memcpyParams.extent = make_cudaExtent(sizeof(float) * inputSize, 1, 1); + memcpyParams.kind = cudaMemcpyHostToDevice; + + // Create an empty graph; nodes and edges will be added below. + cudaGraphCreate(&graph, 0); + + // Node 1: H2D memcpy — no dependencies (NULL, 0), so it can start immediately. + // For a memcpy node the cudaMemcpy3DParms goes into nodeParams.memcpy.copyParams + // (the .memcpy union member is a wrapper struct, so the copy descriptor nests one level deeper). + nodeParams = {}; + nodeParams.type = cudaGraphNodeTypeMemcpy; + nodeParams.memcpy.copyParams = memcpyParams; + cudaGraphAddNode(&memcpyNode, graph, NULL, /*dependencyData=*/NULL, 0, &nodeParams); + + // Make the next node wait for this memcpy to finish before starting. + nodeDependencies.push_back(memcpyNode); + + void *kernelArgs[4] = {(void *)&inputVec_d, (void *)&outputVec_d, &inputSize, &numOfBlocks}; + + // Node 2: first reduction kernel — depends on the H2D memcpy completing. + // Kernel params are set directly on the .kernel union member. + nodeParams = {}; + nodeParams.type = cudaGraphNodeTypeKernel; + nodeParams.kernel.func = (void *)reduce; + nodeParams.kernel.gridDim = dim3(numOfBlocks, 1, 1); + nodeParams.kernel.blockDim = dim3(THREADS_PER_BLOCK, 1, 1); + nodeParams.kernel.sharedMemBytes = 0; + nodeParams.kernel.kernelParams = (void **)kernelArgs; + nodeParams.kernel.extra = NULL; + cudaGraphAddNode( + &kernelNode, graph, nodeDependencies.data(), /*dependencyData=*/NULL, nodeDependencies.size(), &nodeParams); + + // Move dependency forward: the next node waits for this kernel. + nodeDependencies.clear(); + nodeDependencies.push_back(kernelNode); + + void *kernelArgs2[3] = {(void *)&outputVec_d, (void *)&result_d, &numOfBlocks}; + + // Node 3: final reduction kernel — depends on Node 2. + nodeParams = {}; + nodeParams.type = cudaGraphNodeTypeKernel; + nodeParams.kernel.func = (void *)reduceFinal; + nodeParams.kernel.gridDim = dim3(1, 1, 1); + nodeParams.kernel.blockDim = dim3(THREADS_PER_BLOCK, 1, 1); + nodeParams.kernel.sharedMemBytes = 0; + nodeParams.kernel.kernelParams = kernelArgs2; + nodeParams.kernel.extra = NULL; + cudaGraphAddNode( + &kernelNode, graph, nodeDependencies.data(), /*dependencyData=*/NULL, nodeDependencies.size(), &nodeParams); + nodeDependencies.clear(); + nodeDependencies.push_back(kernelNode); + + memset(&memcpyParams, 0, sizeof(memcpyParams)); + + memcpyParams.srcArray = NULL; + memcpyParams.srcPos = make_cudaPos(0, 0, 0); + memcpyParams.srcPtr = make_cudaPitchedPtr(result_d, sizeof(double), 1, 1); + memcpyParams.dstArray = NULL; + memcpyParams.dstPos = make_cudaPos(0, 0, 0); + memcpyParams.dstPtr = make_cudaPitchedPtr(&result_h, sizeof(double), 1, 1); + memcpyParams.extent = make_cudaExtent(sizeof(double), 1, 1); + memcpyParams.kind = cudaMemcpyDeviceToHost; + + // Node 4: D2H memcpy — copies the scalar result back to the host. + // Again the copy descriptor nests in nodeParams.memcpy.copyParams. + nodeParams = {}; + nodeParams.type = cudaGraphNodeTypeMemcpy; + nodeParams.memcpy.copyParams = memcpyParams; + cudaGraphAddNode(&memcpyNode, graph, nodeDependencies.data(), /*dependencyData=*/NULL, nodeDependencies.size(), &nodeParams); + nodeDependencies.clear(); + nodeDependencies.push_back(memcpyNode); + + cudaGraphNode_t hostNode; + callBackData_t hostFnData; + hostFnData.data = &result_h; + hostFnData.fn_name = "cudaGraphsManual"; + + // Node 5: host callback — runs on the CPU after the D2H copy completes. + // The .host member is cudaHostNodeParamsV2 (fn + userData, plus a syncMode field + // left at 0 by the zero-init above). + nodeParams = {}; + nodeParams.type = cudaGraphNodeTypeHost; + nodeParams.host.fn = myHostNodeCallback; + nodeParams.host.userData = &hostFnData; + cudaGraphAddNode(&hostNode, graph, nodeDependencies.data(), /*dependencyData=*/NULL, nodeDependencies.size(), &nodeParams); + + size_t numNodes = 0; + cudaGraphGetNodes(graph, NULL, &numNodes); + printf("Graph node count: %zu\n", numNodes); + + // Instantiate: compile the graph into an executable form (one-time cost). + // This is where CUDA optimizes the schedule; repeated launches reuse this. + cudaGraphExec_t graphExec; + cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0); + + // Demonstrates cudaGraphClone — in practice, cloning is useful when multiple CPU + // threads need to launch the same graph concurrently, each with its own independent graphExec. + cudaGraph_t clonedGraph; + cudaGraphExec_t clonedGraphExec; + cudaGraphClone(&clonedGraph, graph); + cudaGraphInstantiate(&clonedGraphExec, clonedGraph, NULL, NULL, 0); + + // Refill the host input before each launch so the graph's H2D copy processes + // new data every time — one instantiated graph reused for different data. + // The per-iteration sync ensures that copy finishes before we overwrite the buffer. + for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { + init_input(inputVec_h, inputSize); + cudaGraphLaunch(graphExec, streamForGraph); + cudaStreamSynchronize(streamForGraph); + } + + printf("\nCloned graph:\n"); + for (int i = 0; i < GRAPH_LAUNCH_ITERATIONS; i++) { + init_input(inputVec_h, inputSize); + cudaGraphLaunch(clonedGraphExec, streamForGraph); + cudaStreamSynchronize(streamForGraph); + } + + cudaGraphExecDestroy(graphExec); + cudaGraphExecDestroy(clonedGraphExec); + cudaGraphDestroy(graph); + cudaGraphDestroy(clonedGraph); + cudaStreamDestroy(streamForGraph); +} + +int main() +{ + size_t size = 1 << 24; // number of elements to reduce, 16M elements + size_t maxBlocks = 512; + + int devID = 0; + cudaSetDevice(devID); + + int major, minor, smCount; + cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, devID); + cudaDeviceGetAttribute(&minor, cudaDevAttrComputeCapabilityMinor, devID); + cudaDeviceGetAttribute(&smCount, cudaDevAttrMultiProcessorCount, devID); + printf("GPU Device %d: compute capability %d.%d, %d SMs\n\n", devID, major, minor, smCount); + + printf("Reducing %zu elements\n", size); + printf("Threads per block : %d\n", THREADS_PER_BLOCK); + printf("Graph launch iterations: %d\n\n", GRAPH_LAUNCH_ITERATIONS); + + float *inputVec_h = NULL, *inputVec_d = NULL; + double *outputVec_d = NULL, *result_d = NULL; + + cudaMallocHost(&inputVec_h, sizeof(float) * size); + cudaMalloc(&inputVec_d, sizeof(float) * size); + cudaMalloc(&outputVec_d, sizeof(double) * maxBlocks); + cudaMalloc(&result_d, sizeof(double)); + + printf("=== Explicit Graph Construction ===\n"); + cudaGraphsManual(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks); + + cudaFree(inputVec_d); + cudaFree(outputVec_d); + cudaFree(result_d); + cudaFreeHost(inputVec_h); + return EXIT_SUCCESS; +} diff --git a/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/extensions.json b/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/tf32TensorCoreGemm/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/tf32TensorCoreGemm/CMakeLists.txt b/cpp/3_CUDA_Features/tf32TensorCoreGemm/CMakeLists.txt index 6599a6ff..8d8e13a9 100644 --- a/cpp/3_CUDA_Features/tf32TensorCoreGemm/CMakeLists.txt +++ b/cpp/3_CUDA_Features/tf32TensorCoreGemm/CMakeLists.txt @@ -1,23 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(tf32TensorCoreGemm LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -26,16 +21,11 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for tf32TensorCoreGemm add_executable(tf32TensorCoreGemm tf32TensorCoreGemm.cu) target_compile_options(tf32TensorCoreGemm PRIVATE $<$:--extended-lambda>) target_compile_features(tf32TensorCoreGemm PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(tf32TensorCoreGemm PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/c_cpp_properties.json b/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/extensions.json b/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/CMakeLists.txt b/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/CMakeLists.txt index e3046b34..3429ba30 100644 --- a/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/CMakeLists.txt +++ b/cpp/3_CUDA_Features/warpAggregatedAtomicsCG/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(warpAggregatedAtomicsCG LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for warpAggregatedAtomicsCG add_executable(warpAggregatedAtomicsCG warpAggregatedAtomicsCG.cu) target_compile_options(warpAggregatedAtomicsCG PRIVATE $<$:--extended-lambda>) target_compile_features(warpAggregatedAtomicsCG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(warpAggregatedAtomicsCG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/CMakeLists.txt b/cpp/4_CUDA_Libraries/CMakeLists.txt index 0e5dfe9d..cec10106 100644 --- a/cpp/4_CUDA_Libraries/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/CMakeLists.txt @@ -1,39 +1,13 @@ -add_subdirectory(FilterBorderControlNPP) -add_subdirectory(MersenneTwisterGP11213) -add_subdirectory(batchCUBLAS) -add_subdirectory(boxFilterNPP) -add_subdirectory(cannyEdgeDetectorNPP) -add_subdirectory(conjugateGradient) add_subdirectory(conjugateGradientCudaGraphs) add_subdirectory(conjugateGradientMultiBlockCG) add_subdirectory(conjugateGradientMultiDeviceCG) -add_subdirectory(conjugateGradientPrecond) -add_subdirectory(conjugateGradientUM) add_subdirectory(cubDeviceFind) add_subdirectory(cubDeviceSegmentedScan) add_subdirectory(cubDeviceTransform) add_subdirectory(cudaNvSci) -add_subdirectory(cuSolverDn_LinearSolver) -add_subdirectory(cuSolverRf) -add_subdirectory(cuSolverSp_LinearSolver) -add_subdirectory(cuSolverSp_LowlevelCholesky) -add_subdirectory(cuSolverSp_LowlevelQR) -add_subdirectory(freeImageInteropNPP) -add_subdirectory(histEqualizationNPP) add_subdirectory(jitLto) add_subdirectory(libcuxxMdspan) add_subdirectory(libcuxxRandom) add_subdirectory(lineOfSight) -add_subdirectory(matrixMulCUBLAS) -add_subdirectory(nvJPEG) -add_subdirectory(nvJPEG_encoder) add_subdirectory(oceanFFT) add_subdirectory(randomFog) -add_subdirectory(simpleCUBLAS) -add_subdirectory(simpleCUBLASXT) -add_subdirectory(simpleCUBLAS_LU) -add_subdirectory(simpleCUFFT) -add_subdirectory(simpleCUFFT_2d_MGPU) -add_subdirectory(simpleCUFFT_MGPU) -add_subdirectory(simpleCUFFT_callback) -add_subdirectory(watershedSegmentationNPP) diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/CMakeLists.txt deleted file mode 100644 index ee6a980f..00000000 --- a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/CMakeLists.txt +++ /dev/null @@ -1,74 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(FilterBorderControlNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories( - ../../../Common - ../../../Common/UtilNPP -) - -# Source file -find_package(FreeImage) - -if(${FreeImage_FOUND}) - # Add target for FilterBorderControlNPP - add_executable(FilterBorderControlNPP FilterBorderControlNPP.cpp) - -target_compile_options(FilterBorderControlNPP PRIVATE $<$:--extended-lambda>) - -target_compile_features(FilterBorderControlNPP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(FilterBorderControlNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(FilterBorderControlNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} - ${FreeImage_INCLUDE_DIRS} - ) - - target_link_libraries(FilterBorderControlNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppif - CUDA::nppitc - CUDA::nppidei - CUDA::nppial - CUDA::cudart - ${FreeImage_LIBRARIES} - ) - - # Copy data files to output directory - add_custom_command(TARGET FilterBorderControlNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CMAKE_CURRENT_SOURCE_DIR}/data - ${CMAKE_CURRENT_BINARY_DIR}/data - ) - if(WIN32) - add_custom_command(TARGET FilterBorderControlNPP - POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy - ${FreeImage_LIBRARY}/../FreeImage.dll - ${CMAKE_CURRENT_BINARY_DIR}/$ - ) - endif() -else() - message(STATUS "FreeImage not found - will not build sample 'FilterBorderControlNPP'") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/FilterBorderControlNPP.cpp b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/FilterBorderControlNPP.cpp deleted file mode 100644 index 3aa2d152..00000000 --- a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/FilterBorderControlNPP.cpp +++ /dev/null @@ -1,602 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// USING NPP IMAGE FILTER SOURCE IMAGE BORDER CONTROL -// This sample demonstrates how any border version of an NPP filtering function -// can be used in the most common mode, with border control enabled. Mentioned -// functions can be used to duplicate the results of the equivalent non-border -// version of the NPP functions. They can be also used for enabling and -// disabling border control on various source image edges depending on what -// portion of the source image is being used as input. - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#pragma warning(disable : 4819) -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -inline int cudaDeviceInit(int argc, const char **argv) -{ - int deviceCount; - checkCudaErrors(cudaGetDeviceCount(&deviceCount)); - - if (deviceCount == 0) { - std::cerr << "CUDA error: no devices supporting CUDA." << std::endl; - exit(EXIT_FAILURE); - } - - int dev = findCudaDevice(argc, argv); - - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, dev); - std::cerr << "cudaSetDevice GPU" << dev << " = " << deviceProp.name << std::endl; - - checkCudaErrors(cudaSetDevice(dev)); - - return dev; -} - -int main(int argc, char *argv[]) -{ - printf("%s Starting...\n\n", argv[0]); - - try { - const char *inputFile = "teapot512.pgm"; - std::string sFilename = inputFile; - std::string sOutputDir = "./"; - - cudaDeviceInit(argc, (const char **)argv); - - NppStreamContext nppStreamCtx; - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError_t cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - char *filePath; - - if (checkCmdLineFlag(argc, (const char **)argv, "input")) { - getCmdLineArgumentString(argc, (const char **)argv, "input", &filePath); - } - else { - filePath = sdkFindFilePath(inputFile, argv[0]); - } - - if (!filePath) { - std::cerr << "Couldn't find input file " << sFilename << std::endl; - exit(1); - } - - sFilename = filePath; - - // if we specify the filename at the command line, then we only test - // sFilename[0]. - int file_errors = 0; - std::ifstream infile(sFilename.data(), std::ifstream::in); - - if (infile.good()) { - std::cout << "gradientFilterBorderNPP opened <" << sFilename.data() << "> successfully!" << std::endl; - file_errors = 0; - infile.close(); - } - else { - std::cout << "gradientFilterBorderNPP unable to open <" << sFilename.data() << ">" << std::endl; - file_errors++; - infile.close(); - } - - if (file_errors > 0) { - cudaDeviceReset(); - exit(EXIT_FAILURE); - } - - std::string sResultBaseFilename = sFilename; - - std::string::size_type dot = sResultBaseFilename.rfind('.'); - - if (dot != std::string::npos) { - sResultBaseFilename = sResultBaseFilename.substr(0, dot); - } - - std::string sResultXFilename = sOutputDir + sFilename + "_gradientVectorPrewittBorderX_Vertical.pgm"; - std::string sResultYFilename = sResultBaseFilename; - - // sResultXFilename += "_gradientVectorPrewittBorderX_Vertical.pgm"; - sResultYFilename += "_gradientVectorPrewittBorderY_Horizontal.pgm"; - - // if (checkCmdLineFlag(argc, (const char **)argv, "output")) - // { - // char *outputFilePath; - // getCmdLineArgumentString(argc, (const char **)argv, "output", - // &outputFilePath); sResultBaseFilename = outputFilePath; - // } - - // declare a host image object for an 8-bit grayscale image - npp::ImageCPU_8u_C1 oHostSrc; - // load gray-scale image from disk - npp::loadImage(sFilename, oHostSrc); - // declare a device image and copy construct from the host image, - // i.e. upload host to device - npp::ImageNPP_8u_C1 oDeviceSrc(oHostSrc); - - NppiSize oSrcSize = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - NppiPoint oSrcOffset = {0, 0}; - - // create struct with ROI size - NppiSize oSizeROI = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - // allocate device destination images of appropriatedly size - npp::ImageNPP_16s_C1 oDeviceDstX(oSizeROI.width, oSizeROI.height); - npp::ImageNPP_16s_C1 oDeviceDstY(oSizeROI.width, oSizeROI.height); - - // run Prewitt edge detection gradient vector filter - NPP_CHECK_NPP(nppiGradientVectorPrewittBorder_8u16s_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oSrcSize, - oSrcOffset, - oDeviceDstX.data(), - oDeviceDstX.pitch(), - oDeviceDstY.data(), - oDeviceDstY.pitch(), - 0, - 0, - 0, - 0, - oSizeROI, - NPP_MASK_SIZE_3_X_3, - nppiNormL1, - NPP_BORDER_REPLICATE, - nppStreamCtx)); - - // allocate device destination images of appropriatedly size - npp::ImageNPP_8u_C1 oDeviceDstOutX(oSizeROI.width, oSizeROI.height); - npp::ImageNPP_8u_C1 oDeviceDstOutY(oSizeROI.width, oSizeROI.height); - - // convert 16s_C1 result images to binary 8u_C1 output images using constant - // value to adjust amount of visible detail - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstX.data(), - oDeviceDstX.pitch(), - 32, - oDeviceDstOutX.data(), - oDeviceDstOutX.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstY.data(), - oDeviceDstY.pitch(), - 32, - oDeviceDstOutY.data(), - oDeviceDstOutY.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - - // create host images for the results - npp::ImageCPU_8u_C1 oHostDstX(oDeviceDstOutX.size()); - npp::ImageCPU_8u_C1 oHostDstY(oDeviceDstOutY.size()); - // and copy the device result data into them - oDeviceDstOutX.copyTo(oHostDstX.data(), oHostDstX.pitch()); - oDeviceDstOutY.copyTo(oHostDstY.data(), oHostDstY.pitch()); - - saveImage(sResultXFilename, oHostDstX); - std::cout << "Saved image: " << sResultXFilename << std::endl; - saveImage(sResultYFilename, oHostDstY); - std::cout << "Saved image: " << sResultYFilename << std::endl; - - // now use the Prewitt gradient border filter function in such a way that no - // border replication operations will be applied - - // create a Prewitt filter mask size object, Prewitt uses a 3x3 filter - // kernel - NppiSize oMaskSize = {3, 3}; - // create a size object for the enlarged source image - NppiSize oEnlargedSrcSize = {oSrcSize.width + oMaskSize.width - 1, oSrcSize.height + oMaskSize.height - 1}; - - // create an enlarged device source image - npp::ImageNPP_8u_C1 oEnlargedDeviceSrc(oEnlargedSrcSize.width, oEnlargedSrcSize.height); - - // copy and enlarge the original device source image and surround it with a - // white edge (border) - NPP_CHECK_NPP(nppiCopyConstBorder_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oSrcSize, - oEnlargedDeviceSrc.data(), - oEnlargedDeviceSrc.pitch(), - oEnlargedSrcSize, - oMaskSize.width / 2, - oMaskSize.height / 2, - 255, - nppStreamCtx)); - - // adjust oEnlargedDeviceSrc pixel pointer to point to the first pixel of - // the original source image in the enlarged source image - const Npp8u *pTemp = reinterpret_cast(oEnlargedDeviceSrc.data()); - pTemp += (oMaskSize.height / 2) * oEnlargedDeviceSrc.pitch(); - const Npp8u *pAdjustedSrc = reinterpret_cast((void *)(pTemp)); - pAdjustedSrc += oMaskSize.width / 2; - - // create device output images for the no source border results - npp::ImageNPP_8u_C1 oDeviceDstOutXNoBorders(oSizeROI.width, oSizeROI.height); - npp::ImageNPP_8u_C1 oDeviceDstOutYNoBorders(oSizeROI.width, oSizeROI.height); - - // tell the filter function what cartesian pixel position pAdjustedSrc is - // pointing to within the enlarged source image - oSrcOffset.x += oMaskSize.width / 2; - oSrcOffset.y += oMaskSize.height / 2; - - // run Prewitt edge detection gradient vector filter bypassing border - // control due to enlarged source image - NPP_CHECK_NPP(nppiGradientVectorPrewittBorder_8u16s_C1R_Ctx(pAdjustedSrc, - oEnlargedDeviceSrc.pitch(), - oEnlargedSrcSize, - oSrcOffset, - oDeviceDstX.data(), - oDeviceDstX.pitch(), - oDeviceDstY.data(), - oDeviceDstY.pitch(), - 0, - 0, - 0, - 0, - oSizeROI, - NPP_MASK_SIZE_3_X_3, - nppiNormL1, - NPP_BORDER_REPLICATE, - nppStreamCtx)); - - // convert 16s_C1 result images to binary 8u_C1 output images using constant - // value to adjust amount of visible detail - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstX.data(), - oDeviceDstX.pitch(), - 32, - oDeviceDstOutXNoBorders.data(), - oDeviceDstOutXNoBorders.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstY.data(), - oDeviceDstY.pitch(), - 32, - oDeviceDstOutYNoBorders.data(), - oDeviceDstOutYNoBorders.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - // create additional output files - std::string sResultXNoBordersFilename = sResultBaseFilename; - std::string sResultYNoBordersFilename = sResultBaseFilename; - - sResultXNoBordersFilename += "_gradientVectorPrewittBorderX_Vertical_WithNoSourceBorders.pgm"; - sResultYNoBordersFilename += "_gradientVectorPrewittBorderY_Horizontal_WithNoSourceBorders.pgm"; - - // copy the device result data into the host output images - oDeviceDstOutXNoBorders.copyTo(oHostDstX.data(), oHostDstX.pitch()); - oDeviceDstOutYNoBorders.copyTo(oHostDstY.data(), oHostDstY.pitch()); - - saveImage(sResultXNoBordersFilename, oHostDstX); - std::cout << "Saved image: " << sResultXNoBordersFilename << std::endl; - saveImage(sResultYNoBordersFilename, oHostDstY); - std::cout << "Saved image: " << sResultYNoBordersFilename << std::endl; - - // now diff the two output images, one using border control and one - // bypassing border control - - // create device output images for the diff results - npp::ImageNPP_8u_C1 oDeviceDstOutXDiff(oSizeROI.width, oSizeROI.height); - npp::ImageNPP_8u_C1 oDeviceDstOutYDiff(oSizeROI.width, oSizeROI.height); - - // diff the two 8u_C1 result images one with and one without border control - - NPP_CHECK_NPP(nppiAbsDiff_8u_C1R_Ctx(oDeviceDstOutXNoBorders.data(), - oDeviceDstOutXNoBorders.pitch(), - oDeviceDstOutX.data(), - oDeviceDstOutX.pitch(), - oDeviceDstOutXDiff.data(), - oDeviceDstOutXDiff.pitch(), - oSizeROI, - nppStreamCtx)); - - NPP_CHECK_NPP(nppiAbsDiff_8u_C1R_Ctx(oDeviceDstOutYNoBorders.data(), - oDeviceDstOutYNoBorders.pitch(), - oDeviceDstOutY.data(), - oDeviceDstOutY.pitch(), - oDeviceDstOutYDiff.data(), - oDeviceDstOutYDiff.pitch(), - oSizeROI, - nppStreamCtx)); - - // create additional output files - std::string sResultXDiffFilename = sResultBaseFilename; - std::string sResultYDiffFilename = sResultBaseFilename; - - sResultXDiffFilename += "_gradientVectorPrewittBorderX_Vertical_BorderDiffs.pgm"; - sResultYDiffFilename += "_gradientVectorPrewittBorderY_Horizontal_BorderDiffs.pgm"; - - // copy the device result data into the host output images - oDeviceDstOutXDiff.copyTo(oHostDstX.data(), oHostDstX.pitch()); - oDeviceDstOutYDiff.copyTo(oHostDstY.data(), oHostDstY.pitch()); - - saveImage(sResultXDiffFilename, oHostDstX); - std::cout << "Saved image: " << sResultXDiffFilename << std::endl; - saveImage(sResultYDiffFilename, oHostDstY); - std::cout << "Saved image: " << sResultYDiffFilename << std::endl; - - // if you closely examine the above difference files (recommend using GIMP - // for viewing using scaling with no interpolation) you will see several - // single pixel differences (white pixels) along the right and bottom edges - // of the default vs. borderless images this happens because border pixels - // in the original source image are duplicated when the filter kernels - // overlap the edge of the source image when using the first version of the - // filter call but are actually sampled from the enlarged source image when - // using the second version of the filter call the technique used in the - // second filter call can be used with any filter border function in NPP to - // duplicate results that would be generated from a non-border filter - // function call by filling the border pixel outside the embedded source - // image with the appropriate border pixel values - - // here is how to use border control to process a source image in multiple - // calls and get correct output in the destination image - - // since the source image pointer already points to the beginning of the - // source image in the enlarged source image it doesn't need changed - - // tighten up the top and left source image borders - this will enable - // border replication on the left and top borders of the original source - // image - oSrcOffset.x = 0; - oSrcOffset.y = 0; - // tighten up the right and bottom side source image borders - this will - // enable border replication on the right and bottom borders of the original - // source image - oEnlargedSrcSize.width = oSrcSize.width; - oEnlargedSrcSize.height = oSrcSize.height; - - // create device output images for the mixed edge results - npp::ImageNPP_8u_C1 oDeviceDstOutXMixedBorders(oSizeROI.width, oSizeROI.height); - npp::ImageNPP_8u_C1 oDeviceDstOutYMixedBorders(oSizeROI.width, oSizeROI.height); - - // shrink output ROI width so that only the left half of the destination - // image will be generated however since oEnlargedSrcSize.width is still set - // to oSrcSize.width then border control will be disabled when the filter - // needs to access source pixels beyond the right side of the left half of - // the source image - int nLeftWidth = oSizeROI.width / 2; - int nRightWidth = oSizeROI.width - nLeftWidth; - oSizeROI.width = nLeftWidth; - - // run Prewitt edge detection gradient vector filter to generate the left - // side of the output image - NPP_CHECK_NPP(nppiGradientVectorPrewittBorder_8u16s_C1R_Ctx(pAdjustedSrc, - oEnlargedDeviceSrc.pitch(), - oEnlargedSrcSize, - oSrcOffset, - oDeviceDstX.data(), - oDeviceDstX.pitch(), - oDeviceDstY.data(), - oDeviceDstY.pitch(), - 0, - 0, - 0, - 0, - oSizeROI, - NPP_MASK_SIZE_3_X_3, - nppiNormL1, - NPP_BORDER_REPLICATE, - nppStreamCtx)); - - // now move the enlarged source pointer to the horizontal middle of the - // enlarged source image and tell the function where it was moved to - pAdjustedSrc += nLeftWidth; - // and adjust the source offset parameter accordingly - this will in effect - // turn off border control for the left border allowing the necessary source - // pixels to be used - oSrcOffset.x += nLeftWidth; - - // update oSizeROI.width so that only enough destination pixels will be - // produced to fill the right half of the destination image - oSizeROI.width = nRightWidth; - - // run Prewitt edge detection gradient vector filter to generate the right - // side of the output image adjusting the destination image pointers - // appropriately - NPP_CHECK_NPP(nppiGradientVectorPrewittBorder_8u16s_C1R_Ctx(pAdjustedSrc, - oEnlargedDeviceSrc.pitch(), - oEnlargedSrcSize, - oSrcOffset, - oDeviceDstX.data() + nLeftWidth, - oDeviceDstX.pitch(), - oDeviceDstY.data() + nLeftWidth, - oDeviceDstY.pitch(), - 0, - 0, - 0, - 0, - oSizeROI, - NPP_MASK_SIZE_3_X_3, - nppiNormL1, - NPP_BORDER_REPLICATE, - nppStreamCtx)); - - // convert 16s_C1 result images to binary 8u_C1 output images using constant - // value to adjust amount of visible detail - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstX.data(), - oDeviceDstX.pitch(), - 32, - oDeviceDstOutXMixedBorders.data(), - oDeviceDstOutXMixedBorders.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - - NPP_CHECK_NPP(nppiCompareC_16s_C1R_Ctx(oDeviceDstY.data(), - oDeviceDstY.pitch(), - 32, - oDeviceDstOutYMixedBorders.data(), - oDeviceDstOutYMixedBorders.pitch(), - oSizeROI, - NPP_CMP_GREATER_EQ, - nppStreamCtx)); - // create additional output files - std::string sResultXMixedBordersFilename = sResultBaseFilename; - std::string sResultYMixedBordersFilename = sResultBaseFilename; - - sResultXMixedBordersFilename += "_gradientVectorPrewittBorderX_Vertical_WithMixedBorders.pgm"; - sResultYMixedBordersFilename += "_gradientVectorPrewittBorderY_Horizontal_WithMixedBorders.pgm"; - - // copy the device result data into the host output images - oDeviceDstOutXMixedBorders.copyTo(oHostDstX.data(), oHostDstX.pitch()); - oDeviceDstOutYMixedBorders.copyTo(oHostDstY.data(), oHostDstY.pitch()); - - saveImage(sResultXMixedBordersFilename, oHostDstX); - std::cout << "Saved image: " << sResultXMixedBordersFilename << std::endl; - saveImage(sResultYMixedBordersFilename, oHostDstY); - std::cout << "Saved image: " << sResultYMixedBordersFilename << std::endl; - - // diff the original 8u_C1 result images with border control and the mixed - // border control images, they should match (diff image will be all black) - - NPP_CHECK_NPP(nppiAbsDiff_8u_C1R_Ctx(oDeviceDstOutXMixedBorders.data(), - oDeviceDstOutXMixedBorders.pitch(), - oDeviceDstOutX.data(), - oDeviceDstOutX.pitch(), - oDeviceDstOutXDiff.data(), - oDeviceDstOutXDiff.pitch(), - oSizeROI, - nppStreamCtx)); - - NPP_CHECK_NPP(nppiAbsDiff_8u_C1R_Ctx(oDeviceDstOutYMixedBorders.data(), - oDeviceDstOutYMixedBorders.pitch(), - oDeviceDstOutY.data(), - oDeviceDstOutY.pitch(), - oDeviceDstOutYDiff.data(), - oDeviceDstOutYDiff.pitch(), - oSizeROI, - nppStreamCtx)); - - // create additional output files - std::string sResultXMixedDiffFilename = sResultBaseFilename; - std::string sResultYMixedDiffFilename = sResultBaseFilename; - - sResultXMixedDiffFilename += "_gradientVectorPrewittBorderX_Vertical_MixedBorderDiffs.pgm"; - sResultYMixedDiffFilename += "_gradientVectorPrewittBorderY_Horizontal_MixedBorderDiffs.pgm"; - - // copy the device result data into the host output images - oDeviceDstOutXDiff.copyTo(oHostDstX.data(), oHostDstX.pitch()); - oDeviceDstOutYDiff.copyTo(oHostDstY.data(), oHostDstY.pitch()); - - saveImage(sResultXMixedDiffFilename, oHostDstX); - std::cout << "Saved image: " << sResultXMixedDiffFilename << std::endl; - saveImage(sResultYMixedDiffFilename, oHostDstY); - std::cout << "Saved image: " << sResultYMixedDiffFilename << std::endl; - - nppiFree(oDeviceSrc.data()); - nppiFree(oDeviceDstX.data()); - nppiFree(oDeviceDstY.data()); - nppiFree(oDeviceDstOutX.data()); - nppiFree(oDeviceDstOutY.data()); - nppiFree(oDeviceDstOutXNoBorders.data()); - nppiFree(oDeviceDstOutYNoBorders.data()); - nppiFree(oDeviceDstOutXDiff.data()); - nppiFree(oDeviceDstOutYDiff.data()); - nppiFree(oDeviceDstOutXMixedBorders.data()); - nppiFree(oDeviceDstOutYMixedBorders.data()); - nppiFree(oEnlargedDeviceSrc.data()); - - cudaDeviceReset(); - exit(EXIT_SUCCESS); - } - catch (npp::Exception &rException) { - std::cerr << "Program error! The following exception occurred: \n"; - std::cerr << rException << std::endl; - std::cerr << "Aborting." << std::endl; - - cudaDeviceReset(); - exit(EXIT_FAILURE); - } - catch (...) { - std::cerr << "Program error! An unknow type of exception occurred. \n"; - std::cerr << "Aborting." << std::endl; - - cudaDeviceReset(); - exit(EXIT_FAILURE); - return -1; - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/README.md b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/README.md deleted file mode 100644 index 36f197cc..00000000 --- a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# FilterBorderControlNPP - Filter Border Control NPP - -## Description - -This sample demonstrates how any border version of an NPP filtering function can be used in the most common mode, with border control enabled. Mentioned functions can be used to duplicate the results of the equivalent non-border version of the NPP functions. They can be also used for enabling and disabling border control on various source image edges depending on what portion of the source image is being used as input. - -## Key Concepts - -Performance Strategies, Image Processing, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaDeviceReset, cudaSetDevice, cudaGetDeviceCount, cudaDeviceInit, cudaDriverGetVersion, cudaGetDeviceProperties - -## Dependencies needed to build/run -[FreeImage](../../../README.md#freeimage), [NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/data/teapot512.pgm b/cpp/4_CUDA_Libraries/FilterBorderControlNPP/data/teapot512.pgm deleted file mode 100644 index 23db9b9a..00000000 Binary files a/cpp/4_CUDA_Libraries/FilterBorderControlNPP/data/teapot512.pgm and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/extensions.json b/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/CMakeLists.txt b/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/CMakeLists.txt deleted file mode 100644 index e750063e..00000000 --- a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(MersenneTwisterGP11213 LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for MersenneTwisterGP11213 -add_executable(MersenneTwisterGP11213 MersenneTwister.cpp) - -target_compile_options(MersenneTwisterGP11213 PRIVATE $<$:--extended-lambda>) - -target_compile_features(MersenneTwisterGP11213 PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(MersenneTwisterGP11213 PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(MersenneTwisterGP11213 PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(MersenneTwisterGP11213 PRIVATE - CUDA::cudart - CUDA::curand -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/MersenneTwister.cpp b/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/MersenneTwister.cpp deleted file mode 100644 index 1d6af8a2..00000000 --- a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/MersenneTwister.cpp +++ /dev/null @@ -1,179 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample demonstrates the use of CURAND to generate - * random numbers on GPU and CPU. - */ - -// Utilities and system includes -// includes, system -#include -#include -#include -#include - -// Utilities and system includes -#include -#include -#include -#include - -float compareResults(int rand_n, float *h_RandGPU, float *h_RandCPU); - -const int DEFAULT_RAND_N = 2400000; -const unsigned int DEFAULT_SEED = 777; - -/////////////////////////////////////////////////////////////////////////////// -// Main program -/////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - // Start logs - printf("%s Starting...\n\n", argv[0]); - - // initialize the GPU, either identified by --device - // or by picking the device with highest flop rate. - int devID = findCudaDevice(argc, (const char **)argv); - - // parsing the number of random numbers to generate - int rand_n = DEFAULT_RAND_N; - - if (checkCmdLineFlag(argc, (const char **)argv, "count")) { - rand_n = getCmdLineArgumentInt(argc, (const char **)argv, "count"); - } - - printf("Allocating data for %i samples...\n", rand_n); - - // parsing the seed - int seed = DEFAULT_SEED; - - if (checkCmdLineFlag(argc, (const char **)argv, "seed")) { - seed = getCmdLineArgumentInt(argc, (const char **)argv, "seed"); - } - - printf("Seeding with %i ...\n", seed); - - cudaStream_t stream; - checkCudaErrors(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking)); - - float *d_Rand; - checkCudaErrors(cudaMalloc((void **)&d_Rand, rand_n * sizeof(float))); - - curandGenerator_t prngGPU; - checkCudaErrors(curandCreateGenerator(&prngGPU, CURAND_RNG_PSEUDO_MTGP32)); - checkCudaErrors(curandSetStream(prngGPU, stream)); - checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngGPU, seed)); - - curandGenerator_t prngCPU; - checkCudaErrors(curandCreateGeneratorHost(&prngCPU, CURAND_RNG_PSEUDO_MTGP32)); - checkCudaErrors(curandSetPseudoRandomGeneratorSeed(prngCPU, seed)); - - // - // Example 1: Compare random numbers generated on GPU and CPU - float *h_RandGPU; - checkCudaErrors(cudaMallocHost(&h_RandGPU, rand_n * sizeof(float))); - - printf("Generating random numbers on GPU...\n\n"); - checkCudaErrors(curandGenerateUniform(prngGPU, (float *)d_Rand, rand_n)); - - printf("\nReading back the results...\n"); - checkCudaErrors(cudaMemcpyAsync(h_RandGPU, d_Rand, rand_n * sizeof(float), cudaMemcpyDeviceToHost, stream)); - - float *h_RandCPU = (float *)malloc(rand_n * sizeof(float)); - - printf("Generating random numbers on CPU...\n\n"); - checkCudaErrors(curandGenerateUniform(prngCPU, (float *)h_RandCPU, rand_n)); - - checkCudaErrors(cudaStreamSynchronize(stream)); - printf("Comparing CPU/GPU random numbers...\n\n"); - float L1norm = compareResults(rand_n, h_RandGPU, h_RandCPU); - - // - // Example 2: Timing of random number generation on GPU - const int numIterations = 10; - int i; - StopWatchInterface *hTimer; - - sdkCreateTimer(&hTimer); - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - - for (i = 0; i < numIterations; i++) { - checkCudaErrors(curandGenerateUniform(prngGPU, (float *)d_Rand, rand_n)); - } - - checkCudaErrors(cudaStreamSynchronize(stream)); - sdkStopTimer(&hTimer); - - double gpuTime = 1.0e-3 * sdkGetTimerValue(&hTimer) / (double)numIterations; - - printf("MersenneTwisterGP11213, Throughput = %.4f GNumbers/s, Time = %.5f s, " - "Size = %u Numbers\n", - 1.0e-9 * rand_n / gpuTime, - gpuTime, - rand_n); - - printf("Shutting down...\n"); - - checkCudaErrors(curandDestroyGenerator(prngGPU)); - checkCudaErrors(curandDestroyGenerator(prngCPU)); - checkCudaErrors(cudaStreamDestroy(stream)); - checkCudaErrors(cudaFree(d_Rand)); - sdkDeleteTimer(&hTimer); - checkCudaErrors(cudaFreeHost(h_RandGPU)); - free(h_RandCPU); - - exit(L1norm < 1e-6 ? EXIT_SUCCESS : EXIT_FAILURE); -} - -float compareResults(int rand_n, float *h_RandGPU, float *h_RandCPU) -{ - int i; - float rCPU, rGPU, delta; - float max_delta = 0.; - float sum_delta = 0.; - float sum_ref = 0.; - - for (i = 0; i < rand_n; i++) { - rCPU = h_RandCPU[i]; - rGPU = h_RandGPU[i]; - delta = fabs(rCPU - rGPU); - sum_delta += delta; - sum_ref += fabs(rCPU); - - if (delta >= max_delta) { - max_delta = delta; - } - } - - float L1norm = (float)(sum_delta / sum_ref); - printf("Max absolute error: %E\n", max_delta); - printf("L1 norm: %E\n\n", L1norm); - - return L1norm; -} diff --git a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/README.md b/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/README.md deleted file mode 100644 index 58e1d12a..00000000 --- a/cpp/4_CUDA_Libraries/MersenneTwisterGP11213/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# MersenneTwisterGP11213 - MersenneTwisterGP11213 - -## Description - -This sample demonstrates the Mersenne Twister random number generator GP11213 in cuRAND. - -## Key Concepts - -CURAND Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaStreamCreateWithFlags, cudaStreamDestroy, cudaFree, cudaMallocHost, cudaFreeHost, cudaStreamSynchronize, cudaMalloc, cudaMemcpyAsync - -## Dependencies needed to build/run -[CURAND](../../../README.md#curand) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/README.md b/cpp/4_CUDA_Libraries/README.md index e8e23e09..2fa04f17 100644 --- a/cpp/4_CUDA_Libraries/README.md +++ b/cpp/4_CUDA_Libraries/README.md @@ -1,101 +1,24 @@ # 4. CUDA Libraries -### [batchCUBLAS](./batchCUBLAS) -A CUDA Sample that demonstrates how using batched CUBLAS API calls to improve overall performance. - -### [boxFilterNPP](./boxFilterNPP) -A NPP CUDA Sample that demonstrates how to use NPP FilterBox function to perform a Box Filter. - -### [cannyEdgeDetectorNPP](./cannyEdgeDetectorNPP) -An NPP CUDA Sample that demonstrates the recommended parameters to use with the nppiFilterCannyBorder_8u_C1R Canny Edge Detection image filter function. This function expects a single channel 8-bit grayscale input image. You can generate a grayscale image from a color image by first calling nppiColorToGray() or nppiRGBToGray(). The Canny Edge Detection function combines and improves on the techniques required to produce an edge detection image using multiple steps. - -### [conjugateGradient](./conjugateGradient) -This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library. - -### [conjugateGradientCudaGraphs](./conjugateGradientCudaGraphs) +## [conjugateGradientCudaGraphs](./conjugateGradientCudaGraphs) This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library calls captured and called using CUDA Graph APIs. -### [conjugateGradientMultiBlockCG](./conjugateGradientMultiBlockCG) +## [conjugateGradientMultiBlockCG](./conjugateGradientMultiBlockCG) This sample implements a conjugate gradient solver on GPU using Multi Block Cooperative Groups, also uses Unified Memory. -### [conjugateGradientMultiDeviceCG](./conjugateGradientMultiDeviceCG) +## [conjugateGradientMultiDeviceCG](./conjugateGradientMultiDeviceCG) This sample implements a conjugate gradient solver on multiple GPUs using Multi Device Cooperative Groups, also uses Unified Memory optimized using prefetching and usage hints. -### [conjugateGradientPrecond](./conjugateGradientPrecond) -This sample implements a preconditioned conjugate gradient solver on GPU using CUBLAS and CUSPARSE library. - -### [conjugateGradientUM](./conjugateGradientUM) -This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library, using Unified Memory - -### [cudaNvSci](./cudaNvSci) +## [cudaNvSci](./cudaNvSci) This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. Currently only supported on Ubuntu 18.04 -### [cuSolverDn_LinearSolver](./cuSolverDn_LinearSolver) -A CUDA Sample that demonstrates cuSolverDN's LU, QR and Cholesky factorization. - -### [cuSolverRf](./cuSolverRf) -A CUDA Sample that demonstrates cuSolver's refactorization library - CUSOLVERRF. - -### [cuSolverSp_LinearSolver](./cuSolverSp_LinearSolver) -A CUDA Sample that demonstrates cuSolverSP's LU, QR and Cholesky factorization. - -### [cuSolverSp_LowlevelCholesky](./cuSolverSp_LowlevelCholesky) -A CUDA Sample that demonstrates Cholesky factorization using cuSolverSP's low level APIs. - -### [cuSolverSp_LowlevelQR](./cuSolverSp_LowlevelQR) -A CUDA Sample that demonstrates QR factorization using cuSolverSP's low level APIs. - -### [FilterBorderControlNPP](./FilterBorderControlNPP) -This sample demonstrates how any border version of an NPP filtering function can be used in the most common mode, with border control enabled. Mentioned functions can be used to duplicate the results of the equivalent non-border version of the NPP functions. They can be also used for enabling and disabling border control on various source image edges depending on what portion of the source image is being used as input. - -### [freeImageInteropNPP](./freeImageInteropNPP) -A simple CUDA Sample demonstrate how to use FreeImage library with NPP. - -### [histEqualizationNPP](./histEqualizationNPP) -This CUDA Sample demonstrates how to use NPP for histogram equalization for image data. - -### [lineOfSight](./lineOfSight) +## [lineOfSight](./lineOfSight) This sample is an implementation of a simple line-of-sight algorithm: Given a height map and a ray originating at some observation point, it computes all the points along the ray that are visible from the observation point. The implementation is based on the Thrust library. -### [matrixMulCUBLAS](./matrixMulCUBLAS) -This sample implements matrix multiplication from Chapter 3 of the programming guide. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication. - -### [MersenneTwisterGP11213](./MersenneTwisterGP11213) -This sample demonstrates the Mersenne Twister random number generator GP11213 in cuRAND. - -### [nvJPEG](./nvJPEG) -A CUDA Sample that demonstrates single and batched decoding of jpeg images using NVJPEG Library. - -### [nvJPEG_encoder](./nvJPEG_encoder) -A CUDA Sample that demonstrates single encoding of jpeg images using NVJPEG Library. - -### [oceanFFT](./oceanFFT) +## [oceanFFT](./oceanFFT) This sample simulates an Ocean height field using CUFFT Library and renders the result using OpenGL. -### [randomFog](./randomFog) +## [randomFog](./randomFog) This sample illustrates pseudo- and quasi- random numbers produced by CURAND. -### [simpleCUBLAS](./simpleCUBLAS) -Example of using CUBLAS API interface to perform GEMM operations. - -### [simpleCUBLAS_LU](./simpleCUBLAS_LU) -CUDA sample demonstrating cuBLAS API cublasDgetrfBatched() for lower-upper (LU) decomposition of a matrix. - -### [simpleCUBLASXT](./simpleCUBLASXT) -Example of using CUBLAS-XT library which performs GEMM operations over Multiple GPUs. - -### [simpleCUFFT](./simpleCUFFT) -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain. cuFFT plans are created using simple and advanced API functions. - -### [simpleCUFFT_2d_MGPU](./simpleCUFFT_2d_MGPU) -Example of using CUFFT. In this example, CUFFT is used to compute the 2D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU. - -### [simpleCUFFT_callback](./simpleCUFFT_callback) -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain. The difference between this example and the Simple CUFFT example is that the multiplication step is done by the CUFFT kernel with a user-supplied CUFFT callback routine, rather than by a separate kernel call. - -### [simpleCUFFT_MGPU](./simpleCUFFT_MGPU) -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU. - -### [watershedSegmentationNPP](./watershedSegmentationNPP) -An NPP CUDA Sample that demonstrates how to use the NPP watershed segmentation function. diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/extensions.json b/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/CMakeLists.txt b/cpp/4_CUDA_Libraries/batchCUBLAS/CMakeLists.txt deleted file mode 100644 index 49b693e9..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(batchCUBLAS LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for batchCUBLAS -add_executable(batchCUBLAS batchCUBLAS.cpp) - -target_compile_options(batchCUBLAS PRIVATE $<$:--extended-lambda>) - -target_compile_features(batchCUBLAS PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(batchCUBLAS PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(batchCUBLAS PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(batchCUBLAS PRIVATE - CUDA::cublas - CUDA::cudart -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/README.md b/cpp/4_CUDA_Libraries/batchCUBLAS/README.md deleted file mode 100644 index 19dec59e..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# batchCUBLAS - batchCUBLAS - -## Description - -A CUDA Sample that demonstrates how using batched CUBLAS API calls to improve overall performance. - -## Key Concepts - -Linear Algebra, CUBLAS Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuRand, cuEqual - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaGetErrorString, cudaFree, cudaGetLastError, cudaDeviceSynchronize, cudaGetDevice, cudaMalloc, cudaStreamCreate, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.cpp b/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.cpp deleted file mode 100644 index a8a2c1c2..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.cpp +++ /dev/null @@ -1,727 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This example demonstrates how to get better performance by - * batching CUBLAS calls with the use of using streams - */ - -#include -#include -#include -#include -#include -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#include -#endif - -/* Using updated (v2) interfaces to cublas and cusparse */ -#include -#include - -// Utilities and system includes -#include - -#include "batchCUBLAS.h" - -const char *sSDKname = "batchCUBLAS"; - -//============================================================================== -// Device information utilities -//============================================================================== - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - - int getDeviceVersion(void) - { - int device; - struct cudaDeviceProp properties; - - if (cudaGetDevice(&device) != cudaSuccess) { - printf("failed to get device\n"); - return 0; - } - - if (cudaGetDeviceProperties(&properties, device) != cudaSuccess) { - printf("failed to get properties\n"); - return 0; - } - - return properties.major * 100 + properties.minor * 10; - } - - size_t getDeviceMemory(void) - { - struct cudaDeviceProp properties; - int device; - - if (cudaGetDevice(&device) != cudaSuccess) { - return 0; - } - - if (cudaGetDeviceProperties(&properties, device) != cudaSuccess) { - return 0; - } - - return properties.totalGlobalMem; - } -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -//============================================================================== -// random utilities -//============================================================================== - -template void fillupMatrix(T_ELEM *A, int lda, int rows, int cols, int seed = 0); - -template void fillupMatrix(T_ELEM *A, int lda, int rows, int cols, int seed) -{ - for (int j = 0; j < cols; j++) { - for (int i = 0; i < rows; i++) { - A[i + lda * j] = cuGet(((double)(((lda * i + j + seed) % 253) + 1)) / 256.0, - ((double)((((cols * i + j) + 123 + seed) % 253) + 1)) / 256.0); - } - } -} -/* Explicit instantiation */ -template void fillupMatrix(float *A, int lda, int rows, int cols, int seed); -template void fillupMatrix(double *A, int lda, int rows, int cols, int seed); - -/* For debugging */ -void printCuType(const char *str, float A) { fprintf(stdout, "%s (0x%08x, %g)", str, floatAsUInt(A), A); } - -void printCuType(const char *str, double A) { fprintf(stdout, "%s (0x%016llx, %g)", str, doubleAsULL(A), A); } - -//============================================================================== -// defines and structures -//============================================================================== - -#define CUBLAS_SGEMM_MAX_ULP_ERR (.3) -#define CUBLAS_DGEMM_MAX_ULP_ERR (1.e-3) -#define CUBLAS_SGEMM_MAX_RELATIVE_ERR (6.e-6) -#define CUBLAS_DGEMM_MAX_RELATIVE_ERR (0.0) -#define CUBLAS_GEMM_TEST_COUNT (30) -#define BENCH_MATRIX_M (128) -#define BENCH_MATRIX_K (128) -#define BENCH_MATRIX_N (128) - -#define CLEANUP() \ - do { \ - if (A) \ - free(A); \ - if (B) \ - free(B); \ - if (C) \ - free(C); \ - for (int i = 0; i < opts.N; ++i) { \ - if (devPtrA[i]) \ - cudaFree(devPtrA[i]); \ - if (devPtrB[i]) \ - cudaFree(devPtrB[i]); \ - if (devPtrC[i]) \ - cudaFree(devPtrC[i]); \ - } \ - if (devPtrA) \ - free(devPtrA); \ - if (devPtrB) \ - free(devPtrB); \ - if (devPtrC) \ - free(devPtrC); \ - if (devPtrA_dev) \ - cudaFree(devPtrA_dev); \ - if (devPtrB_dev) \ - cudaFree(devPtrB_dev); \ - if (devPtrC_dev) \ - cudaFree(devPtrC_dev); \ - fflush(stdout); \ - } while (0) - -enum testMethod { tmRegular, tmStream, tmBatched }; - -struct gemmOpts -{ - int m; - int n; - int k; - testMethod test_method; - char *elem_type; - int N; // number of multiplications -}; - -template struct gemmTestParams -{ - cublasOperation_t transa; - cublasOperation_t transb; - int m; - int n; - int k; - T_ELEM alpha; - T_ELEM beta; -}; - -//============================================================================== -// template wrappers for cuda functions -//============================================================================== - -static inline cublasStatus_t cublasXgemm(cublasHandle_t handle, - cublasOperation_t transa, - cublasOperation_t transb, - int m, - int n, - int k, - float *alpha, - const float *A, - int lda, - float *B, - int ldb, - float *beta, - float *C, - int ldc) -{ - return cublasSgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc); -} - -static inline cublasStatus_t cublasXgemm(cublasHandle_t handle, - cublasOperation_t transa, - cublasOperation_t transb, - int m, - int n, - int k, - double *alpha, - const double *A, - int lda, - double *B, - int ldb, - double *beta, - double *C, - int ldc) -{ - return cublasDgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc); -} - -static inline cublasStatus_t cublasXgemmBatched(cublasHandle_t handle, - cublasOperation_t transa, - cublasOperation_t transb, - int m, - int n, - int k, - float *alpha, - const float *Aarray[], - int lda, - const float *Barray[], - int ldb, - float *beta, - float *Carray[], - int ldc, - int batchCount) -{ -#if CUDART_VERSION >= 4010 - return cublasSgemmBatched( - handle, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount); -#else - return CUBLAS_STATUS_SUCCESS; -#endif -} - -static inline cublasStatus_t cublasXgemmBatched(cublasHandle_t handle, - cublasOperation_t transa, - cublasOperation_t transb, - int m, - int n, - int k, - double *alpha, - const double *Aarray[], - int lda, - const double *Barray[], - int ldb, - double *beta, - double *Carray[], - int ldc, - int batchCount) -{ -#if CUDART_VERSION >= 4010 - return cublasDgemmBatched( - handle, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount); -#else - return CUBLAS_STATUS_SUCCESS; -#endif -} - -//============================================================================== -// Primary Application code -//============================================================================== - -static int processArgs(int argc, char *argv[], struct gemmOpts *opts) -{ - int error = 0; - int oldError; - memset(opts, 0, sizeof(*opts)); - static char default_type[] = "d"; // default double - opts->elem_type = default_type; - opts->N = 10; - - while (argc) { - oldError = error; - - if (*argv[0] == SWITCH_CHAR) { - switch (*(argv[0] + 1)) { - case 'm': - opts->m = (int)atol(argv[0] + 2); - break; - - case 'n': - opts->n = (int)atol(argv[0] + 2); - break; - - case 'k': - opts->k = (int)atol(argv[0] + 2); - break; - - case 'N': - opts->N = (int)atol(argv[0] + 2); - break; - - default: - break; - } - } - - if (error > oldError) { - fprintf(stderr, "Invalid switch '%c%s'\n", SWITCH_CHAR, argv[0] + 1); - } - - argc -= 1; - argv++; - } - - return error; -} - -template -static int TESTGEN(gemm)(const struct gemmOpts *opts, - int matrixM, - int matrixN, - int matrixK, - int &numTests, - struct gemmTestParams *params) -{ - static T_ELEM alpha[] = { - cuGet(0, 0), cuGet(-1, -1), cuGet(1, -2), cuGet(2, -1), cuGet(0, -3)}; - static T_ELEM beta[] = { - cuGet(0, 0), cuGet(-1, -1), cuGet(1, -2), cuGet(2, -1), cuGet(0, -3)}; - -#define NBR_ALPHAS (sizeof(alpha) / sizeof(alpha[0])) -#define NBR_BETAS (sizeof(beta) / sizeof(beta[0])) - static T_ELEM theAlpha; - static T_ELEM theBeta; - static int state; - static int m; - static int n; - static int k; - - if (numTests-- <= 0) { - return -1; - } - - theAlpha = alpha[cuRand() % NBR_ALPHAS]; - theBeta = beta[cuRand() % NBR_BETAS]; - params->transa = CUBLAS_OP_N; - params->transb = CUBLAS_OP_N; - m = matrixM; - n = matrixN; - k = matrixK; - params->m = m; - params->n = n; - params->k = k; - params->alpha = theAlpha; - params->beta = theBeta; - - printf("#### args: ta=%d tb=%d m=%d n=%d k=%d ", - (unsigned int)params->transa, - (unsigned int)params->transb, - params->m, - params->n, - params->k); - printCuType(" alpha =", params->alpha); - printCuType(" beta=", params->beta); - printf("\n"); - - m = cuRand() % matrixM; - n = cuRand() % matrixN; - k = cuRand() % matrixK; - - state = cuRand() % 9; - return 0; -} - -template void fillupMatrixDebug(T_ELEM *A, int lda, int rows, int cols) -{ - for (int j = 0; j < cols; j++) { - for (int i = 0; i < rows; i++) { - A[i + lda * j] = cuGet(i + j); - } - } -} - -template -int test_gemm_loop(struct gemmOpts &opts, float err, double max_relative_error, cublasHandle_t handle) -{ - struct gemmTestParams params; - cudaStream_t *streamArray = 0; - cublasStatus_t status1, status2, status3; - T_ELEM *A = NULL; - T_ELEM *B = NULL; - T_ELEM *C = NULL; - T_ELEM **devPtrA = 0; - T_ELEM **devPtrB = 0; - T_ELEM **devPtrC = 0; - T_ELEM **devPtrA_dev = NULL; - T_ELEM **devPtrB_dev = NULL; - T_ELEM **devPtrC_dev = NULL; - int matrixM, matrixN, matrixK; - int rowsA, rowsB, rowsC; - int colsA, colsB, colsC; - int matrixSizeA, matrixSizeB, matrixSizeC; - int errors; - double start, stop; - - printf("Testing %cgemm\n", *opts.elem_type); - - matrixM = (opts.m) ? opts.m : BENCH_MATRIX_M; - matrixN = (opts.n) ? opts.n : BENCH_MATRIX_N; - matrixK = (opts.k) ? opts.k : BENCH_MATRIX_K; - - rowsA = imax(1, matrixM); - colsA = imax(1, matrixK); - rowsB = imax(1, matrixK); - colsB = imax(1, matrixN); - rowsC = imax(1, matrixM); - colsC = imax(1, matrixN); - - matrixSizeA = rowsA * colsA; - matrixSizeB = rowsB * colsB; - matrixSizeC = rowsC * colsC; - - devPtrA = (T_ELEM **)malloc(opts.N * sizeof(*devPtrA)); - devPtrB = (T_ELEM **)malloc(opts.N * sizeof(*devPtrB)); - devPtrC = (T_ELEM **)malloc(opts.N * sizeof(*devPtrC)); - - for (int i = 0; i < opts.N; i++) { - cudaError_t err1 = cudaMalloc((void **)&devPtrA[i], matrixSizeA * sizeof(devPtrA[0][0])); - cudaError_t err2 = cudaMalloc((void **)&devPtrB[i], matrixSizeB * sizeof(devPtrB[0][0])); - cudaError_t err3 = cudaMalloc((void **)&devPtrC[i], matrixSizeC * sizeof(devPtrC[0][0])); - - if ((err1 != cudaSuccess) || (err2 != cudaSuccess) || (err3 != cudaSuccess)) { - CLEANUP(); - fprintf(stderr, "!!!! GPU memory allocation error\n"); - return CUBLASTEST_FAILED; - } - } - - // For batched processing we need those arrays on the device - if (opts.test_method == tmBatched) { - cudaError_t err1 = cudaMalloc((void **)&devPtrA_dev, opts.N * sizeof(*devPtrA)); - cudaError_t err2 = cudaMalloc((void **)&devPtrB_dev, opts.N * sizeof(*devPtrB)); - cudaError_t err3 = cudaMalloc((void **)&devPtrC_dev, opts.N * sizeof(*devPtrC)); - - if ((err1 != cudaSuccess) || (err2 != cudaSuccess) || (err3 != cudaSuccess)) { - CLEANUP(); - fprintf(stderr, "!!!! GPU memory allocation error\n"); - return CUBLASTEST_FAILED; - } - - err1 = cudaMemcpy(devPtrA_dev, devPtrA, opts.N * sizeof(*devPtrA), cudaMemcpyHostToDevice); - err2 = cudaMemcpy(devPtrB_dev, devPtrB, opts.N * sizeof(*devPtrB), cudaMemcpyHostToDevice); - err3 = cudaMemcpy(devPtrC_dev, devPtrC, opts.N * sizeof(*devPtrC), cudaMemcpyHostToDevice); - - if ((err1 != cudaSuccess) || (err2 != cudaSuccess) || (err3 != cudaSuccess)) { - CLEANUP(); - fprintf(stderr, "!!!! cannot copy pointer array to device\n"); - return CUBLASTEST_FAILED; - } - } - - A = (T_ELEM *)malloc(matrixSizeA * sizeof(A[0])); - B = (T_ELEM *)malloc(matrixSizeB * sizeof(B[0])); - C = (T_ELEM *)malloc(matrixSizeC * sizeof(C[0])); - - if ((!A) || (!B) || (!C)) { - CLEANUP(); - fprintf(stderr, "!!!! system memory allocation error\n"); - return CUBLASTEST_FAILED; - } - - streamArray = (cudaStream_t *)malloc(opts.N * sizeof(cudaStream_t *)); - - for (int i = 0; i < opts.N; i++) { - if (opts.test_method == tmStream) { - cudaError_t cudaErr = cudaStreamCreate(&streamArray[i]); - - if (cudaErr != cudaSuccess) { - CLEANUP(); - fprintf(stderr, "!!!! cannot create stream\n"); - return CUBLASTEST_FAILED; - } - } - else { - streamArray[i] = 0; - } - } - - errors = 0; - int numTests = 1; - - while (TESTGEN(gemm)(&opts, matrixM, matrixN, matrixK, numTests, ¶ms) == 0) { - printf("#### args: lda=%d ldb=%d ldc=%d\n", rowsA, rowsB, rowsC); - - // fillup with Nan first (so lda padding is full on Nan) - memset(A, 0xFF, matrixSizeA * sizeof(A[0])); - fillupMatrixDebug(A, rowsA, params.m, params.k); - memset(B, 0xFF, matrixSizeB * sizeof(B[0])); - fillupMatrix(B, rowsB, params.k, params.n, 121); - - if (!cuEqual(params.beta, cuGet(0))) { - fillupMatrix(C, rowsC, params.m, params.n); - } - else { - /* fill with SNaNs to make sure ZGEMM doesn't access C */ - memset(C, 0xFF, matrixSizeC * sizeof(C[0])); - } - - double flopsCoef = 2.0; - - for (int i = 0; i < opts.N; i++) { - status1 = cublasSetMatrix(rowsA, colsA, sizeof(A[0]), A, rowsA, devPtrA[i], rowsA); - status2 = cublasSetMatrix(rowsB, colsB, sizeof(B[0]), B, rowsB, devPtrB[i], rowsB); - status3 = cublasSetMatrix(rowsC, colsC, sizeof(C[0]), C, rowsC, devPtrC[i], rowsC); - - if ((status1 != CUBLAS_STATUS_SUCCESS) || (status2 != status1) || (status3 != status1)) { - CLEANUP(); - fprintf(stderr, "!!!! GPU access error (write)\n"); - return CUBLASTEST_FAILED; - } - } - - start = second(); - - if (opts.test_method == tmBatched) { - cublasSetStream(handle, streamArray[0]); - status1 = cublasXgemmBatched(handle, - params.transa, - params.transb, - params.m, - params.n, - params.k, - ¶ms.alpha, - (const T_ELEM **)devPtrA_dev, - rowsA, - (const T_ELEM **)devPtrB_dev, - rowsB, - ¶ms.beta, - devPtrC_dev, - rowsC, - opts.N); - - if (status1 != CUBLAS_STATUS_SUCCESS) { - cudaError_t cudaStatus = cudaGetLastError(); - CLEANUP(); - fprintf(stderr, - "!!!! GPU program execution error : cublas Error=%d, cuda " - "Error=%d,(%s)\n", - status1, - cudaStatus, - cudaGetErrorString(cudaStatus)); - return CUBLASTEST_FAILED; - } - } - else { - for (int i = 0; i < opts.N; i++) { - cublasSetStream(handle, streamArray[i]); - status1 = cublasXgemm(handle, - params.transa, - params.transb, - params.m, - params.n, - params.k, - ¶ms.alpha, - devPtrA[i], - rowsA, - devPtrB[i], - rowsB, - ¶ms.beta, - devPtrC[i], - rowsC); - - if (status1 != CUBLAS_STATUS_SUCCESS) { - cudaError_t cudaStatus = cudaGetLastError(); - CLEANUP(); - fprintf(stderr, - "!!!! GPU program execution error : cublas Error=%d, cuda " - "Error=%d,(%s)\n", - status1, - cudaStatus, - cudaGetErrorString(cudaStatus)); - return CUBLASTEST_FAILED; - } - } - } - - cudaError_t cudaStatus = cudaDeviceSynchronize(); - - if (cudaStatus != cudaSuccess) { - CLEANUP(); - fprintf(stderr, - "!!!! GPU program execution error on cudaDeviceSynchronize : " - "cudaError=%d,(%s)\n", - cudaStatus, - cudaGetErrorString(cudaStatus)); - return CUBLASTEST_FAILED; - } - - stop = second(); - - fprintf(stdout, - "^^^^ elapsed = %10.8f sec GFLOPS=%g\n", - (stop - start), - opts.N * (1e-9 * flopsCoef * params.m * params.n * params.k) / (stop - start)); - - } // end while (TESTGEN.. - - CLEANUP(); - fprintf(stdout, "@@@@ %cgemm test %s\n", *opts.elem_type, errors ? "FAIL" : "OK"); - return CUBLASTEST_PASSED; -} - -int main(int argc, char *argv[]) -{ - struct gemmOpts opts; - int errors, nTimes, nTotalErrors = 0; - int status = CUBLASTEST_PASSED; - - printf("%s Starting...\n\n", sSDKname); - - int dev = findCudaDevice(argc, (const char **)argv); - - if (dev == -1) { - return CUBLASTEST_FAILED; - } - - errors = processArgs(argc, argv, &opts); - - if (errors) { - fprintf(stdout, - "\n Usage: batchcublas [-mSIZE_M] [-nSIZE_N] [-kSIZE_N] " - "[-NSIZE_NUM_ITERATIONS] [-qatest] [-noprompt]\n"); - return CUBLASTEST_FAILED; - } - - cublasHandle_t handle; - - if (cublasCreate(&handle) != CUBLAS_STATUS_SUCCESS) { - fprintf(stdout, "CUBLAS initialization failed!\n"); - exit(EXIT_FAILURE); - } - - // Run single kernels - fprintf(stdout, "\n ==== Running single kernels ==== \n\n"); - nTimes = opts.N; - opts.N = 1; - *(opts.elem_type) = 's'; - status = - test_gemm_loop(opts, (float)CUBLAS_SGEMM_MAX_ULP_ERR, (double)CUBLAS_SGEMM_MAX_RELATIVE_ERR, handle); - - // Run Double version - *(opts.elem_type) = 'd'; - - if (getDeviceVersion() < DEV_VER_DBL_SUPPORT) { - fprintf(stdout, "@@@@ dgemm test WAIVED due to lack of DP support\n"); - exit(EXIT_WAIVED); - } - - status = - test_gemm_loop(opts, (float)CUBLAS_DGEMM_MAX_ULP_ERR, (double)CUBLAS_DGEMM_MAX_RELATIVE_ERR, handle); - nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1); - opts.N = nTimes; - - // Run with and without streams and then batched. The batched functions are a - // feature new feature in 4.1 -#if CUDART_VERSION >= 4010 - - for (int ii = 0; ii < 3; ii++) { -#else - - for (int ii = 0; ii < 2; ii++) { -#endif - - switch (ii) { - case 0: - opts.test_method = tmRegular; - fprintf(stdout, "\n ==== Running N=%d without streams ==== \n\n", opts.N); - break; - - case 1: - opts.test_method = tmStream; - fprintf(stdout, "\n ==== Running N=%d with streams ==== \n\n", opts.N); - break; - - case 2: - opts.test_method = tmBatched; - fprintf(stdout, "\n ==== Running N=%d batched ==== \n\n", opts.N); - break; - } - - // Run single version - *(opts.elem_type) = 's'; - status = - test_gemm_loop(opts, (float)CUBLAS_SGEMM_MAX_ULP_ERR, (double)CUBLAS_SGEMM_MAX_RELATIVE_ERR, handle); - nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1); - - // Run Double version - *(opts.elem_type) = 'd'; - - // Test doesn't meet minSpec, will will wave the DP test - if (getDeviceVersion() < DEV_VER_DBL_SUPPORT) { - fprintf(stdout, "@@@@ dgemm test WAIVED due to lack of DP support\n"); - exit(EXIT_WAIVED); - } - else { - status = test_gemm_loop( - opts, (float)CUBLAS_DGEMM_MAX_ULP_ERR, (double)CUBLAS_DGEMM_MAX_RELATIVE_ERR, handle); - nTotalErrors += (status == CUBLASTEST_PASSED ? 0 : 1); - } - } - - cublasDestroy(handle); - - printf("\nTest Summary\n"); - printf("%d error(s)\n", nTotalErrors); - exit(nTotalErrors == 0 ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.h b/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.h deleted file mode 100644 index 5bc88780..00000000 --- a/cpp/4_CUDA_Libraries/batchCUBLAS/batchCUBLAS.h +++ /dev/null @@ -1,179 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This example demonstrates how to get better performance by - * batching CUBLAS calls with the use of using streams - */ - -#include -#include -#include -#include - -#define SWITCH_CHAR '-' - -#define REFFUNC(funcname) ref_##funcname -#define TESTGEN(funcname) get_##funcname##_params -#define TESTPARAMS(funcname) funcname##TestParams - -#define DEV_VER_DBL_SUPPORT (130) -#define DEV_VER_ALL_SUPPORT (999) - -/* Errors Tests to be returned by all the Cublas test */ -#define CUBLASTEST_PASSED 0 -#define CUBLASTEST_FAILED 1 -#define CUBLASTEST_WAIVED 2 - -//============================================================================== -// Device information utilities -//============================================================================== - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - int getDeviceVersion(void); - size_t getDeviceMemory(void); -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -//============================================================================== -// Math utilities -//============================================================================== - -static __inline__ int imax(int x, int y) { return (x > y) ? x : y; } - -static __inline__ unsigned floatAsUInt(float x) -{ - volatile union - { - float f; - unsigned i; - } xx; - xx.f = x; - return xx.i; -} - -static __inline__ unsigned long long doubleAsULL(double x) -{ - volatile union - { - double f; - unsigned long long i; - } xx; - xx.f = x; - return xx.i; -} - -static __inline__ unsigned cuRand(void) -{ - /* George Marsaglia's fast inline random number generator */ -#define CUDA_ZNEW (cuda_z = 36969 * (cuda_z & 65535) + (cuda_z >> 16)) -#define CUDA_WNEW (cuda_w = 18000 * (cuda_w & 65535) + (cuda_w >> 16)) -#define CUDA_MWC ((CUDA_ZNEW << 16) + CUDA_WNEW) -#define CUDA_SHR3 \ - (cuda_jsr = cuda_jsr ^ (cuda_jsr << 17), \ - cuda_jsr = cuda_jsr ^ (cuda_jsr >> 13), \ - cuda_jsr = cuda_jsr ^ (cuda_jsr << 5)) -#define CUDA_CONG (cuda_jcong = 69069 * cuda_jcong + 1234567) -#define KISS ((CUDA_MWC ^ CUDA_CONG) + CUDA_SHR3) - static unsigned int cuda_z = 362436069, cuda_w = 521288629; - static unsigned int cuda_jsr = 123456789, cuda_jcong = 380116160; - return KISS; -} - -//============================================================================== -// cuGet and cuEqual versions -//============================================================================== - -template __inline__ __device__ __host__ T_ELEM cuGet(double); -template <> __inline__ __device__ __host__ float cuGet(double x) { return float(x); } -template <> __inline__ __device__ __host__ double cuGet(double x) { return double(x); } - -template __inline__ __device__ __host__ T_ELEM cuGet(double, double); -template <> __inline__ __device__ __host__ float cuGet(double x, double y) { return float(x); } -template <> __inline__ __device__ __host__ double cuGet(double x, double y) { return double(x); } -static __inline__ __device__ __host__ bool cuEqual(float x, float y) { return (x == y); } -static __inline__ __device__ __host__ bool cuEqual(double x, double y) { return (x == y); } - -//============================================================================== -// Platform dependent timing utility -//============================================================================== - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#if !defined(WIN32_LEAN_AND_MEAN) -#define WIN32_LEAN_AND_MEAN -#endif -#include -static __inline__ double second(void) -{ - LARGE_INTEGER t; - static double oofreq; - static int checkedForHighResTimer; - static BOOL hasHighResTimer; - - if (!checkedForHighResTimer) { - hasHighResTimer = QueryPerformanceFrequency(&t); - oofreq = 1.0 / (double)t.QuadPart; - checkedForHighResTimer = 1; - } - - if (hasHighResTimer) { - QueryPerformanceCounter(&t); - return (double)t.QuadPart * oofreq; - } - else { - return (double)GetTickCount() / 1000.0; - } -} -#elif defined(__linux__) || defined(__QNX__) -#include -#include -#include -static double second(void) -{ - struct timeval tv; - gettimeofday(&tv, NULL); - return (double)tv.tv_sec + (double)tv.tv_usec / 1000000.0; -} -#elif defined(__APPLE__) -#include -#include -#include -#include -#include -static double second(void) -{ - struct timeval tv; - gettimeofday(&tv, NULL); - return (double)tv.tv_sec + (double)tv.tv_usec / 1000000.0; -} -#else -#error unsupported platform -#endif diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/boxFilterNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/boxFilterNPP/CMakeLists.txt deleted file mode 100644 index 0ac3682c..00000000 --- a/cpp/4_CUDA_Libraries/boxFilterNPP/CMakeLists.txt +++ /dev/null @@ -1,71 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(boxFilterNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories( - ../../../Common - ../../../Common/UtilNPP -) - -# Source file -find_package(FreeImage) - -if(${FreeImage_FOUND}) - # Add target for boxFilterNPP - add_executable(boxFilterNPP boxFilterNPP.cpp) - -target_compile_options(boxFilterNPP PRIVATE $<$:--extended-lambda>) - -target_compile_features(boxFilterNPP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(boxFilterNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(boxFilterNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} - ${FreeImage_INCLUDE_DIRS} - ) - - target_link_libraries(boxFilterNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppif - CUDA::cudart - ${FreeImage_LIBRARIES} - ) - - # Copy data files to output directory - add_custom_command(TARGET boxFilterNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/teapot512.pgm - ${CMAKE_CURRENT_BINARY_DIR} - ) - if(WIN32) - add_custom_command(TARGET boxFilterNPP - POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy - ${FreeImage_LIBRARY}/../FreeImage.dll - ${CMAKE_CURRENT_BINARY_DIR}/$ - ) - endif() -else() - message(STATUS "FreeImage not found - will not build sample 'boxFilterNPP'") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/README.md b/cpp/4_CUDA_Libraries/boxFilterNPP/README.md deleted file mode 100644 index 23a99cee..00000000 --- a/cpp/4_CUDA_Libraries/boxFilterNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# boxFilterNPP - Box Filter with NPP - -## Description - -A NPP CUDA Sample that demonstrates how to use NPP FilterBox function to perform a Box Filter. - -## Key Concepts - -Performance Strategies, Image Processing, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaDriverGetVersion - -## Dependencies needed to build/run -[FreeImage](../../../README.md#freeimage), [NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/boxFilterNPP.cpp b/cpp/4_CUDA_Libraries/boxFilterNPP/boxFilterNPP.cpp deleted file mode 100644 index 8ced11db..00000000 --- a/cpp/4_CUDA_Libraries/boxFilterNPP/boxFilterNPP.cpp +++ /dev/null @@ -1,213 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#pragma warning(disable : 4819) -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - - -int main(int argc, char *argv[]) -{ - printf("%s Starting...\n\n", argv[0]); - - try { - std::string sFilename; - char *filePath; - NppStreamContext nppStreamCtx; - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError_t cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - if (checkCmdLineFlag(argc, (const char **)argv, "input")) { - getCmdLineArgumentString(argc, (const char **)argv, "input", &filePath); - } - else { - filePath = sdkFindFilePath("teapot512.pgm", argv[0]); - } - - if (filePath) { - sFilename = filePath; - } - else { - sFilename = "teapot512.pgm"; - } - - // if we specify the filename at the command line, then we only test - // sFilename[0]. - int file_errors = 0; - std::ifstream infile(sFilename.data(), std::ifstream::in); - - if (infile.good()) { - std::cout << "boxFilterNPP opened: <" << sFilename.data() << "> successfully!" << std::endl; - file_errors = 0; - infile.close(); - } - else { - std::cout << "boxFilterNPP unable to open: <" << sFilename.data() << ">" << std::endl; - file_errors++; - infile.close(); - } - - if (file_errors > 0) { - exit(EXIT_FAILURE); - } - - std::string sResultFilename = sFilename; - - std::string::size_type dot = sResultFilename.rfind('.'); - - if (dot != std::string::npos) { - sResultFilename = sResultFilename.substr(0, dot); - } - - sResultFilename += "_boxFilter.pgm"; - - if (checkCmdLineFlag(argc, (const char **)argv, "output")) { - char *outputFilePath; - getCmdLineArgumentString(argc, (const char **)argv, "output", &outputFilePath); - sResultFilename = outputFilePath; - } - - // declare a host image object for an 8-bit grayscale image - npp::ImageCPU_8u_C1 oHostSrc; - // load gray-scale image from disk - npp::loadImage(sFilename, oHostSrc); - // declare a device image and copy construct from the host image, - // i.e. upload host to device - npp::ImageNPP_8u_C1 oDeviceSrc(oHostSrc); - - // create struct with box-filter mask size - NppiSize oMaskSize = {5, 5}; - - NppiSize oSrcSize = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - NppiPoint oSrcOffset = {0, 0}; - - // create struct with ROI size - NppiSize oSizeROI = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - // allocate device image of appropriately reduced size - npp::ImageNPP_8u_C1 oDeviceDst(oSizeROI.width, oSizeROI.height); - // set anchor point inside the mask to (oMaskSize.width / 2, - // oMaskSize.height / 2) It should round down when odd - NppiPoint oAnchor = {oMaskSize.width / 2, oMaskSize.height / 2}; - - // run box filter - NPP_CHECK_NPP(nppiFilterBoxBorder_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oSrcSize, - oSrcOffset, - oDeviceDst.data(), - oDeviceDst.pitch(), - oSizeROI, - oMaskSize, - oAnchor, - NPP_BORDER_REPLICATE, - nppStreamCtx)); - - // declare a host image for the result - npp::ImageCPU_8u_C1 oHostDst(oDeviceDst.size()); - // and copy the device result data into it - oDeviceDst.copyTo(oHostDst.data(), oHostDst.pitch()); - - saveImage(sResultFilename, oHostDst); - std::cout << "Saved image: " << sResultFilename << std::endl; - - nppiFree(oDeviceSrc.data()); - nppiFree(oDeviceDst.data()); - - exit(EXIT_SUCCESS); - } - catch (npp::Exception &rException) { - std::cerr << "Program error! The following exception occurred: \n"; - std::cerr << rException << std::endl; - std::cerr << "Aborting." << std::endl; - - exit(EXIT_FAILURE); - } - catch (...) { - std::cerr << "Program error! An unknow type of exception occurred. \n"; - std::cerr << "Aborting." << std::endl; - - exit(EXIT_FAILURE); - return -1; - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/boxFilterNPP/teapot512.pgm b/cpp/4_CUDA_Libraries/boxFilterNPP/teapot512.pgm deleted file mode 100644 index 23db9b9a..00000000 Binary files a/cpp/4_CUDA_Libraries/boxFilterNPP/teapot512.pgm and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/CMakeLists.txt deleted file mode 100644 index 5cfffd7b..00000000 --- a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/CMakeLists.txt +++ /dev/null @@ -1,71 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cannyEdgeDetectorNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories( - ../../../Common - ../../../Common/UtilNPP -) - -# Source file -find_package(FreeImage) - -if(${FreeImage_FOUND}) - # Add target for cannyEdgeDetectorNPP - add_executable(cannyEdgeDetectorNPP cannyEdgeDetectorNPP.cpp) - -target_compile_options(cannyEdgeDetectorNPP PRIVATE $<$:--extended-lambda>) - -target_compile_features(cannyEdgeDetectorNPP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cannyEdgeDetectorNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(cannyEdgeDetectorNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} - ${FreeImage_INCLUDE_DIRS} - ) - - target_link_libraries(cannyEdgeDetectorNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppif - CUDA::cudart - ${FreeImage_LIBRARIES} - ) - - # Copy data files to output directory - add_custom_command(TARGET cannyEdgeDetectorNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/teapot512.pgm - ${CMAKE_CURRENT_BINARY_DIR} - ) - if(WIN32) - add_custom_command(TARGET cannyEdgeDetectorNPP - POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy - ${FreeImage_LIBRARY}/../FreeImage.dll - ${CMAKE_CURRENT_BINARY_DIR}/$ - ) - endif() -else() - message(STATUS "FreeImage not found - will not build sample 'cannyEdgeDetectorNPP'") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/README.md b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/README.md deleted file mode 100644 index b78ed33a..00000000 --- a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# cannyEdgeDetectorNPP - Canny Edge Detector NPP - -## Description - -An NPP CUDA Sample that demonstrates the recommended parameters to use with the nppiFilterCannyBorder_8u_C1R Canny Edge Detection image filter function. This function expects a single channel 8-bit grayscale input image. You can generate a grayscale image from a color image by first calling nppiColorToGray() or nppiRGBToGray(). The Canny Edge Detection function combines and improves on the techniques required to produce an edge detection image using multiple steps. - -## Key Concepts - -Performance Strategies, Image Processing, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaFree, cudaSetDevice, cudaGetDeviceCount, cudaDeviceInit, cudaDriverGetVersion, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[FreeImage](../../../README.md#freeimage), [NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/cannyEdgeDetectorNPP.cpp b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/cannyEdgeDetectorNPP.cpp deleted file mode 100644 index 174f4176..00000000 --- a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/cannyEdgeDetectorNPP.cpp +++ /dev/null @@ -1,260 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#pragma warning(disable : 4819) -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -inline int cudaDeviceInit(int argc, const char **argv) -{ - int deviceCount; - checkCudaErrors(cudaGetDeviceCount(&deviceCount)); - - if (deviceCount == 0) { - std::cerr << "CUDA error: no devices supporting CUDA." << std::endl; - exit(EXIT_FAILURE); - } - - int dev = findCudaDevice(argc, argv); - - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, dev); - std::cerr << "cudaSetDevice GPU" << dev << " = " << deviceProp.name << std::endl; - - checkCudaErrors(cudaSetDevice(dev)); - - return dev; -} - -int main(int argc, char *argv[]) -{ - printf("%s Starting...\n\n", argv[0]); - - try { - std::string sFilename; - char *filePath; - - cudaDeviceInit(argc, (const char **)argv); - - NppStreamContext nppStreamCtx; - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError_t cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - if (checkCmdLineFlag(argc, (const char **)argv, "input")) { - getCmdLineArgumentString(argc, (const char **)argv, "input", &filePath); - } - else { - filePath = sdkFindFilePath("teapot512.pgm", argv[0]); - } - - if (filePath) { - sFilename = filePath; - } - else { - sFilename = "teapot512.pgm"; - } - - // if we specify the filename at the command line, then we only test - // sFilename[0]. - int file_errors = 0; - std::ifstream infile(sFilename.data(), std::ifstream::in); - - if (infile.good()) { - std::cout << "cannyEdgeDetectionNPP opened: <" << sFilename.data() << "> successfully!" << std::endl; - file_errors = 0; - infile.close(); - } - else { - std::cout << "cannyEdgeDetectionNPP unable to open: <" << sFilename.data() << ">" << std::endl; - file_errors++; - infile.close(); - } - - if (file_errors > 0) { - exit(EXIT_FAILURE); - } - - std::string sResultFilename = sFilename; - - std::string::size_type dot = sResultFilename.rfind('.'); - - if (dot != std::string::npos) { - sResultFilename = sResultFilename.substr(0, dot); - } - - sResultFilename += "_cannyEdgeDetection.pgm"; - - if (checkCmdLineFlag(argc, (const char **)argv, "output")) { - char *outputFilePath; - getCmdLineArgumentString(argc, (const char **)argv, "output", &outputFilePath); - sResultFilename = outputFilePath; - } - - // declare a host image object for an 8-bit grayscale image - npp::ImageCPU_8u_C1 oHostSrc; - // load gray-scale image from disk - npp::loadImage(sFilename, oHostSrc); - // declare a device image and copy construct from the host image, - // i.e. upload host to device - npp::ImageNPP_8u_C1 oDeviceSrc(oHostSrc); - - NppiSize oSrcSize = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - NppiPoint oSrcOffset = {0, 0}; - - // create struct with ROI size - NppiSize oSizeROI = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; - // allocate device image of appropriately reduced size - npp::ImageNPP_8u_C1 oDeviceDst(oSizeROI.width, oSizeROI.height); - - int nBufferSize = 0; - Npp8u *pScratchBufferNPP = 0; - - // get necessary scratch buffer size and allocate that much device memory - NPP_CHECK_NPP(nppiFilterCannyBorderGetBufferSize(oSizeROI, &nBufferSize)); - - cudaMalloc((void **)&pScratchBufferNPP, nBufferSize); - - // now run the canny edge detection filter - // Using nppiNormL2 will produce larger magnitude values allowing for finer - // control of threshold values while nppiNormL1 will be slightly faster. - // Also, selecting the sobel gradient filter allows up to a 5x5 kernel size - // which can produce more precise results but is a bit slower. Commonly - // nppiNormL2 and sobel gradient filter size of 3x3 are used. Canny - // recommends that the high threshold value should be about 3 times the low - // threshold value. The threshold range will depend on the range of - // magnitude values that the sobel gradient filter generates for a - // particular image. - - Npp16s nLowThreshold = 72; - Npp16s nHighThreshold = 256; - - if ((nBufferSize > 0) && (pScratchBufferNPP != 0)) { - NPP_CHECK_NPP(nppiFilterCannyBorder_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oSrcSize, - oSrcOffset, - oDeviceDst.data(), - oDeviceDst.pitch(), - oSizeROI, - NPP_FILTER_SOBEL, - NPP_MASK_SIZE_3_X_3, - nLowThreshold, - nHighThreshold, - nppiNormL2, - NPP_BORDER_REPLICATE, - pScratchBufferNPP, - nppStreamCtx)); - } - - // free scratch buffer memory - cudaFree(pScratchBufferNPP); - - // declare a host image for the result - npp::ImageCPU_8u_C1 oHostDst(oDeviceDst.size()); - // and copy the device result data into it - oDeviceDst.copyTo(oHostDst.data(), oHostDst.pitch()); - - saveImage(sResultFilename, oHostDst); - std::cout << "Saved image: " << sResultFilename << std::endl; - - nppiFree(oDeviceSrc.data()); - nppiFree(oDeviceDst.data()); - - exit(EXIT_SUCCESS); - } - catch (npp::Exception &rException) { - std::cerr << "Program error! The following exception occurred: \n"; - std::cerr << rException << std::endl; - std::cerr << "Aborting." << std::endl; - - exit(EXIT_FAILURE); - } - catch (...) { - std::cerr << "Program error! An unknow type of exception occurred. \n"; - std::cerr << "Aborting." << std::endl; - - exit(EXIT_FAILURE); - return -1; - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/teapot512.pgm b/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/teapot512.pgm deleted file mode 100644 index 23db9b9a..00000000 Binary files a/cpp/4_CUDA_Libraries/cannyEdgeDetectorNPP/teapot512.pgm and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradient/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradient/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradient/CMakeLists.txt deleted file mode 100644 index bc1a5c4a..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradient/CMakeLists.txt +++ /dev/null @@ -1,44 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(conjugateGradient LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for conjugateGradient -add_executable(conjugateGradient main.cpp) - -target_compile_options(conjugateGradient PRIVATE $<$:--extended-lambda>) - -target_compile_features(conjugateGradient PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(conjugateGradient PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(conjugateGradient PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(conjugateGradient PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusparse -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradient/README.md b/cpp/4_CUDA_Libraries/conjugateGradient/README.md deleted file mode 100644 index 150cf63f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradient/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# conjugateGradient - ConjugateGradient - -## Description - -This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library. - -## Key Concepts - -Linear Algebra, CUBLAS Library, CUSPARSE Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaFree, cudaDeviceSynchronize, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/conjugateGradient/main.cpp b/cpp/4_CUDA_Libraries/conjugateGradient/main.cpp deleted file mode 100644 index d2391bc0..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradient/main.cpp +++ /dev/null @@ -1,302 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample implements a conjugate gradient solver on GPU - * using CUBLAS and CUSPARSE - * - */ - -// includes, system -#include -#include -#include - -/* Using updated (v2) interfaces to cublas */ -#include -#include -#include - -// Utilities and system includes -#include // helper function CUDA error checking and initialization -#include // helper for shared functions common to CUDA Samples - -const char *sSDKname = "conjugateGradient"; - -/* genTridiag: generate a random tridiagonal symmetric matrix */ -void genTridiag(int *I, int *J, float *val, int N, int nz) -{ - I[0] = 0, J[0] = 0, J[1] = 1; - val[0] = (float)rand() / RAND_MAX + 10.0f; - val[1] = (float)rand() / RAND_MAX; - int start; - - for (int i = 1; i < N; i++) { - if (i > 1) { - I[i] = I[i - 1] + 3; - } - else { - I[1] = 2; - } - - start = (i - 1) * 3 + 2; - J[start] = i - 1; - J[start + 1] = i; - - if (i < N - 1) { - J[start + 2] = i + 1; - } - - val[start] = val[start - 1]; - val[start + 1] = (float)rand() / RAND_MAX + 10.0f; - - if (i < N - 1) { - val[start + 2] = (float)rand() / RAND_MAX; - } - } - - I[N] = nz; -} - -int main(int argc, char **argv) -{ - int M = 0, N = 0, nz = 0, *I = NULL, *J = NULL; - float *val = NULL; - const float tol = 1e-5f; - const int max_iter = 10000; - float *x; - float *rhs; - float a, b, na, r0, r1; - int *d_col, *d_row; - float *d_val, *d_x, dot; - float *d_r, *d_p, *d_Ax; - int k; - float alpha, beta, alpham1; - - // This will pick the best possible CUDA capable device - cudaDeviceProp deviceProp; - int devID = findCudaDevice(argc, (const char **)argv); - - if (devID < 0) { - printf("exiting...\n"); - exit(EXIT_SUCCESS); - } - - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID)); - - // Statistics about the GPU device - printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n", - deviceProp.multiProcessorCount, - deviceProp.major, - deviceProp.minor); - - /* Generate a random tridiagonal symmetric matrix in CSR format */ - M = N = 1048576; - nz = (N - 2) * 3 + 4; - I = (int *)malloc(sizeof(int) * (N + 1)); - J = (int *)malloc(sizeof(int) * nz); - val = (float *)malloc(sizeof(float) * nz); - genTridiag(I, J, val, N, nz); - - x = (float *)malloc(sizeof(float) * N); - rhs = (float *)malloc(sizeof(float) * N); - - for (int i = 0; i < N; i++) { - rhs[i] = 1.0; - x[i] = 0.0; - } - - /* Get handle to the CUBLAS context */ - cublasHandle_t cublasHandle = 0; - cublasStatus_t cublasStatus; - cublasStatus = cublasCreate(&cublasHandle); - - checkCudaErrors(cublasStatus); - - /* Get handle to the CUSPARSE context */ - cusparseHandle_t cusparseHandle = 0; - checkCudaErrors(cusparseCreate(&cusparseHandle)); - - checkCudaErrors(cudaMalloc((void **)&d_col, nz * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_row, (N + 1) * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_val, nz * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_x, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_r, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_p, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_Ax, N * sizeof(float))); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - checkCudaErrors(cusparseCreateCsr(&matA, - N, - N, - nz, - d_row, - d_col, - d_val, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_32F)); - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, N, d_x, CUDA_R_32F)); - cusparseDnVecDescr_t vecp = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, N, d_Ax, CUDA_R_32F)); - - /* Initialize problem data */ - cudaMemcpy(d_col, J, nz * sizeof(int), cudaMemcpyHostToDevice); - cudaMemcpy(d_row, I, (N + 1) * sizeof(int), cudaMemcpyHostToDevice); - cudaMemcpy(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice); - cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice); - cudaMemcpy(d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice); - - alpha = 1.0; - alpham1 = -1.0; - beta = 0.0; - r0 = 0.; - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecx, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - /* Begin CG */ - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecx, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - cublasSaxpy(cublasHandle, N, &alpham1, d_Ax, 1, d_r, 1); - cublasStatus = cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); - - k = 1; - - while (r1 > tol * tol && k <= max_iter) { - if (k > 1) { - b = r1 / r0; - cublasStatus = cublasSscal(cublasHandle, N, &b, d_p, 1); - cublasStatus = cublasSaxpy(cublasHandle, N, &alpha, d_r, 1, d_p, 1); - } - else { - cublasStatus = cublasScopy(cublasHandle, N, d_r, 1, d_p, 1); - } - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecp, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - cublasStatus = cublasSdot(cublasHandle, N, d_p, 1, d_Ax, 1, &dot); - a = r1 / dot; - - cublasStatus = cublasSaxpy(cublasHandle, N, &a, d_p, 1, d_x, 1); - na = -a; - cublasStatus = cublasSaxpy(cublasHandle, N, &na, d_Ax, 1, d_r, 1); - - r0 = r1; - cublasStatus = cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1); - cudaDeviceSynchronize(); - printf("iteration = %3d, residual = %e\n", k, sqrt(r1)); - k++; - } - - cudaMemcpy(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost); - - float rsum, diff, err = 0.0; - - for (int i = 0; i < N; i++) { - rsum = 0.0; - - for (int j = I[i]; j < I[i + 1]; j++) { - rsum += val[j] * x[J[j]]; - } - - diff = fabs(rsum - rhs[i]); - - if (diff > err) { - err = diff; - } - } - - if (buffer) { - checkCudaErrors(cudaFree(buffer)); - } - - cusparseDestroy(cusparseHandle); - cublasDestroy(cublasHandle); - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - if (vecp) { - checkCudaErrors(cusparseDestroyDnVec(vecp)); - } - - free(I); - free(J); - free(val); - free(x); - free(rhs); - cudaFree(d_col); - cudaFree(d_row); - cudaFree(d_val); - cudaFree(d_x); - cudaFree(d_r); - cudaFree(d_p); - cudaFree(d_Ax); - - printf("Test Summary: Error amount = %f\n", err); - exit((k <= max_iter) ? 0 : 1); -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/CMakeLists.txt index 2636bf60..6bad6cd1 100644 --- a/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/conjugateGradientCudaGraphs/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(conjugateGradientCudaGraphs LANGUAGES CUDA CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -13,19 +20,8 @@ find_package(CUDAToolkit REQUIRED) set(CMAKE_POSITION_INDEPENDENT_CODE ON) -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for conjugateGradientCudaGraphs add_executable(conjugateGradientCudaGraphs conjugateGradientCudaGraphs.cu) target_compile_options(conjugateGradientCudaGraphs PRIVATE $<$:--extended-lambda>) @@ -39,6 +35,5 @@ target_link_libraries(conjugateGradientCudaGraphs PRIVATE CUDA::cusparse ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/CMakeLists.txt index df0b3535..1dd7355f 100644 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/CMakeLists.txt @@ -1,24 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(conjugateGradientMultiBlockCG LANGUAGES CUDA CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -maxrregcount=128") # limit register usage to 128 per thread to comply with the maximum number of 32-bit registers per SM -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -27,21 +21,16 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for conjugateGradientMultiBlockCG add_executable(conjugateGradientMultiBlockCG conjugateGradientMultiBlockCG.cu) target_compile_options(conjugateGradientMultiBlockCG PRIVATE $<$:--extended-lambda>) target_compile_features(conjugateGradientMultiBlockCG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(conjugateGradientMultiBlockCG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(conjugateGradientMultiBlockCG PRIVATE CUDA::cublas CUDA::cusparse ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/conjugateGradientMultiBlockCG.cu b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/conjugateGradientMultiBlockCG.cu index 879dfecf..9a5d84d7 100644 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/conjugateGradientMultiBlockCG.cu +++ b/cpp/4_CUDA_Libraries/conjugateGradientMultiBlockCG/conjugateGradientMultiBlockCG.cu @@ -257,7 +257,9 @@ gpuScaleVectorAndSaxpy(const float *x, float *y, float a, float scale, int size, } } -extern "C" __global__ void gpuConjugateGradient(int *I, +// __launch_bounds__ keeps one full block resident: under -G, register inflation can drop +// occupancy to 0 blocks/SM, zeroing the cooperative grid and failing the launch (Blackwell sm_110+). +extern "C" __global__ void __launch_bounds__(THREADS_PER_BLOCK) gpuConjugateGradient(int *I, int *J, float *val, float *x, diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/CMakeLists.txt index 613d4db8..2a1bac62 100644 --- a/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/conjugateGradientMultiDeviceCG/CMakeLists.txt @@ -1,23 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(conjugateGradientMultiDeviceCG LANGUAGES CUDA CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -26,21 +21,16 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for conjugateGradientMultiDeviceCG add_executable(conjugateGradientMultiDeviceCG conjugateGradientMultiDeviceCG.cu) target_compile_options(conjugateGradientMultiDeviceCG PRIVATE $<$:--extended-lambda>) target_compile_features(conjugateGradientMultiDeviceCG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(conjugateGradientMultiDeviceCG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(conjugateGradientMultiDeviceCG PRIVATE CUDA::cublas CUDA::cusparse ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradientPrecond/CMakeLists.txt deleted file mode 100644 index b20f5aec..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/CMakeLists.txt +++ /dev/null @@ -1,44 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(conjugateGradientPrecond LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for conjugateGradientPrecond -add_executable(conjugateGradientPrecond main.cpp) - -target_compile_options(conjugateGradientPrecond PRIVATE $<$:--extended-lambda>) - -target_compile_features(conjugateGradientPrecond PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(conjugateGradientPrecond PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(conjugateGradientPrecond PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(conjugateGradientPrecond PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusparse -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/README.md b/cpp/4_CUDA_Libraries/conjugateGradientPrecond/README.md deleted file mode 100644 index 30a00455..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# conjugateGradientPrecond - Preconditioned Conjugate Gradient - -## Description - -This sample implements a preconditioned conjugate gradient solver on GPU using CUBLAS and CUSPARSE library. - -## Key Concepts - -Linear Algebra, CUBLAS Library, CUSPARSE Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaFree, cudaMemset, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/main.cpp b/cpp/4_CUDA_Libraries/conjugateGradientPrecond/main.cpp deleted file mode 100644 index 1d1ea9a9..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientPrecond/main.cpp +++ /dev/null @@ -1,626 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample implements a preconditioned conjugate gradient solver on - * the GPU using CUBLAS and CUSPARSE. Relative to the conjugateGradient - * SDK example, this demonstrates the use of cusparseScsrilu02() for - * computing the incompute-LU preconditioner and cusparseScsrsv2_solve() - * for solving triangular systems. Specifically, the preconditioned - * conjugate gradient method with an incomplete LU preconditioner is - * used to solve the Laplacian operator in 2D on a uniform mesh. - * - * Note that the code in this example and the specific matrices used here - * were chosen to demonstrate the use of the CUSPARSE library as simply - * and as clearly as possible. This is not optimized code and the input - * matrices have been chosen for simplicity rather than performance. - * These should not be used either as a performance guide or for - * benchmarking purposes. - */ - - -// includes, system -#include -#include -#include -#include - -// CUDA Runtime -#include - -// Using updated (v2) interfaces for CUBLAS and CUSPARSE -#include -#include - -// Utilities and system includes -#include // CUDA error checking -#include // shared functions common to CUDA Samples - -const char *sSDKname = "conjugateGradientPrecond"; - -/* - * Generate a matrix representing a second order regular Laplacian operator - * on a 2D domain in Compressed Sparse Row format. - */ -void genLaplace(int *row_ptr, int *col_ind, float *val, int M, int N, int nz, float *rhs) -{ - assert(M == N); - int n = (int)sqrt((double)N); - assert(n * n == N); - printf("laplace dimension = %d\n", n); - int idx = 0; - - // loop over degrees of freedom - for (int i = 0; i < N; i++) { - int ix = i % n; - int iy = i / n; - - row_ptr[i] = idx; - - // up - if (iy > 0) { - val[idx] = 1.0; - col_ind[idx] = i - n; - idx++; - } - else { - rhs[i] -= 1.0; - } - - // left - if (ix > 0) { - val[idx] = 1.0; - col_ind[idx] = i - 1; - idx++; - } - else { - rhs[i] -= 0.0; - } - - // center - val[idx] = -4.0; - col_ind[idx] = i; - idx++; - - // right - if (ix < n - 1) { - val[idx] = 1.0; - col_ind[idx] = i + 1; - idx++; - } - else { - rhs[i] -= 0.0; - } - - // down - if (iy < n - 1) { - val[idx] = 1.0; - col_ind[idx] = i + n; - idx++; - } - else { - rhs[i] -= 0.0; - } - } - - row_ptr[N] = idx; -} - -/* - * Solve Ax=b using the conjugate gradient method - * a) without any preconditioning, - * b) using an Incomplete Cholesky preconditioner, and - * c) using an ILU0 preconditioner. - */ -int main(int argc, char **argv) -{ - const int max_iter = 1000; - int k, M = 0, N = 0, nz = 0, *I = NULL, *J = NULL; - int *d_col, *d_row; - int qatest = 0; - const float tol = 1e-12f; - float *x, *rhs; - float r0, r1, alpha, beta; - float *d_val, *d_x; - float *d_zm1, *d_zm2, *d_rm2; - float *d_r, *d_p, *d_omega, *d_y; - float *val = NULL; - float *d_valsILU0; - float rsum, diff, err = 0.0; - float qaerr1, qaerr2 = 0.0; - float dot, numerator, denominator, nalpha; - const float floatone = 1.0; - const float floatzero = 0.0; - - int nErrors = 0; - - printf("conjugateGradientPrecond starting...\n"); - - /* QA testing mode */ - if (checkCmdLineFlag(argc, (const char **)argv, "qatest")) { - qatest = 1; - } - - /* This will pick the best possible CUDA capable device */ - cudaDeviceProp deviceProp; - int devID = findCudaDevice(argc, (const char **)argv); - printf("GPU selected Device ID = %d \n", devID); - - if (devID < 0) { - printf("Invalid GPU device %d selected, exiting...\n", devID); - exit(EXIT_SUCCESS); - } - - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID)); - - /* Statistics about the GPU device */ - printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n", - deviceProp.multiProcessorCount, - deviceProp.major, - deviceProp.minor); - - /* Generate a Laplace matrix in CSR (Compressed Sparse Row) format */ - M = N = 16384; - nz = 5 * N - 4 * (int)sqrt((double)N); - I = (int *)malloc(sizeof(int) * (N + 1)); // csr row pointers for matrix A - J = (int *)malloc(sizeof(int) * nz); // csr column indices for matrix A - val = (float *)malloc(sizeof(float) * nz); // csr values for matrix A - x = (float *)malloc(sizeof(float) * N); - rhs = (float *)malloc(sizeof(float) * N); - - for (int i = 0; i < N; i++) { - rhs[i] = 0.0; // Initialize RHS - x[i] = 0.0; // Initial solution approximation - } - - genLaplace(I, J, val, M, N, nz, rhs); - - /* Create CUBLAS context */ - cublasHandle_t cublasHandle = NULL; - checkCudaErrors(cublasCreate(&cublasHandle)); - - /* Create CUSPARSE context */ - cusparseHandle_t cusparseHandle = NULL; - checkCudaErrors(cusparseCreate(&cusparseHandle)); - - /* Description of the A matrix */ - cusparseMatDescr_t descr = 0; - checkCudaErrors(cusparseCreateMatDescr(&descr)); - checkCudaErrors(cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL)); - checkCudaErrors(cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO)); - - /* Allocate required memory */ - checkCudaErrors(cudaMalloc((void **)&d_col, nz * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_row, (N + 1) * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_val, nz * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_x, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_y, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_r, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_p, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_omega, N * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_valsILU0, nz * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_zm1, (N) * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_zm2, (N) * sizeof(float))); - checkCudaErrors(cudaMalloc((void **)&d_rm2, (N) * sizeof(float))); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseDnVecDescr_t vecp = NULL, vecX = NULL, vecY = NULL, vecR = NULL, vecZM1 = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecp, N, d_p, CUDA_R_32F)); - checkCudaErrors(cusparseCreateDnVec(&vecX, N, d_x, CUDA_R_32F)); - checkCudaErrors(cusparseCreateDnVec(&vecY, N, d_y, CUDA_R_32F)); - checkCudaErrors(cusparseCreateDnVec(&vecR, N, d_r, CUDA_R_32F)); - checkCudaErrors(cusparseCreateDnVec(&vecZM1, N, d_zm1, CUDA_R_32F)); - cusparseDnVecDescr_t vecomega = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecomega, N, d_omega, CUDA_R_32F)); - - /* Initialize problem data */ - checkCudaErrors(cudaMemcpy(d_col, J, nz * sizeof(int), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_row, I, (N + 1) * sizeof(int), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_val, val, nz * sizeof(float), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice)); - - cusparseSpMatDescr_t matA = NULL; - cusparseSpMatDescr_t matM_lower, matM_upper; - cusparseFillMode_t fill_lower = CUSPARSE_FILL_MODE_LOWER; - cusparseDiagType_t diag_unit = CUSPARSE_DIAG_TYPE_UNIT; - cusparseFillMode_t fill_upper = CUSPARSE_FILL_MODE_UPPER; - cusparseDiagType_t diag_non_unit = CUSPARSE_DIAG_TYPE_NON_UNIT; - - checkCudaErrors(cusparseCreateCsr(&matA, - N, - N, - nz, - d_row, - d_col, - d_val, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_32F)); - - /* Copy A data to ILU(0) vals as input*/ - checkCudaErrors(cudaMemcpy(d_valsILU0, d_val, nz * sizeof(float), cudaMemcpyDeviceToDevice)); - - // Lower Part - checkCudaErrors(cusparseCreateCsr(&matM_lower, - N, - N, - nz, - d_row, - d_col, - d_valsILU0, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_32F)); - - checkCudaErrors(cusparseSpMatSetAttribute(matM_lower, CUSPARSE_SPMAT_FILL_MODE, &fill_lower, sizeof(fill_lower))); - checkCudaErrors(cusparseSpMatSetAttribute(matM_lower, CUSPARSE_SPMAT_DIAG_TYPE, &diag_unit, sizeof(diag_unit))); - // M_upper - checkCudaErrors(cusparseCreateCsr(&matM_upper, - N, - N, - nz, - d_row, - d_col, - d_valsILU0, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_32F)); - checkCudaErrors(cusparseSpMatSetAttribute(matM_upper, CUSPARSE_SPMAT_FILL_MODE, &fill_upper, sizeof(fill_upper))); - checkCudaErrors( - cusparseSpMatSetAttribute(matM_upper, CUSPARSE_SPMAT_DIAG_TYPE, &diag_non_unit, sizeof(diag_non_unit))); - - - /* Create ILU(0) info object */ - int bufferSizeLU = 0; - size_t bufferSizeMV, bufferSizeL, bufferSizeU; - void *d_bufferLU, *d_bufferMV, *d_bufferL, *d_bufferU; - cusparseSpSVDescr_t spsvDescrL, spsvDescrU; - cusparseMatDescr_t matLU; - csrilu02Info_t infoILU = NULL; - - checkCudaErrors(cusparseCreateCsrilu02Info(&infoILU)); - checkCudaErrors(cusparseCreateMatDescr(&matLU)); - checkCudaErrors(cusparseSetMatType(matLU, CUSPARSE_MATRIX_TYPE_GENERAL)); - checkCudaErrors(cusparseSetMatIndexBase(matLU, CUSPARSE_INDEX_BASE_ZERO)); - - /* Allocate workspace for cuSPARSE */ - checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matA, - vecp, - &floatzero, - vecomega, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSizeMV)); - checkCudaErrors(cudaMalloc(&d_bufferMV, bufferSizeMV)); - - checkCudaErrors( - cusparseScsrilu02_bufferSize(cusparseHandle, N, nz, matLU, d_val, d_row, d_col, infoILU, &bufferSizeLU)); - checkCudaErrors(cudaMalloc(&d_bufferLU, bufferSizeLU)); - - checkCudaErrors(cusparseSpSV_createDescr(&spsvDescrL)); - checkCudaErrors(cusparseSpSV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_lower, - vecR, - vecX, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrL, - &bufferSizeL)); - checkCudaErrors(cudaMalloc(&d_bufferL, bufferSizeL)); - - checkCudaErrors(cusparseSpSV_createDescr(&spsvDescrU)); - checkCudaErrors(cusparseSpSV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_upper, - vecR, - vecX, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrU, - &bufferSizeU)); - checkCudaErrors(cudaMalloc(&d_bufferU, bufferSizeU)); - - /* Conjugate gradient without preconditioning. - ------------------------------------------ - - Follows the description by Golub & Van Loan, - "Matrix Computations 3rd ed.", Section 10.2.6 */ - - printf("Convergence of CG without preconditioning: \n"); - k = 0; - r0 = 0; - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1)); - - while (r1 > tol * tol && k <= max_iter) { - k++; - - if (k == 1) { - checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_p, 1)); - } - else { - beta = r1 / r0; - checkCudaErrors(cublasSscal(cublasHandle, N, &beta, d_p, 1)); - checkCudaErrors(cublasSaxpy(cublasHandle, N, &floatone, d_r, 1, d_p, 1)); - } - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matA, - vecp, - &floatzero, - vecomega, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - d_bufferMV)); - checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &dot)); - alpha = r1 / dot; - checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1)); - nalpha = -alpha; - checkCudaErrors(cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1)); - r0 = r1; - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1)); - } - - printf(" iteration = %3d, residual = %e \n", k, sqrt(r1)); - - checkCudaErrors(cudaMemcpy(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost)); - - /* check result */ - err = 0.0; - - for (int i = 0; i < N; i++) { - rsum = 0.0; - - for (int j = I[i]; j < I[i + 1]; j++) { - rsum += val[j] * x[J[j]]; - } - - diff = fabs(rsum - rhs[i]); - - if (diff > err) { - err = diff; - } - } - - printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL"); - nErrors += (k > max_iter) ? 1 : 0; - qaerr1 = err; - - if (0) { - // output result in matlab-style array - int n = (int)sqrt((double)N); - printf("a = [ "); - - for (int iy = 0; iy < n; iy++) { - for (int ix = 0; ix < n; ix++) { - printf(" %f ", x[iy * n + ix]); - } - - if (iy == n - 1) { - printf(" ]"); - } - - printf("\n"); - } - } - - - /* Preconditioned Conjugate Gradient using ILU. - -------------------------------------------- - Follows the description by Golub & Van Loan, - "Matrix Computations 3rd ed.", Algorithm 10.3.1 */ - - printf("\nConvergence of CG using ILU(0) preconditioning: \n"); - - /* Perform analysis for ILU(0) */ - checkCudaErrors(cusparseScsrilu02_analysis( - cusparseHandle, N, nz, descr, d_valsILU0, d_row, d_col, infoILU, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU)); - - /* generate the ILU(0) factors */ - checkCudaErrors(cusparseScsrilu02( - cusparseHandle, N, nz, matLU, d_valsILU0, d_row, d_col, infoILU, CUSPARSE_SOLVE_POLICY_USE_LEVEL, d_bufferLU)); - - /* perform triangular solve analysis */ - checkCudaErrors(cusparseSpSV_analysis(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_lower, - vecR, - vecX, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrL, - d_bufferL)); - - checkCudaErrors(cusparseSpSV_analysis(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_upper, - vecR, - vecX, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrU, - d_bufferU)); - - /* reset the initial guess of the solution to zero */ - for (int i = 0; i < N; i++) { - x[i] = 0.0; - } - checkCudaErrors(cudaMemcpy(d_r, rhs, N * sizeof(float), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice)); - - k = 0; - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1)); - - while (r1 > tol * tol && k <= max_iter) { - // preconditioner application: d_zm1 = U^-1 L^-1 d_r - checkCudaErrors(cusparseSpSV_solve(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_lower, - vecR, - vecY, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrL)); - - checkCudaErrors(cusparseSpSV_solve(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matM_upper, - vecY, - vecZM1, - CUDA_R_32F, - CUSPARSE_SPSV_ALG_DEFAULT, - spsvDescrU)); - k++; - - if (k == 1) { - checkCudaErrors(cublasScopy(cublasHandle, N, d_zm1, 1, d_p, 1)); - } - else { - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator)); - checkCudaErrors(cublasSdot(cublasHandle, N, d_rm2, 1, d_zm2, 1, &denominator)); - beta = numerator / denominator; - checkCudaErrors(cublasSscal(cublasHandle, N, &beta, d_p, 1)); - checkCudaErrors(cublasSaxpy(cublasHandle, N, &floatone, d_zm1, 1, d_p, 1)); - } - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &floatone, - matA, - vecp, - &floatzero, - vecomega, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - d_bufferMV)); - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_zm1, 1, &numerator)); - checkCudaErrors(cublasSdot(cublasHandle, N, d_p, 1, d_omega, 1, &denominator)); - alpha = numerator / denominator; - checkCudaErrors(cublasSaxpy(cublasHandle, N, &alpha, d_p, 1, d_x, 1)); - checkCudaErrors(cublasScopy(cublasHandle, N, d_r, 1, d_rm2, 1)); - checkCudaErrors(cublasScopy(cublasHandle, N, d_zm1, 1, d_zm2, 1)); - nalpha = -alpha; - checkCudaErrors(cublasSaxpy(cublasHandle, N, &nalpha, d_omega, 1, d_r, 1)); - checkCudaErrors(cublasSdot(cublasHandle, N, d_r, 1, d_r, 1, &r1)); - } - - printf(" iteration = %3d, residual = %e \n", k, sqrt(r1)); - - checkCudaErrors(cudaMemcpy(x, d_x, N * sizeof(float), cudaMemcpyDeviceToHost)); - - /* check result */ - err = 0.0; - - for (int i = 0; i < N; i++) { - rsum = 0.0; - - for (int j = I[i]; j < I[i + 1]; j++) { - rsum += val[j] * x[J[j]]; - } - - diff = fabs(rsum - rhs[i]); - - if (diff > err) { - err = diff; - } - } - - printf(" Convergence Test: %s \n", (k <= max_iter) ? "OK" : "FAIL"); - nErrors += (k > max_iter) ? 1 : 0; - qaerr2 = err; - - /* Destroy descriptors */ - checkCudaErrors(cusparseDestroyCsrilu02Info(infoILU)); - checkCudaErrors(cusparseDestroyMatDescr(matLU)); - checkCudaErrors(cusparseSpSV_destroyDescr(spsvDescrL)); - checkCudaErrors(cusparseSpSV_destroyDescr(spsvDescrU)); - checkCudaErrors(cusparseDestroySpMat(matM_lower)); - checkCudaErrors(cusparseDestroySpMat(matM_upper)); - checkCudaErrors(cusparseDestroySpMat(matA)); - checkCudaErrors(cusparseDestroyDnVec(vecp)); - checkCudaErrors(cusparseDestroyDnVec(vecomega)); - checkCudaErrors(cusparseDestroyDnVec(vecR)); - checkCudaErrors(cusparseDestroyDnVec(vecX)); - checkCudaErrors(cusparseDestroyDnVec(vecY)); - checkCudaErrors(cusparseDestroyDnVec(vecZM1)); - - /* Destroy contexts */ - checkCudaErrors(cusparseDestroy(cusparseHandle)); - checkCudaErrors(cublasDestroy(cublasHandle)); - - /* Free device memory */ - free(I); - free(J); - free(val); - free(x); - free(rhs); - checkCudaErrors(cudaFree(d_bufferMV)); - checkCudaErrors(cudaFree(d_bufferLU)); - checkCudaErrors(cudaFree(d_bufferL)); - checkCudaErrors(cudaFree(d_bufferU)); - checkCudaErrors(cudaFree(d_col)); - checkCudaErrors(cudaFree(d_row)); - checkCudaErrors(cudaFree(d_val)); - checkCudaErrors(cudaFree(d_x)); - checkCudaErrors(cudaFree(d_y)); - checkCudaErrors(cudaFree(d_r)); - checkCudaErrors(cudaFree(d_p)); - checkCudaErrors(cudaFree(d_omega)); - checkCudaErrors(cudaFree(d_valsILU0)); - checkCudaErrors(cudaFree(d_zm1)); - checkCudaErrors(cudaFree(d_zm2)); - checkCudaErrors(cudaFree(d_rm2)); - - // cudaDeviceReset causes the driver to clean up all state. While - // not mandatory in normal operation, it is good practice. It is also - // needed to ensure correct operation when the application is being - // profiled. Calling cudaDeviceReset causes all profile data to be - // flushed before the application exits - cudaDeviceReset(); - - printf("\n"); - printf("Test Summary:\n"); - printf(" Counted total of %d errors\n", nErrors); - printf(" qaerr1 = %f qaerr2 = %f\n\n", fabs(qaerr1), fabs(qaerr2)); - exit((nErrors == 0 && fabs(qaerr1) < 1e-5 && fabs(qaerr2) < 1e-5 ? EXIT_SUCCESS : EXIT_FAILURE)); -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/extensions.json b/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientUM/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/conjugateGradientUM/CMakeLists.txt b/cpp/4_CUDA_Libraries/conjugateGradientUM/CMakeLists.txt deleted file mode 100644 index 4cbcc4da..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientUM/CMakeLists.txt +++ /dev/null @@ -1,44 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(conjugateGradientUM LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for conjugateGradientUM -add_executable(conjugateGradientUM main.cpp) - -target_compile_options(conjugateGradientUM PRIVATE $<$:--extended-lambda>) - -target_compile_features(conjugateGradientUM PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(conjugateGradientUM PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(conjugateGradientUM PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(conjugateGradientUM PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusparse -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/conjugateGradientUM/README.md b/cpp/4_CUDA_Libraries/conjugateGradientUM/README.md deleted file mode 100644 index 36038190..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientUM/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# conjugateGradientUM - ConjugateGradientUM - -## Description - -This sample implements a conjugate gradient solver on GPU using CUBLAS and CUSPARSE library, using Unified Memory - -## Key Concepts - -Unified Memory, Linear Algebra, CUBLAS Library, CUSPARSE Library - -## Supported SM Architectures - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaFree, cudaMallocManaged, cudaDeviceSynchronize, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[UVM](../../../README.md#unified-virtual-memory), [CUBLAS](../../../README.md#cublas), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/conjugateGradientUM/main.cpp b/cpp/4_CUDA_Libraries/conjugateGradientUM/main.cpp deleted file mode 100644 index baa74997..00000000 --- a/cpp/4_CUDA_Libraries/conjugateGradientUM/main.cpp +++ /dev/null @@ -1,296 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample implements a conjugate gradient solver on GPU - * using CUBLAS and CUSPARSE - * - */ - -// includes, system -#include -#include -#include - -/* Using updated (v2) interfaces to cublas and cusparse */ -#include -#include -#include - -// Utilities and system includes -#include // helper function CUDA error checking and initialization -#include // helper for shared functions common to CUDA Samples - -const char *sSDKname = "conjugateGradientUM"; - -/* genTridiag: generate a random tridiagonal symmetric matrix */ -void genTridiag(int *I, int *J, float *val, int N, int nz) -{ - I[0] = 0, J[0] = 0, J[1] = 1; - val[0] = (float)rand() / RAND_MAX + 10.0f; - val[1] = (float)rand() / RAND_MAX; - int start; - - for (int i = 1; i < N; i++) { - if (i > 1) { - I[i] = I[i - 1] + 3; - } - else { - I[1] = 2; - } - - start = (i - 1) * 3 + 2; - J[start] = i - 1; - J[start + 1] = i; - - if (i < N - 1) { - J[start + 2] = i + 1; - } - - val[start] = val[start - 1]; - val[start + 1] = (float)rand() / RAND_MAX + 10.0f; - - if (i < N - 1) { - val[start + 2] = (float)rand() / RAND_MAX; - } - } - - I[N] = nz; -} - -int main(int argc, char **argv) -{ - int N = 0, nz = 0, *I = NULL, *J = NULL; - float *val = NULL; - const float tol = 1e-5f; - const int max_iter = 10000; - float *x; - float *rhs; - float a, b, na, r0, r1; - float dot; - float *r, *p, *Ax; - int k; - float alpha, beta, alpham1; - - printf("Starting [%s]...\n", sSDKname); - - // This will pick the best possible CUDA capable device - cudaDeviceProp deviceProp; - int devID = findCudaDevice(argc, (const char **)argv); - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID)); - - if (!deviceProp.managedMemory) { - // This samples requires being run on a device that supports Unified Memory - fprintf(stderr, "Unified Memory not supported on this device\n"); - exit(EXIT_WAIVED); - } - - // Statistics about the GPU device - printf("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n\n", - deviceProp.multiProcessorCount, - deviceProp.major, - deviceProp.minor); - - /* Generate a random tridiagonal symmetric matrix in CSR format */ - N = 1048576; - nz = (N - 2) * 3 + 4; - - cudaMallocManaged((void **)&I, sizeof(int) * (N + 1)); - cudaMallocManaged((void **)&J, sizeof(int) * nz); - cudaMallocManaged((void **)&val, sizeof(float) * nz); - - genTridiag(I, J, val, N, nz); - - cudaMallocManaged((void **)&x, sizeof(float) * N); - cudaMallocManaged((void **)&rhs, sizeof(float) * N); - - for (int i = 0; i < N; i++) { - rhs[i] = 1.0; - x[i] = 0.0; - } - - /* Get handle to the CUBLAS context */ - cublasHandle_t cublasHandle = 0; - cublasStatus_t cublasStatus; - cublasStatus = cublasCreate(&cublasHandle); - - checkCudaErrors(cublasStatus); - - /* Get handle to the CUSPARSE context */ - cusparseHandle_t cusparseHandle = 0; - cusparseStatus_t cusparseStatus; - cusparseStatus = cusparseCreate(&cusparseHandle); - - checkCudaErrors(cusparseStatus); - - cusparseMatDescr_t descr = 0; - cusparseStatus = cusparseCreateMatDescr(&descr); - - checkCudaErrors(cusparseStatus); - - cusparseSetMatType(descr, CUSPARSE_MATRIX_TYPE_GENERAL); - cusparseSetMatIndexBase(descr, CUSPARSE_INDEX_BASE_ZERO); - - // temp memory for CG - checkCudaErrors(cudaMallocManaged((void **)&r, N * sizeof(float))); - checkCudaErrors(cudaMallocManaged((void **)&p, N * sizeof(float))); - checkCudaErrors(cudaMallocManaged((void **)&Ax, N * sizeof(float))); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - checkCudaErrors(cusparseCreateCsr( - &matA, N, N, nz, I, J, val, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_32F)); - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, N, x, CUDA_R_32F)); - cusparseDnVecDescr_t vecp = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecp, N, p, CUDA_R_32F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, N, Ax, CUDA_R_32F)); - - cudaDeviceSynchronize(); - - for (int i = 0; i < N; i++) { - r[i] = rhs[i]; - } - - alpha = 1.0; - alpham1 = -1.0; - beta = 0.0; - r0 = 0.; - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecx, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecx, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - cublasSaxpy(cublasHandle, N, &alpham1, Ax, 1, r, 1); - cublasStatus = cublasSdot(cublasHandle, N, r, 1, r, 1, &r1); - - k = 1; - - while (r1 > tol * tol && k <= max_iter) { - if (k > 1) { - b = r1 / r0; - cublasStatus = cublasSscal(cublasHandle, N, &b, p, 1); - cublasStatus = cublasSaxpy(cublasHandle, N, &alpha, r, 1, p, 1); - } - else { - cublasStatus = cublasScopy(cublasHandle, N, r, 1, p, 1); - } - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &alpha, - matA, - vecp, - &beta, - vecAx, - CUDA_R_32F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - cublasStatus = cublasSdot(cublasHandle, N, p, 1, Ax, 1, &dot); - a = r1 / dot; - - cublasStatus = cublasSaxpy(cublasHandle, N, &a, p, 1, x, 1); - na = -a; - cublasStatus = cublasSaxpy(cublasHandle, N, &na, Ax, 1, r, 1); - - r0 = r1; - cublasStatus = cublasSdot(cublasHandle, N, r, 1, r, 1, &r1); - cudaDeviceSynchronize(); - printf("iteration = %3d, residual = %e\n", k, sqrt(r1)); - k++; - } - - printf("Final residual: %e\n", sqrt(r1)); - - fprintf(stdout, "&&&& conjugateGradientUM %s\n", (sqrt(r1) < tol) ? "PASSED" : "FAILED"); - - float rsum, diff, err = 0.0; - - for (int i = 0; i < N; i++) { - rsum = 0.0; - - for (int j = I[i]; j < I[i + 1]; j++) { - rsum += val[j] * x[J[j]]; - } - - diff = fabs(rsum - rhs[i]); - - if (diff > err) { - err = diff; - } - } - - cusparseDestroy(cusparseHandle); - cublasDestroy(cublasHandle); - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - if (vecp) { - checkCudaErrors(cusparseDestroyDnVec(vecp)); - } - - cudaFree(I); - cudaFree(J); - cudaFree(val); - cudaFree(x); - cudaFree(rhs); - cudaFree(r); - cudaFree(p); - cudaFree(Ax); - - printf("Test Summary: Error amount = %f, result = %s\n", err, (k <= max_iter) ? "SUCCESS" : "FAILURE"); - exit((k <= max_iter) ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/CMakeLists.txt b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/CMakeLists.txt deleted file mode 100644 index 4238fd5d..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/CMakeLists.txt +++ /dev/null @@ -1,56 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cuSolverDn_LinearSolver LANGUAGES C CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for cuSolverDn_LinearSolver -add_executable(cuSolverDn_LinearSolver cuSolverDn_LinearSolver.cpp mmio.c mmio_wrapper.cpp) - -target_compile_options(cuSolverDn_LinearSolver PRIVATE $<$:--extended-lambda>) - -target_compile_features(cuSolverDn_LinearSolver PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(cuSolverDn_LinearSolver PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(cuSolverDn_LinearSolver PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(cuSolverDn_LinearSolver PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusolver -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverDn_LinearSolver POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/gr_900_900_crg.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) -add_custom_command(TARGET cuSolverDn_LinearSolver POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap3D_7pt_n20.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/README.md b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/README.md deleted file mode 100644 index d955ee16..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# cuSolverDn_LinearSolver - cuSolverDn Linear Solver - -## Description - -A CUDA Sample that demonstrates cuSolverDN's LU, QR and Cholesky factorization. - -## Key Concepts - -Linear Algebra, CUSOLVER Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuDoubleComplex, cuComplex - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaStreamDestroy, cudaFree, cudaDeviceSynchronize, cudaMemset, cudaMalloc, cudaStreamCreate - -## Dependencies needed to build/run -[CUSOLVER](../../../README.md#cusolver), [CUBLAS](../../../README.md#cublas), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/cuSolverDn_LinearSolver.cpp b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/cuSolverDn_LinearSolver.cpp deleted file mode 100644 index cd33ceb0..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/cuSolverDn_LinearSolver.cpp +++ /dev/null @@ -1,587 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * Test three linear solvers, including Cholesky, LU and QR. - * The user has to prepare a sparse matrix of "matrix market format" (with - * extension .mtx). For example, the user can download matrices in Florida - * Sparse Matrix Collection. - * (http://www.cise.ufl.edu/research/sparse/matrices/) - * - * The user needs to choose a solver by switch -R and - * to provide the path of the matrix by switch -F, then - * the program solves - * A*x = b where b = ones(m,1) - * and reports relative error - * |b-A*x|/(|A|*|x|) - * - * The elapsed time is also reported so the user can compare efficiency of - * different solvers. - * - * How to use - * ./cuSolverDn_LinearSolver // Default: cholesky - * ./cuSolverDn_LinearSolver -R=chol -filefile> // cholesky factorization - * ./cuSolverDn_LinearSolver -R=lu -file // LU with partial - * pivoting - * ./cuSolverDn_LinearSolver -R=qr -file // QR factorization - * - * Remark: the absolute error on solution x is meaningless without knowing - * condition number of A. The relative error on residual should be close to - * machine zero, i.e. 1.e-15. - */ - -#include -#include -#include -#include -#include -#include - -#include "cublas_v2.h" -#include "cusolverDn.h" -#include "helper_cuda.h" -#include "helper_cusolver.h" - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -void UsageDN(void) -{ - printf("\n"); - printf("-h : display this help\n"); - printf("-R= : choose a linear solver\n"); - printf(" chol (cholesky factorization), this is default\n"); - printf(" qr (QR factorization)\n"); - printf(" lu (LU factorization)\n"); - printf("-lda= : leading dimension of A , m by default\n"); - printf("-file=: filename containing a matrix in MM format\n"); - printf("-device= : if want to run on specific GPU\n"); - - exit(0); -} - -/* - * solve A*x = b by Cholesky factorization - * - */ -int linearSolverCHOL(cusolverDnHandle_t handle, int n, const double *Acopy, int lda, const double *b, double *x) -{ - int bufferSize = 0; - int *info = NULL; - double *buffer = NULL; - double *A = NULL; - int h_info = 0; - double start, stop; - double time_solve; - cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER; - - checkCudaErrors(cusolverDnDpotrf_bufferSize(handle, uplo, n, (double *)Acopy, lda, &bufferSize)); - - checkCudaErrors(cudaMalloc(&info, sizeof(int))); - checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize)); - checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n)); - - // prepare a copy of A because potrf will overwrite A with L - checkCudaErrors(cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice)); - checkCudaErrors(cudaMemset(info, 0, sizeof(int))); - - start = second(); - start = second(); - - checkCudaErrors(cusolverDnDpotrf(handle, uplo, n, A, lda, buffer, bufferSize, info)); - - checkCudaErrors(cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost)); - - if (0 != h_info) { - fprintf(stderr, "Error: Cholesky factorization failed\n"); - } - - checkCudaErrors(cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice)); - - checkCudaErrors(cusolverDnDpotrs(handle, uplo, n, 1, A, lda, x, n, info)); - - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - - time_solve = stop - start; - fprintf(stdout, "timing: cholesky = %10.6f sec\n", time_solve); - - if (info) { - checkCudaErrors(cudaFree(info)); - } - if (buffer) { - checkCudaErrors(cudaFree(buffer)); - } - if (A) { - checkCudaErrors(cudaFree(A)); - } - - return 0; -} - -/* - * solve A*x = b by LU with partial pivoting - * - */ -int linearSolverLU(cusolverDnHandle_t handle, int n, const double *Acopy, int lda, const double *b, double *x) -{ - int bufferSize = 0; - int *info = NULL; - double *buffer = NULL; - double *A = NULL; - int *ipiv = NULL; // pivoting sequence - int h_info = 0; - double start, stop; - double time_solve; - - checkCudaErrors(cusolverDnDgetrf_bufferSize(handle, n, n, (double *)Acopy, lda, &bufferSize)); - - checkCudaErrors(cudaMalloc(&info, sizeof(int))); - checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize)); - checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n)); - checkCudaErrors(cudaMalloc(&ipiv, sizeof(int) * n)); - - // prepare a copy of A because getrf will overwrite A with L - checkCudaErrors(cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice)); - checkCudaErrors(cudaMemset(info, 0, sizeof(int))); - - start = second(); - start = second(); - - checkCudaErrors(cusolverDnDgetrf(handle, n, n, A, lda, buffer, ipiv, info)); - checkCudaErrors(cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost)); - - if (0 != h_info) { - fprintf(stderr, "Error: LU factorization failed\n"); - } - - checkCudaErrors(cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice)); - checkCudaErrors(cusolverDnDgetrs(handle, CUBLAS_OP_N, n, 1, A, lda, ipiv, x, n, info)); - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - - time_solve = stop - start; - fprintf(stdout, "timing: LU = %10.6f sec\n", time_solve); - - if (info) { - checkCudaErrors(cudaFree(info)); - } - if (buffer) { - checkCudaErrors(cudaFree(buffer)); - } - if (A) { - checkCudaErrors(cudaFree(A)); - } - if (ipiv) { - checkCudaErrors(cudaFree(ipiv)); - } - - return 0; -} - -/* - * solve A*x = b by QR - * - */ -int linearSolverQR(cusolverDnHandle_t handle, int n, const double *Acopy, int lda, const double *b, double *x) -{ - cublasHandle_t cublasHandle = NULL; // used in residual evaluation - int bufferSize = 0; - int bufferSize_geqrf = 0; - int bufferSize_ormqr = 0; - int *info = NULL; - double *buffer = NULL; - double *A = NULL; - double *tau = NULL; - int h_info = 0; - double start, stop; - double time_solve; - const double one = 1.0; - - checkCudaErrors(cublasCreate(&cublasHandle)); - - checkCudaErrors(cusolverDnDgeqrf_bufferSize(handle, n, n, (double *)Acopy, lda, &bufferSize_geqrf)); - checkCudaErrors(cusolverDnDormqr_bufferSize( - handle, CUBLAS_SIDE_LEFT, CUBLAS_OP_T, n, 1, n, A, lda, NULL, x, n, &bufferSize_ormqr)); - - printf("buffer_geqrf = %d, buffer_ormqr = %d \n", bufferSize_geqrf, bufferSize_ormqr); - - bufferSize = (bufferSize_geqrf > bufferSize_ormqr) ? bufferSize_geqrf : bufferSize_ormqr; - - checkCudaErrors(cudaMalloc(&info, sizeof(int))); - checkCudaErrors(cudaMalloc(&buffer, sizeof(double) * bufferSize)); - checkCudaErrors(cudaMalloc(&A, sizeof(double) * lda * n)); - checkCudaErrors(cudaMalloc((void **)&tau, sizeof(double) * n)); - - // prepare a copy of A because getrf will overwrite A with L - checkCudaErrors(cudaMemcpy(A, Acopy, sizeof(double) * lda * n, cudaMemcpyDeviceToDevice)); - - checkCudaErrors(cudaMemset(info, 0, sizeof(int))); - - start = second(); - start = second(); - - // compute QR factorization - checkCudaErrors(cusolverDnDgeqrf(handle, n, n, A, lda, tau, buffer, bufferSize, info)); - - checkCudaErrors(cudaMemcpy(&h_info, info, sizeof(int), cudaMemcpyDeviceToHost)); - - if (0 != h_info) { - fprintf(stderr, "Error: QR factorization failed\n"); - } - - checkCudaErrors(cudaMemcpy(x, b, sizeof(double) * n, cudaMemcpyDeviceToDevice)); - - // compute Q^T*b - checkCudaErrors( - cusolverDnDormqr(handle, CUBLAS_SIDE_LEFT, CUBLAS_OP_T, n, 1, n, A, lda, tau, x, n, buffer, bufferSize, info)); - - // x = R \ Q^T*b - checkCudaErrors(cublasDtrsm(cublasHandle, - CUBLAS_SIDE_LEFT, - CUBLAS_FILL_MODE_UPPER, - CUBLAS_OP_N, - CUBLAS_DIAG_NON_UNIT, - n, - 1, - &one, - A, - lda, - x, - n)); - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - - time_solve = stop - start; - fprintf(stdout, "timing: QR = %10.6f sec\n", time_solve); - - if (cublasHandle) { - checkCudaErrors(cublasDestroy(cublasHandle)); - } - if (info) { - checkCudaErrors(cudaFree(info)); - } - if (buffer) { - checkCudaErrors(cudaFree(buffer)); - } - if (A) { - checkCudaErrors(cudaFree(A)); - } - if (tau) { - checkCudaErrors(cudaFree(tau)); - } - - return 0; -} - -void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) -{ - memset(&opts, 0, sizeof(opts)); - - if (checkCmdLineFlag(argc, (const char **)argv, "-h")) { - UsageDN(); - } - - if (checkCmdLineFlag(argc, (const char **)argv, "R")) { - char *solverType = NULL; - getCmdLineArgumentString(argc, (const char **)argv, "R", &solverType); - - if (solverType) { - if ((STRCASECMP(solverType, "chol") != 0) && (STRCASECMP(solverType, "lu") != 0) - && (STRCASECMP(solverType, "qr") != 0)) { - printf("\nIncorrect argument passed to -R option\n"); - UsageDN(); - } - else { - opts.testFunc = solverType; - } - } - } - - if (checkCmdLineFlag(argc, (const char **)argv, "file")) { - char *fileName = 0; - getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName); - - if (fileName) { - opts.sparse_mat_filename = fileName; - } - else { - printf("\nIncorrect filename passed to -file \n "); - UsageDN(); - } - } - - if (checkCmdLineFlag(argc, (const char **)argv, "lda")) { - opts.lda = getCmdLineArgumentInt(argc, (const char **)argv, "lda"); - } -} - -int main(int argc, char *argv[]) -{ - struct testOpts opts; - cusolverDnHandle_t handle = NULL; - cublasHandle_t cublasHandle = NULL; // used in residual evaluation - cudaStream_t stream = NULL; - - int rowsA = 0; // number of rows of A - int colsA = 0; // number of columns of A - int nnzA = 0; // number of nonzeros of A - int baseA = 0; // base index in CSR format - int lda = 0; // leading dimension in dense matrix - - // CSR(A) from I/O - int *h_csrRowPtrA = NULL; - int *h_csrColIndA = NULL; - double *h_csrValA = NULL; - - double *h_A = NULL; // dense matrix from CSR(A) - double *h_x = NULL; // a copy of d_x - double *h_b = NULL; // b = ones(m,1) - double *h_r = NULL; // r = b - A*x, a copy of d_r - - double *d_A = NULL; // a copy of h_A - double *d_x = NULL; // x = A \ b - double *d_b = NULL; // a copy of h_b - double *d_r = NULL; // r = b - A*x - - // the constants are used in residual evaluation, r = b - A*x - const double minus_one = -1.0; - const double one = 1.0; - - double x_inf = 0.0; - double r_inf = 0.0; - double A_inf = 0.0; - int errors = 0; - - parseCommandLineArguments(argc, argv, opts); - - if (NULL == opts.testFunc) { - opts.testFunc = "chol"; // By default running Cholesky as NO solver - // selected with -R option. - } - - findCudaDevice(argc, (const char **)argv); - - printf("step 1: read matrix market format\n"); - - if (opts.sparse_mat_filename == NULL) { - opts.sparse_mat_filename = sdkFindFilePath("gr_900_900_crg.mtx", argv[0]); - if (opts.sparse_mat_filename != NULL) - printf("Using default input file [%s]\n", opts.sparse_mat_filename); - else - printf("Could not find gr_900_900_crg.mtx\n"); - } - else { - printf("Using input file [%s]\n", opts.sparse_mat_filename); - } - - if (opts.sparse_mat_filename == NULL) { - fprintf(stderr, "Error: input matrix is not provided\n"); - return EXIT_FAILURE; - } - - if (loadMMSparseMatrix(opts.sparse_mat_filename, - 'd', - true, - &rowsA, - &colsA, - &nnzA, - &h_csrValA, - &h_csrRowPtrA, - &h_csrColIndA, - true)) { - exit(EXIT_FAILURE); - } - baseA = h_csrRowPtrA[0]; // baseA = {0,1} - - printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA); - - if (rowsA != colsA) { - fprintf(stderr, "Error: only support square matrix\n"); - exit(EXIT_FAILURE); - } - - printf("step 2: convert CSR(A) to dense matrix\n"); - - lda = opts.lda ? opts.lda : rowsA; - if (lda < rowsA) { - fprintf(stderr, "Error: lda must be greater or equal to dimension of A\n"); - exit(EXIT_FAILURE); - } - - h_A = (double *)malloc(sizeof(double) * lda * colsA); - h_x = (double *)malloc(sizeof(double) * colsA); - h_b = (double *)malloc(sizeof(double) * rowsA); - h_r = (double *)malloc(sizeof(double) * rowsA); - assert(NULL != h_A); - assert(NULL != h_x); - assert(NULL != h_b); - assert(NULL != h_r); - - memset(h_A, 0, sizeof(double) * lda * colsA); - - for (int row = 0; row < rowsA; row++) { - const int start = h_csrRowPtrA[row] - baseA; - const int end = h_csrRowPtrA[row + 1] - baseA; - for (int colidx = start; colidx < end; colidx++) { - const int col = h_csrColIndA[colidx] - baseA; - const double Areg = h_csrValA[colidx]; - h_A[row + col * lda] = Areg; - } - } - - printf("step 3: set right hand side vector (b) to 1\n"); - for (int row = 0; row < rowsA; row++) { - h_b[row] = 1.0; - } - - // verify if A is symmetric or not. - if (0 == strcmp(opts.testFunc, "chol")) { - int issym = 1; - for (int j = 0; j < colsA; j++) { - for (int i = j; i < rowsA; i++) { - double Aij = h_A[i + j * lda]; - double Aji = h_A[j + i * lda]; - if (Aij != Aji) { - issym = 0; - break; - } - } - } - if (!issym) { - printf("Error: A has no symmetric pattern, please use LU or QR \n"); - exit(EXIT_FAILURE); - } - } - - checkCudaErrors(cusolverDnCreate(&handle)); - checkCudaErrors(cublasCreate(&cublasHandle)); - checkCudaErrors(cudaStreamCreate(&stream)); - - checkCudaErrors(cusolverDnSetStream(handle, stream)); - checkCudaErrors(cublasSetStream(cublasHandle, stream)); - - checkCudaErrors(cudaMalloc((void **)&d_A, sizeof(double) * lda * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA)); - - printf("step 4: prepare data on device\n"); - checkCudaErrors(cudaMemcpy(d_A, h_A, sizeof(double) * lda * colsA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_b, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - printf("step 5: solve A*x = b \n"); - // d_A and d_b are read-only - if (0 == strcmp(opts.testFunc, "chol")) { - linearSolverCHOL(handle, rowsA, d_A, lda, d_b, d_x); - } - else if (0 == strcmp(opts.testFunc, "lu")) { - linearSolverLU(handle, rowsA, d_A, lda, d_b, d_x); - } - else if (0 == strcmp(opts.testFunc, "qr")) { - linearSolverQR(handle, rowsA, d_A, lda, d_b, d_x); - } - else { - fprintf(stderr, "Error: %s is unknown function\n", opts.testFunc); - exit(EXIT_FAILURE); - } - printf("step 6: evaluate residual\n"); - checkCudaErrors(cudaMemcpy(d_r, d_b, sizeof(double) * rowsA, cudaMemcpyDeviceToDevice)); - - // r = b - A*x - checkCudaErrors(cublasDgemm_v2( - cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, rowsA, 1, colsA, &minus_one, d_A, lda, d_x, rowsA, &one, d_r, rowsA)); - - checkCudaErrors(cudaMemcpy(h_x, d_x, sizeof(double) * colsA, cudaMemcpyDeviceToHost)); - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - A_inf = mat_norminf(rowsA, colsA, h_A, lda); - - printf("|b - A*x| = %E \n", r_inf); - printf("|A| = %E \n", A_inf); - printf("|x| = %E \n", x_inf); - printf("|b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - if (handle) { - checkCudaErrors(cusolverDnDestroy(handle)); - } - if (cublasHandle) { - checkCudaErrors(cublasDestroy(cublasHandle)); - } - if (stream) { - checkCudaErrors(cudaStreamDestroy(stream)); - } - - if (h_csrValA) { - free(h_csrValA); - } - if (h_csrRowPtrA) { - free(h_csrRowPtrA); - } - if (h_csrColIndA) { - free(h_csrColIndA); - } - - if (h_A) { - free(h_A); - } - if (h_x) { - free(h_x); - } - if (h_b) { - free(h_b); - } - if (h_r) { - free(h_r); - } - - if (d_A) { - checkCudaErrors(cudaFree(d_A)); - } - if (d_x) { - checkCudaErrors(cudaFree(d_x)); - } - if (d_b) { - checkCudaErrors(cudaFree(d_b)); - } - if (d_r) { - checkCudaErrors(cudaFree(d_r)); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/gr_900_900_crg.mtx b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/gr_900_900_crg.mtx deleted file mode 100644 index cabd2573..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/gr_900_900_crg.mtx +++ /dev/null @@ -1,4330 +0,0 @@ -%%MatrixMarket matrix coordinate real symmetric -% -% GR_30_30 -% Finite difference laplacian, from the -% LAPLACIANS group of the Harwell-Boeing Sparse Matrix Collection. -% -900 900 4322 -1 1 8.0000000000000e+00 -2 1 -1.0000000000000e+00 -31 1 -1.0000000000000e+00 -32 1 -1.0000000000000e+00 -2 2 8.0000000000000e+00 -3 2 -1.0000000000000e+00 -31 2 -1.0000000000000e+00 -32 2 -1.0000000000000e+00 -33 2 -1.0000000000000e+00 -3 3 8.0000000000000e+00 -4 3 -1.0000000000000e+00 -32 3 -1.0000000000000e+00 -33 3 -1.0000000000000e+00 -34 3 -1.0000000000000e+00 -4 4 8.0000000000000e+00 -5 4 -1.0000000000000e+00 -33 4 -1.0000000000000e+00 -34 4 -1.0000000000000e+00 -35 4 -1.0000000000000e+00 -5 5 8.0000000000000e+00 -6 5 -1.0000000000000e+00 -34 5 -1.0000000000000e+00 -35 5 -1.0000000000000e+00 -36 5 -1.0000000000000e+00 -6 6 8.0000000000000e+00 -7 6 -1.0000000000000e+00 -35 6 -1.0000000000000e+00 -36 6 -1.0000000000000e+00 -37 6 -1.0000000000000e+00 -7 7 8.0000000000000e+00 -8 7 -1.0000000000000e+00 -36 7 -1.0000000000000e+00 -37 7 -1.0000000000000e+00 -38 7 -1.0000000000000e+00 -8 8 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10852428..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio.c +++ /dev/null @@ -1,481 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -// System includes -#include -#include -#include -#include - -// Project includes -#include "mmio.h" - - -int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_) -{ - FILE *f; - MM_typecode matcode; - int M, N, nz; - int i; - double *val; - int *I, *J; - - if ((f = fopen(fname, "r")) == NULL) - return -1; - - - if (mm_read_banner(f, &matcode) != 0) { - printf("mm_read_unsymetric: Could not process Matrix Market banner "); - printf(" in file [%s]\n", fname); - return -1; - } - - - if (!(mm_is_real(matcode) && mm_is_matrix(matcode) && mm_is_sparse(matcode))) { - fprintf(stderr, "Sorry, this application does not support "); - fprintf(stderr, "Market Market type: [%s]\n", mm_typecode_to_str(matcode)); - return -1; - } - - /* find out size of sparse matrix: M, N, nz .... */ - - if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) { - fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n"); - return -1; - } - - *M_ = M; - *N_ = N; - *nz_ = nz; - - /* reseve memory for matrices */ - - I = (int *)malloc(nz * sizeof(int)); - J = (int *)malloc(nz * sizeof(int)); - val = (double *)malloc(nz * sizeof(double)); - - *val_ = val; - *I_ = I; - *J_ = J; - - /* NOTE: when reading in doubles, ANSI C requires the use of the "l" */ - /* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */ - /* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */ - - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) { - return -1; - } - I[i]--; /* adjust from 1-based to 0-based */ - J[i]--; - } - fclose(f); - - return 0; -} - -int mm_is_valid(MM_typecode matcode) -{ - if (!mm_is_matrix(matcode)) - return 0; - if (mm_is_dense(matcode) && mm_is_pattern(matcode)) - return 0; - if (mm_is_real(matcode) && mm_is_hermitian(matcode)) - return 0; - if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) || mm_is_skew(matcode))) - return 0; - return 1; -} - -int mm_read_banner(FILE *f, MM_typecode *matcode) -{ - char line[MM_MAX_LINE_LENGTH]; - char banner[MM_MAX_TOKEN_LENGTH]; - char mtx[MM_MAX_TOKEN_LENGTH]; - char crd[MM_MAX_TOKEN_LENGTH]; - char data_type[MM_MAX_TOKEN_LENGTH]; - char storage_scheme[MM_MAX_TOKEN_LENGTH]; - char *p; - - - mm_clear_typecode(matcode); - - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - - if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type, storage_scheme) != 5) - return MM_PREMATURE_EOF; - - for (p = mtx; *p != '\0'; *p = tolower(*p), p++) - ; /* convert to lower case */ - for (p = crd; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = data_type; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++) - ; - - /* check for banner */ - if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0) - return MM_NO_HEADER; - - /* first field should be "mtx" */ - if (strcmp(mtx, MM_MTX_STR) != 0) - return MM_UNSUPPORTED_TYPE; - mm_set_matrix(matcode); - - - /* second field describes whether this is a sparse matrix (in coordinate - storgae) or a dense array */ - - - if (strcmp(crd, MM_SPARSE_STR) == 0) - mm_set_sparse(matcode); - else if (strcmp(crd, MM_DENSE_STR) == 0) - mm_set_dense(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* third field */ - - if (strcmp(data_type, MM_REAL_STR) == 0) - mm_set_real(matcode); - else if (strcmp(data_type, MM_COMPLEX_STR) == 0) - mm_set_complex(matcode); - else if (strcmp(data_type, MM_PATTERN_STR) == 0) - mm_set_pattern(matcode); - else if (strcmp(data_type, MM_INT_STR) == 0) - mm_set_integer(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* fourth field */ - - if (strcmp(storage_scheme, MM_GENERAL_STR) == 0) - mm_set_general(matcode); - else if (strcmp(storage_scheme, MM_SYMM_STR) == 0) - mm_set_symmetric(matcode); - else if (strcmp(storage_scheme, MM_HERM_STR) == 0) - mm_set_hermitian(matcode); - else if (strcmp(storage_scheme, MM_SKEW_STR) == 0) - mm_set_skew(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - return 0; -} - -int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) -{ - if (fprintf(f, "%d %d %d\n", M, N, nz) != 3) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - - /* set return null parameter values, in case we exit with errors */ - *M = *N = *nz = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d %d", M, N, nz) == 3) - return 0; - - else - do { - num_items_read = fscanf(f, "%d %d %d", M, N, nz); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 3); - - return 0; -} - - -int mm_read_mtx_array_size(FILE *f, int *M, int *N) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - /* set return null parameter values, in case we exit with errors */ - *M = *N = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d", M, N) == 2) - return 0; - - else /* we have a blank line */ - do { - num_items_read = fscanf(f, "%d %d", M, N); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 2); - - return 0; -} - -int mm_write_mtx_array_size(FILE *f, int M, int N) -{ - if (fprintf(f, "%d %d\n", M, N) != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - - -/*-------------------------------------------------------------------------*/ - -/******************************************************************/ -/* use when I[], J[], and val[]J, and val[] are already allocated */ -/******************************************************************/ - -int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - int i; - if (mm_is_complex(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i], &val[2 * i + 1]) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) - return MM_PREMATURE_EOF; - } - } - - else if (mm_is_pattern(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag, MM_typecode matcode) -{ - if (mm_is_complex(matcode)) { - if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) - return MM_PREMATURE_EOF; - } - - else if (mm_is_pattern(matcode)) { - if (fscanf(f, "%d %d", I, J) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - - -/************************************************************************ - mm_read_mtx_crd() fills M, N, nz, array of values, and return - type code, e.g. 'MCRS' - - if matrix is complex, values[] is of size 2*nz, - (nz pairs of real/imaginary values) -************************************************************************/ - -int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode) -{ - int ret_code; - FILE *f; - - if (strcmp(fname, "stdin") == 0) - f = stdin; - else if ((f = fopen(fname, "r")) == NULL) - return MM_COULD_NOT_READ_FILE; - - - if ((ret_code = mm_read_banner(f, matcode)) != 0) - return ret_code; - - if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) && mm_is_matrix(*matcode))) - return MM_UNSUPPORTED_TYPE; - - if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) - return ret_code; - - - *I = (int *)malloc(*nz * sizeof(int)); - *J = (int *)malloc(*nz * sizeof(int)); - *val = NULL; - - if (mm_is_complex(*matcode)) { - *val = (double *)malloc(*nz * 2 * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - else if (mm_is_real(*matcode) || mm_is_integer(*matcode)) { - *val = (double *)malloc(*nz * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - else if (mm_is_pattern(*matcode)) { - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - if (f != stdin) - fclose(f); - return 0; -} - -int mm_write_banner(FILE *f, MM_typecode matcode) -{ - char *str = mm_typecode_to_str(matcode); - int ret_code; - - ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str); - free(str); - if (ret_code != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - FILE *f; - int i; - - if (strcmp(fname, "stdout") == 0) - f = stdout; - else if ((f = fopen(fname, "w")) == NULL) - return MM_COULD_NOT_WRITE_FILE; - - /* print banner followed by typecode */ - fprintf(f, "%s ", MatrixMarketBanner); - fprintf(f, "%s\n", mm_typecode_to_str(matcode)); - - /* print matrix sizes and nonzeros */ - fprintf(f, "%d %d %d\n", M, N, nz); - - /* print values */ - if (mm_is_pattern(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d\n", I[i], J[i]); - else if (mm_is_integer(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %d\n", I[i], J[i], (int)val[i]); - else if (mm_is_real(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]); - else if (mm_is_complex(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i], val[2 * i + 1]); - else { - if (f != stdout) - fclose(f); - return MM_UNSUPPORTED_TYPE; - } - - if (f != stdout) - fclose(f); - - return 0; -} - - -/** - * Create a new copy of a string s. mm_strdup() is a common routine, but - * not part of ANSI C, so it is included here. Used by mm_typecode_to_str(). - * - */ -static char *mm_strdup(const char *s) -{ - size_t len = strlen(s); - char *s2 = (char *)malloc((len + 1) * sizeof(char)); - return strcpy(s2, s); -} - -char *mm_typecode_to_str(MM_typecode matcode) -{ - char buffer[MM_MAX_LINE_LENGTH]; - char *types[4]; - // char *mm_strdup(const char *); - // int error =0; - - /* check for MTX type */ - if (mm_is_matrix(matcode)) - types[0] = MM_MTX_STR; - else - return NULL; // error=1; - - /* check for CRD or ARR matrix */ - if (mm_is_sparse(matcode)) - types[1] = MM_SPARSE_STR; - else if (mm_is_dense(matcode)) - types[1] = MM_DENSE_STR; - else - return NULL; - - /* check for element data type */ - if (mm_is_real(matcode)) - types[2] = MM_REAL_STR; - else if (mm_is_complex(matcode)) - types[2] = MM_COMPLEX_STR; - else if (mm_is_pattern(matcode)) - types[2] = MM_PATTERN_STR; - else if (mm_is_integer(matcode)) - types[2] = MM_INT_STR; - else - return NULL; - - - /* check for symmetry type */ - if (mm_is_general(matcode)) - types[3] = MM_GENERAL_STR; - else if (mm_is_symmetric(matcode)) - types[3] = MM_SYMM_STR; - else if (mm_is_hermitian(matcode)) - types[3] = MM_HERM_STR; - else if (mm_is_skew(matcode)) - types[3] = MM_SKEW_STR; - else - return NULL; - - sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]); - return mm_strdup(buffer); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio.h b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio.h deleted file mode 100644 index a05f6063..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio.h +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -#ifndef MM_IO_H -#define MM_IO_H - -#include - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - -#define MM_MAX_LINE_LENGTH 1025 -#define MatrixMarketBanner "%%MatrixMarket" -#define MM_MAX_TOKEN_LENGTH 64 - - typedef char MM_typecode[4]; - - char *mm_typecode_to_str(MM_typecode matcode); - - int mm_read_banner(FILE *f, MM_typecode *matcode); - int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz); - int mm_read_mtx_array_size(FILE *f, int *M, int *N); - - int mm_write_banner(FILE *f, MM_typecode matcode); - int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz); - int mm_write_mtx_array_size(FILE *f, int M, int N); - - - /********************* MM_typecode query fucntions ***************************/ - -#define mm_is_matrix(typecode) ((typecode)[0] == 'M') - -#define mm_is_sparse(typecode) ((typecode)[1] == 'C') -#define mm_is_coordinate(typecode) ((typecode)[1] == 'C') -#define mm_is_dense(typecode) ((typecode)[1] == 'A') -#define mm_is_array(typecode) ((typecode)[1] == 'A') - -#define mm_is_complex(typecode) ((typecode)[2] == 'C') -#define mm_is_real(typecode) ((typecode)[2] == 'R') -#define mm_is_pattern(typecode) ((typecode)[2] == 'P') -#define mm_is_integer(typecode) ((typecode)[2] == 'I') - -#define mm_is_symmetric(typecode) ((typecode)[3] == 'S') -#define mm_is_general(typecode) ((typecode)[3] == 'G') -#define mm_is_skew(typecode) ((typecode)[3] == 'K') -#define mm_is_hermitian(typecode) ((typecode)[3] == 'H') - - int mm_is_valid(MM_typecode matcode); /* too complex for a macro */ - - - /********************* MM_typecode modify fucntions ***************************/ - -#define mm_set_matrix(typecode) ((*typecode)[0] = 'M') -#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C') -#define mm_set_array(typecode) ((*typecode)[1] = 'A') -#define mm_set_dense(typecode) mm_set_array(typecode) -#define mm_set_sparse(typecode) mm_set_coordinate(typecode) - -#define mm_set_complex(typecode) ((*typecode)[2] = 'C') -#define mm_set_real(typecode) ((*typecode)[2] = 'R') -#define mm_set_pattern(typecode) ((*typecode)[2] = 'P') -#define mm_set_integer(typecode) ((*typecode)[2] = 'I') - - -#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S') -#define mm_set_general(typecode) ((*typecode)[3] = 'G') -#define mm_set_skew(typecode) ((*typecode)[3] = 'K') -#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H') - -#define mm_clear_typecode(typecode) ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', (*typecode)[3] = 'G') - -#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode) - - - /********************* Matrix Market error codes ***************************/ - - -#define MM_COULD_NOT_READ_FILE 11 -#define MM_PREMATURE_EOF 12 -#define MM_NOT_MTX 13 -#define MM_NO_HEADER 14 -#define MM_UNSUPPORTED_TYPE 15 -#define MM_LINE_TOO_LONG 16 -#define MM_COULD_NOT_WRITE_FILE 17 - - - /******************** Matrix Market internal definitions ******************** - - MM_matrix_typecode: 4-character sequence - - ojbect sparse/ data storage - dense type scheme - - string position: [0] [1] [2] [3] - - Matrix typecode: M(atrix) C(oord) R(eal) G(eneral) - A(array) C(omplex) H(ermitian) - P(attern) S(ymmetric) - I(nteger) K(kew) - - ***********************************************************************/ - -#define MM_MTX_STR "matrix" -#define MM_ARRAY_STR "array" -#define MM_DENSE_STR "array" -#define MM_COORDINATE_STR "coordinate" -#define MM_SPARSE_STR "coordinate" -#define MM_COMPLEX_STR "complex" -#define MM_REAL_STR "real" -#define MM_INT_STR "integer" -#define MM_GENERAL_STR "general" -#define MM_SYMM_STR "symmetric" -#define MM_HERM_STR "hermitian" -#define MM_SKEW_STR "skew-symmetric" -#define MM_PATTERN_STR "pattern" - - - /* high level routines */ - int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode); - - int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img, MM_typecode matcode); - - int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif diff --git a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio_wrapper.cpp b/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio_wrapper.cpp deleted file mode 100644 index f6f6c8e4..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverDn_LinearSolver/mmio_wrapper.cpp +++ /dev/null @@ -1,473 +0,0 @@ - -#include -#include -#include -#include - -#include "mmio.h" - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -/* various __inline__ __device__ function to initialize a T_ELEM */ -template __inline__ T_ELEM cuGet(int); -template <> __inline__ float cuGet(int x) { return float(x); } - -template <> __inline__ double cuGet(int x) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(int x) { return (make_cuDoubleComplex(double(x), 0.0)); } - - -template __inline__ T_ELEM cuGet(int, int); -template <> __inline__ float cuGet(int x, int y) { return float(x); } - -template <> __inline__ double cuGet(int x, int y) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x, int y) { return make_cuComplex(float(x), float(y)); } - -template <> __inline__ cuDoubleComplex cuGet(int x, int y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(float); -template <> __inline__ float cuGet(float x) { return float(x); } - -template <> __inline__ double cuGet(float x) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(float x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(float, float); -template <> __inline__ float cuGet(float x, float y) { return float(x); } - -template <> __inline__ double cuGet(float x, float y) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x, float y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(float x, float y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(double); -template <> __inline__ float cuGet(double x) { return float(x); } - -template <> __inline__ double cuGet(double x) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(double x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(double, double); -template <> __inline__ float cuGet(double x, double y) { return float(x); } - -template <> __inline__ double cuGet(double x, double y) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x, double y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(double x, double y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -static void compress_index(const int *Ind, int nnz, int m, int *Ptr, int base) -{ - int i; - - /* initialize everything to zero */ - for (i = 0; i < m + 1; i++) { - Ptr[i] = 0; - } - /* count elements in every row */ - Ptr[0] = base; - for (i = 0; i < nnz; i++) { - Ptr[Ind[i] + (1 - base)]++; - } - /* add all the values */ - for (i = 0; i < m; i++) { - Ptr[i + 1] += Ptr[i]; - } -} - - -struct cooFormat -{ - int i; - int j; - int p; // permutation -}; - - -int cmp_cooFormat_csr(struct cooFormat *s, struct cooFormat *t) -{ - if (s->i < t->i) { - return -1; - } - else if (s->i > t->i) { - return 1; - } - else { - return s->j - t->j; - } -} - -int cmp_cooFormat_csc(struct cooFormat *s, struct cooFormat *t) -{ - if (s->j < t->j) { - return -1; - } - else if (s->j > t->j) { - return 1; - } - else { - return s->i - t->i; - } -} - -typedef int (*FUNPTR)(const void *, const void *); -typedef int (*FUNPTR2)(struct cooFormat *s, struct cooFormat *t); - -static FUNPTR2 fptr_array[2] = { - cmp_cooFormat_csr, - cmp_cooFormat_csc, -}; - - -static int verify_pattern(int m, int nnz, int *csrRowPtr, int *csrColInd) -{ - int i, col, start, end, base_index; - int error_found = 0; - - if (nnz != (csrRowPtr[m] - csrRowPtr[0])) { - fprintf(stderr, - "Error (nnz check failed): (csrRowPtr[%d]=%d - csrRowPtr[%d]=%d) != (nnz=%d)\n", - 0, - csrRowPtr[0], - m, - csrRowPtr[m], - nnz); - error_found = 1; - } - - base_index = csrRowPtr[0]; - if ((0 != base_index) && (1 != base_index)) { - fprintf(stderr, "Error (base index check failed): base index = %d\n", base_index); - error_found = 1; - } - - for (i = 0; (!error_found) && (i < m); i++) { - start = csrRowPtr[i] - base_index; - end = csrRowPtr[i + 1] - base_index; - if (start > end) { - fprintf(stderr, - "Error (corrupted row): csrRowPtr[%d] (=%d) > csrRowPtr[%d] (=%d)\n", - i, - start + base_index, - i + 1, - end + base_index); - error_found = 1; - } - for (col = start; col < end; col++) { - if (csrColInd[col] < base_index) { - fprintf(stderr, "Error (column vs. base index check failed): csrColInd[%d] < %d\n", col, base_index); - error_found = 1; - } - if ((col < (end - 1)) && (csrColInd[col] >= csrColInd[col + 1])) { - fprintf( - stderr, - "Error (sorting of the column indecis check failed): (csrColInd[%d]=%d) >= (csrColInd[%d]=%d)\n", - col, - csrColInd[col], - col + 1, - csrColInd[col + 1]); - error_found = 1; - } - } - } - return error_found; -} - - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix) -{ - MM_typecode matcode; - double *tempVal; - int *tempRowInd, *tempColInd; - double *tval; - int *trow, *tcol; - int *csrRowPtr, *cscColPtr; - int i, j, error, base, count; - struct cooFormat *work; - - /* read the matrix */ - error = mm_read_mtx_crd(filename, m, n, nnz, &trow, &tcol, &tval, &matcode); - if (error) { - fprintf(stderr, "!!!! can not open file: '%s'\n", filename); - return 1; - } - - /* start error checking */ - if (mm_is_complex(matcode) && ((elem_type != 'z') && (elem_type != 'c'))) { - fprintf(stderr, "!!!! complex matrix requires type 'z' or 'c'\n"); - return 1; - } - - if (mm_is_dense(matcode) || mm_is_array(matcode) || mm_is_pattern(matcode) /*|| mm_is_integer(matcode)*/) { - fprintf(stderr, "!!!! dense, array, pattern and integer matrices are not supported\n"); - return 1; - } - - /* if necessary symmetrize the pattern (transform from triangular to full) */ - if ((extendSymMatrix) && (mm_is_symmetric(matcode) || mm_is_hermitian(matcode) || mm_is_skew(matcode))) { - // count number of non-diagonal elements - count = 0; - for (i = 0; i < (*nnz); i++) { - if (trow[i] != tcol[i]) { - count++; - } - } - // allocate space for the symmetrized matrix - tempRowInd = (int *)malloc((*nnz + count) * sizeof(int)); - tempColInd = (int *)malloc((*nnz + count) * sizeof(int)); - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal = (double *)malloc((*nnz + count) * sizeof(double)); - } - else { - tempVal = (double *)malloc(2 * (*nnz + count) * sizeof(double)); - } - // copy the elements regular and transposed locations - for (j = 0, i = 0; i < (*nnz); i++) { - tempRowInd[j] = trow[i]; - tempColInd[j] = tcol[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal[j] = tval[i]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - j++; - if (trow[i] != tcol[i]) { - tempRowInd[j] = tcol[i]; - tempColInd[j] = trow[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (mm_is_skew(matcode)) { - tempVal[j] = -tval[i]; - } - else { - tempVal[j] = tval[i]; - } - } - else { - if (mm_is_hermitian(matcode)) { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = -tval[2 * i + 1]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - } - j++; - } - } - (*nnz) += count; - // free temporary storage - free(trow); - free(tcol); - free(tval); - } - else { - tempRowInd = trow; - tempColInd = tcol; - tempVal = tval; - } - // life time of (trow, tcol, tval) is over. - // please use COO format (tempRowInd, tempColInd, tempVal) - - // use qsort to sort COO format - work = (struct cooFormat *)malloc(sizeof(struct cooFormat) * (*nnz)); - if (NULL == work) { - fprintf(stderr, "!!!! allocation error, malloc failed\n"); - return 1; - } - for (i = 0; i < (*nnz); i++) { - work[i].i = tempRowInd[i]; - work[i].j = tempColInd[i]; - work[i].p = i; // permutation is identity - } - - if (csrFormat) { - /* create row-major ordering of indices (sorted by row and within each row by column) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[0]); - } - else { - /* create column-major ordering of indices (sorted by column and within each column by row) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[1]); - } - - // (tempRowInd, tempColInd) is sorted either by row-major or by col-major - for (i = 0; i < (*nnz); i++) { - tempRowInd[i] = work[i].i; - tempColInd[i] = work[i].j; - } - - // setup base - // check if there is any row/col 0, if so base-0 - // check if there is any row/col equal to matrix dimension m/n, if so base-1 - int base0 = 0; - int base1 = 0; - for (i = 0; i < (*nnz); i++) { - const int row = tempRowInd[i]; - const int col = tempColInd[i]; - if ((0 == row) || (0 == col)) { - base0 = 1; - } - if ((*m == row) || (*n == col)) { - base1 = 1; - } - } - if (base0 && base1) { - printf("Error: input matrix is base-0 and base-1 \n"); - return 1; - } - - base = 0; - if (base1) { - base = 1; - } - - /* compress the appropriate indices */ - if (csrFormat) { - /* CSR format (assuming row-major format) */ - csrRowPtr = (int *)malloc(((*m) + 1) * sizeof(csrRowPtr[0])); - if (!csrRowPtr) - return 1; - compress_index(tempRowInd, *nnz, *m, csrRowPtr, base); - - *aRowInd = csrRowPtr; - *aColInd = (int *)malloc((*nnz) * sizeof(int)); - } - else { - /* CSC format (assuming column-major format) */ - cscColPtr = (int *)malloc(((*n) + 1) * sizeof(cscColPtr[0])); - if (!cscColPtr) - return 1; - compress_index(tempColInd, *nnz, *n, cscColPtr, base); - - *aColInd = cscColPtr; - *aRowInd = (int *)malloc((*nnz) * sizeof(int)); - } - - /* transfrom the matrix values of type double into one of the cusparse library types */ - *aVal = (T_ELEM *)malloc((*nnz) * sizeof(T_ELEM)); - - for (i = 0; i < (*nnz); i++) { - if (csrFormat) { - (*aColInd)[i] = tempColInd[i]; - } - else { - (*aRowInd)[i] = tempRowInd[i]; - } - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - (*aVal)[i] = cuGet(tempVal[work[i].p]); - } - else { - (*aVal)[i] = cuGet(tempVal[2 * work[i].p], tempVal[2 * work[i].p + 1]); - } - } - - /* check for corruption */ - int error_found; - if (csrFormat) { - error_found = verify_pattern(*m, *nnz, *aRowInd, *aColInd); - } - else { - error_found = verify_pattern(*n, *nnz, *aColInd, *aRowInd); - } - if (error_found) { - fprintf(stderr, "!!!! verify_pattern failed\n"); - return 1; - } - - /* cleanup and exit */ - free(work); - free(tempVal); - free(tempColInd); - free(tempRowInd); - - return 0; -} - - -/* specific instantiation */ -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - float **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - double **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuDoubleComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/CMakeLists.txt b/cpp/4_CUDA_Libraries/cuSolverRf/CMakeLists.txt deleted file mode 100644 index eed7f8bf..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/CMakeLists.txt +++ /dev/null @@ -1,58 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cuSolverRf LANGUAGES C CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for cuSolverRf -add_executable(cuSolverRf cuSolverRf.cpp mmio.c mmio_wrapper.cpp) - -target_compile_options(cuSolverRf PRIVATE $<$:--extended-lambda>) - -target_compile_features(cuSolverRf PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(cuSolverRf PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(cuSolverRf PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(cuSolverRf PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusolver -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverRf POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap2D_5pt_n100.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverRf POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap3D_7pt_n20.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/README.md b/cpp/4_CUDA_Libraries/cuSolverRf/README.md deleted file mode 100644 index aaa2cbe2..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# cuSolverRf - cuSolverRf Refactorization - -## Description - -A CUDA Sample that demonstrates cuSolver's refactorization library - CUSOLVERRF. - -## Key Concepts - -Linear Algebra, CUSOLVER Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuGet, cuDoubleComplex, cuComplex - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaStreamDestroy, cudaFree, cudaDeviceSynchronize, cudaMalloc, cudaStreamCreate - -## Dependencies needed to build/run -[CUSOLVER](../../../README.md#cusolver), [CUBLAS](../../../README.md#cublas), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/cuSolverRf.cpp b/cpp/4_CUDA_Libraries/cuSolverRf/cuSolverRf.cpp deleted file mode 100644 index 8794d231..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/cuSolverRf.cpp +++ /dev/null @@ -1,899 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * A framework of refactorization process. - * - * step 1: compute P*A*Q = L*U by - * - reordering and - * - LU with partial pivoting in cusolverSp - * - * step 2: set up cusolverRf by (P, Q, L, U) - * - * step 3: analyze and refactor A - * - * How to use - * ./cuSolverRf -P=symrcm -file - * ./cuSolverRf -P=symamd -file - * - */ - -#include "cusolverRf.h" - -#include -#include -#include -#include -#include -#include - -#include "cusolverSp.h" -#include "cusolverSp_LOWLEVEL_PREVIEW.h" -#include "helper_cuda.h" -#include "helper_cusolver.h" -#include "helper_string.h" - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -void UsageRF(void) -{ - printf("\n"); - printf("-h : display this help\n"); - printf("-P= : choose a reordering\n"); - printf(" symrcm (Reverse Cuthill-McKee)\n"); - printf(" symamd (Approximate Minimum Degree)\n"); - printf("-file= : filename containing a matrix in MM format\n"); - printf("-device= : if want to run on specific GPU\n"); - - exit(0); -} - -void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) -{ - memset(&opts, 0, sizeof(opts)); - - if (checkCmdLineFlag(argc, (const char **)argv, "-h")) { - UsageRF(); - } - - if (checkCmdLineFlag(argc, (const char **)argv, "P")) { - char *reorderType = NULL; - getCmdLineArgumentString(argc, (const char **)argv, "P", &reorderType); - - if (reorderType) { - if ((STRCASECMP(reorderType, "symrcm") != 0) && (STRCASECMP(reorderType, "symamd") != 0)) { - printf("\nIncorrect argument passed to -P option\n"); - UsageRF(); - } - else { - opts.reorder = reorderType; - } - } - } - - if (!opts.reorder) { - opts.reorder = "symrcm"; // Setting default reordering to be symrcm. - } - - if (checkCmdLineFlag(argc, (const char **)argv, "file")) { - char *fileName = 0; - getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName); - - if (fileName) { - opts.sparse_mat_filename = fileName; - } - else { - printf("\nIncorrect filename passed to -file \n "); - UsageRF(); - } - } -} - -int main(int argc, char *argv[]) -{ - struct testOpts opts; - cusolverRfHandle_t cusolverRfH = NULL; // refactorization - cusolverSpHandle_t cusolverSpH = NULL; // reordering, permutation and 1st LU factorization - cusparseHandle_t cusparseH = NULL; // residual evaluation - cudaStream_t stream = NULL; - cusparseMatDescr_t descrA = NULL; // A is a base-0 general matrix - - csrluInfoHost_t info = NULL; // opaque info structure for LU with parital pivoting - - int rowsA = 0; // number of rows of A - int colsA = 0; // number of columns of A - int nnzA = 0; // number of nonzeros of A - int baseA = 0; // base index in CSR format - // cusolverRf only works for base-0 - - // cusolverRf only works for square matrix, - // assume n = rowsA = colsA - - // CSR(A) from I/O - int *h_csrRowPtrA = NULL; // n+1 - int *h_csrColIndA = NULL; // nnzA - double *h_csrValA = NULL; // nnzA - - int *h_Qreorder = NULL; // n - // reorder to reduce zero fill-in - // Qreorder = symrcm(A) or Qreroder = symamd(A) - // B = Q*A*Q^T - int *h_csrRowPtrB = NULL; // n+1 - int *h_csrColIndB = NULL; // nnzA - double *h_csrValB = NULL; // nnzA - int *h_mapBfromA = NULL; // nnzA - - double *h_x = NULL; // n, x = A \ b - double *h_b = NULL; // n, b = ones(m,1) - double *h_r = NULL; // n, r = b - A*x - - // solve B*(Qx) = Q*b - double *h_xhat = NULL; // n, Q*x_hat = x - double *h_bhat = NULL; // n, b_hat = Q*b - - size_t size_perm = 0; - size_t size_internal = 0; - size_t size_lu = 0; // size of working space for csrlu - void *buffer_cpu = NULL; // working space for - // - permutation: B = Q*A*Q^T - // - LU with partial pivoting in cusolverSp - - // cusolverSp computes LU with partial pivoting - // Plu*B*Qlu^T = L*U - // where B = Q*A*Q^T - // - // nnzL and nnzU are not known until factorization is done. - // However upper bound of L+U is known after symbolic analysis of LU. - int *h_Plu = NULL; // n - int *h_Qlu = NULL; // n - - int nnzL = 0; - int *h_csrRowPtrL = NULL; // n+1 - int *h_csrColIndL = NULL; // nnzL - double *h_csrValL = NULL; // nnzL - - int nnzU = 0; - int *h_csrRowPtrU = NULL; // n+1 - int *h_csrColIndU = NULL; // nnzU - double *h_csrValU = NULL; // nnzU - - int *h_P = NULL; // n, P = Plu * Qreorder - int *h_Q = NULL; // n, Q = Qlu * Qreorder - - int *d_csrRowPtrA = NULL; // n+1 - int *d_csrColIndA = NULL; // nnzA - double *d_csrValA = NULL; // nnzA - double *d_x = NULL; // n, x = A \ b - double *d_b = NULL; // n, a copy of h_b - double *d_r = NULL; // n, r = b - A*x - - int *d_P = NULL; // n, P*A*Q^T = L*U - int *d_Q = NULL; // n - - double *d_T = NULL; // working space in cusolverRfSolve - // |d_T| = n * nrhs - - // the constants used in residual evaluation, r = b - A*x - const double minus_one = -1.0; - const double one = 1.0; - // the constants used in cusolverRf - // nzero is the value below which zero pivot is flagged. - // nboost is the value which is substitured for zero pivot. - double nzero = 0.0; - double nboost = 0.0; - // the constant used in cusolverSp - // singularity is -1 if A is invertible under tol - // tol determines the condition of singularity - // pivot_threshold decides pivoting strategy - int singularity = 0; - const double tol = 1.e-14; - const double pivot_threshold = 1.0; - // the constants used in cusolverRf - const cusolverRfFactorization_t fact_alg = CUSOLVERRF_FACTORIZATION_ALG0; // default - const cusolverRfTriangularSolve_t solve_alg = CUSOLVERRF_TRIANGULAR_SOLVE_ALG1; // default - - double x_inf = 0.0; // |x| - double r_inf = 0.0; // |r| - double A_inf = 0.0; // |A| - int errors = 0; - - double start, stop; - double time_reorder; - double time_perm; - double time_sp_analysis; - double time_sp_factor; - double time_sp_solve; - double time_sp_extract; - double time_rf_assemble; - double time_rf_reset; - double time_rf_refactor; - double time_rf_solve; - - parseCommandLineArguments(argc, argv, opts); - - printf("step 1.1: preparation\n"); - printf("step 1.1: read matrix market format\n"); - - findCudaDevice(argc, (const char **)argv); - - if (opts.sparse_mat_filename == NULL) { - opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n100.mtx", argv[0]); - printf("Using default input file [%s]\n", opts.sparse_mat_filename); - } - else { - printf("Using input file [%s]\n", opts.sparse_mat_filename); - } - - if (opts.sparse_mat_filename) { - if (loadMMSparseMatrix(opts.sparse_mat_filename, - 'd', - true, - &rowsA, - &colsA, - &nnzA, - &h_csrValA, - &h_csrRowPtrA, - &h_csrColIndA, - true)) { - return 1; - } - baseA = h_csrRowPtrA[0]; // baseA = {0,1} - } - - if (rowsA != colsA) { - fprintf(stderr, "Error: only support square matrix\n"); - return 1; - } - - printf("WARNING: cusolverRf only works for base-0 \n"); - if (baseA) { - for (int i = 0; i <= rowsA; i++) { - h_csrRowPtrA[i]--; - } - for (int i = 0; i < nnzA; i++) { - h_csrColIndA[i]--; - } - baseA = 0; - } - - printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA); - - checkCudaErrors(cusolverSpCreate(&cusolverSpH)); - checkCudaErrors(cusparseCreate(&cusparseH)); - checkCudaErrors(cudaStreamCreate(&stream)); - - checkCudaErrors(cusolverSpSetStream(cusolverSpH, stream)); - checkCudaErrors(cusparseSetStream(cusparseH, stream)); - - checkCudaErrors(cusparseCreateMatDescr(&descrA)); - checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL)); - - if (baseA) { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE)); - } - else { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO)); - } - - h_Qreorder = (int *)malloc(sizeof(int) * colsA); - - h_csrRowPtrB = (int *)malloc(sizeof(int) * (rowsA + 1)); - h_csrColIndB = (int *)malloc(sizeof(int) * nnzA); - h_csrValB = (double *)malloc(sizeof(double) * nnzA); - h_mapBfromA = (int *)malloc(sizeof(int) * nnzA); - - h_x = (double *)malloc(sizeof(double) * colsA); - h_b = (double *)malloc(sizeof(double) * rowsA); - h_r = (double *)malloc(sizeof(double) * rowsA); - h_xhat = (double *)malloc(sizeof(double) * colsA); - h_bhat = (double *)malloc(sizeof(double) * rowsA); - - assert(NULL != h_Qreorder); - - assert(NULL != h_csrRowPtrB); - assert(NULL != h_csrColIndB); - assert(NULL != h_csrValB); - assert(NULL != h_mapBfromA); - - assert(NULL != h_x); - assert(NULL != h_b); - assert(NULL != h_r); - assert(NULL != h_xhat); - assert(NULL != h_bhat); - - checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int) * (rowsA + 1))); - checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrValA, sizeof(double) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_P, sizeof(int) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_Q, sizeof(int) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_T, sizeof(double) * rowsA * 1)); - - printf("step 1.2: set right hand side vector (b) to 1\n"); - for (int row = 0; row < rowsA; row++) { - h_b[row] = 1.0; - } - - printf("step 2: reorder the matrix to reduce zero fill-in\n"); - printf(" Q = symrcm(A) or Q = symamd(A) \n"); - start = second(); - start = second(); - - if (0 == strcmp(opts.reorder, "symrcm")) { - checkCudaErrors( - cusolverSpXcsrsymrcmHost(cusolverSpH, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_Qreorder)); - } - else if (0 == strcmp(opts.reorder, "symamd")) { - checkCudaErrors( - cusolverSpXcsrsymamdHost(cusolverSpH, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_Qreorder)); - } - else { - fprintf(stderr, "Error: %s is unknow reordering\n", opts.reorder); - return 1; - } - - stop = second(); - time_reorder = stop - start; - - printf("step 3: B = Q*A*Q^T\n"); - memcpy(h_csrRowPtrB, h_csrRowPtrA, sizeof(int) * (rowsA + 1)); - memcpy(h_csrColIndB, h_csrColIndA, sizeof(int) * nnzA); - - start = second(); - start = second(); - - checkCudaErrors(cusolverSpXcsrperm_bufferSizeHost( - cusolverSpH, rowsA, colsA, nnzA, descrA, h_csrRowPtrB, h_csrColIndB, h_Qreorder, h_Qreorder, &size_perm)); - - if (buffer_cpu) { - free(buffer_cpu); - } - buffer_cpu = (void *)malloc(sizeof(char) * size_perm); - assert(NULL != buffer_cpu); - - // h_mapBfromA = Identity - for (int j = 0; j < nnzA; j++) { - h_mapBfromA[j] = j; - } - checkCudaErrors(cusolverSpXcsrpermHost(cusolverSpH, - rowsA, - colsA, - nnzA, - descrA, - h_csrRowPtrB, - h_csrColIndB, - h_Qreorder, - h_Qreorder, - h_mapBfromA, - buffer_cpu)); - - // B = A( mapBfromA ) - for (int j = 0; j < nnzA; j++) { - h_csrValB[j] = h_csrValA[h_mapBfromA[j]]; - } - - stop = second(); - time_perm = stop - start; - - printf("step 4: solve A*x = b by LU(B) in cusolverSp\n"); - - printf("step 4.1: create opaque info structure\n"); - checkCudaErrors(cusolverSpCreateCsrluInfoHost(&info)); - - printf("step 4.2: analyze LU(B) to know structure of Q and R, and upper bound " - "for nnz(L+U)\n"); - start = second(); - start = second(); - - checkCudaErrors(cusolverSpXcsrluAnalysisHost(cusolverSpH, rowsA, nnzA, descrA, h_csrRowPtrB, h_csrColIndB, info)); - - stop = second(); - time_sp_analysis = stop - start; - - printf("step 4.3: workspace for LU(B)\n"); - checkCudaErrors(cusolverSpDcsrluBufferInfoHost( - cusolverSpH, rowsA, nnzA, descrA, h_csrValB, h_csrRowPtrB, h_csrColIndB, info, &size_internal, &size_lu)); - - if (buffer_cpu) { - free(buffer_cpu); - } - buffer_cpu = (void *)malloc(sizeof(char) * size_lu); - assert(NULL != buffer_cpu); - - printf("step 4.4: compute Ppivot*B = L*U \n"); - start = second(); - start = second(); - - checkCudaErrors(cusolverSpDcsrluFactorHost( - cusolverSpH, rowsA, nnzA, descrA, h_csrValB, h_csrRowPtrB, h_csrColIndB, info, pivot_threshold, buffer_cpu)); - - stop = second(); - time_sp_factor = stop - start; - - // TODO: check singularity by tol - printf("step 4.5: check if the matrix is singular \n"); - checkCudaErrors(cusolverSpDcsrluZeroPivotHost(cusolverSpH, info, tol, &singularity)); - - if (0 <= singularity) { - fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity); - return 1; - } - - printf("step 4.6: solve A*x = b \n"); - printf(" i.e. solve B*(Qx) = Q*b \n"); - start = second(); - start = second(); - - // b_hat = Q*b - for (int j = 0; j < rowsA; j++) { - h_bhat[j] = h_b[h_Qreorder[j]]; - } - // B*x_hat = b_hat - checkCudaErrors(cusolverSpDcsrluSolveHost(cusolverSpH, rowsA, h_bhat, h_xhat, info, buffer_cpu)); - - // x = Q^T * x_hat - for (int j = 0; j < rowsA; j++) { - h_x[h_Qreorder[j]] = h_xhat[j]; - } - - stop = second(); - time_sp_solve = stop - start; - - printf("step 4.7: evaluate residual r = b - A*x (result on CPU)\n"); - // use GPU gemv to compute r = b - A*x - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_x, h_x, sizeof(double) * colsA, cudaMemcpyHostToDevice)); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - if (baseA) { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ONE, - CUDA_R_64F)); - } - else { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_64F)); - } - - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F)); - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA); - - printf("(CPU) |b - A*x| = %E \n", r_inf); - printf("(CPU) |A| = %E \n", A_inf); - printf("(CPU) |x| = %E \n", x_inf); - printf("(CPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - printf("step 5: extract P, Q, L and U from P*B*Q^T = L*U \n"); - printf(" L has implicit unit diagonal\n"); - start = second(); - start = second(); - - checkCudaErrors(cusolverSpXcsrluNnzHost(cusolverSpH, &nnzL, &nnzU, info)); - - h_Plu = (int *)malloc(sizeof(int) * rowsA); - h_Qlu = (int *)malloc(sizeof(int) * colsA); - - h_csrValL = (double *)malloc(sizeof(double) * nnzL); - h_csrRowPtrL = (int *)malloc(sizeof(int) * (rowsA + 1)); - h_csrColIndL = (int *)malloc(sizeof(int) * nnzL); - - h_csrValU = (double *)malloc(sizeof(double) * nnzU); - h_csrRowPtrU = (int *)malloc(sizeof(int) * (rowsA + 1)); - h_csrColIndU = (int *)malloc(sizeof(int) * nnzU); - - assert(NULL != h_Plu); - assert(NULL != h_Qlu); - - assert(NULL != h_csrValL); - assert(NULL != h_csrRowPtrL); - assert(NULL != h_csrColIndL); - - assert(NULL != h_csrValU); - assert(NULL != h_csrRowPtrU); - assert(NULL != h_csrColIndU); - - checkCudaErrors(cusolverSpDcsrluExtractHost(cusolverSpH, - h_Plu, - h_Qlu, - descrA, - h_csrValL, - h_csrRowPtrL, - h_csrColIndL, - descrA, - h_csrValU, - h_csrRowPtrU, - h_csrColIndU, - info, - buffer_cpu)); - - stop = second(); - time_sp_extract = stop - start; - - printf("nnzL = %d, nnzU = %d\n", nnzL, nnzU); - - /* B = Qreorder*A*Qreorder^T - * Plu*B*Qlu^T = L*U - * - * (Plu*Qreorder)*A*(Qlu*Qreorder)^T = L*U - * - * Let P = Plu*Qreroder, Q = Qlu*Qreorder, - * then we have - * P*A*Q^T = L*U - * which is the fundamental relation in cusolverRf. - */ - printf("step 6: form P*A*Q^T = L*U\n"); - - h_P = (int *)malloc(sizeof(int) * rowsA); - h_Q = (int *)malloc(sizeof(int) * colsA); - assert(NULL != h_P); - assert(NULL != h_Q); - - printf("step 6.1: P = Plu*Qreroder\n"); - // gather operation, P = Qreorder(Plu) - for (int j = 0; j < rowsA; j++) { - h_P[j] = h_Qreorder[h_Plu[j]]; - } - - printf("step 6.2: Q = Qlu*Qreorder \n"); - // gather operation, Q = Qreorder(Qlu) - for (int j = 0; j < colsA; j++) { - h_Q[j] = h_Qreorder[h_Qlu[j]]; - } - - printf("step 7: create cusolverRf handle\n"); - checkCudaErrors(cusolverRfCreate(&cusolverRfH)); - - printf("step 8: set parameters for cusolverRf \n"); - // numerical values for checking "zeros" and for boosting. - checkCudaErrors(cusolverRfSetNumericProperties(cusolverRfH, nzero, nboost)); - - // choose algorithm for refactorization and solve - checkCudaErrors(cusolverRfSetAlgs(cusolverRfH, fact_alg, solve_alg)); - - // matrix mode: L and U are CSR format, and L has implicit unit diagonal - checkCudaErrors( - cusolverRfSetMatrixFormat(cusolverRfH, CUSOLVERRF_MATRIX_FORMAT_CSR, CUSOLVERRF_UNIT_DIAGONAL_ASSUMED_L)); - - // fast mode for matrix assembling - checkCudaErrors(cusolverRfSetResetValuesFastMode(cusolverRfH, CUSOLVERRF_RESET_VALUES_FAST_MODE_ON)); - - printf("step 9: assemble P*A*Q = L*U \n"); - start = second(); - start = second(); - - checkCudaErrors(cusolverRfSetupHost(rowsA, - nnzA, - h_csrRowPtrA, - h_csrColIndA, - h_csrValA, - nnzL, - h_csrRowPtrL, - h_csrColIndL, - h_csrValL, - nnzU, - h_csrRowPtrU, - h_csrColIndU, - h_csrValU, - h_P, - h_Q, - cusolverRfH)); - - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - time_rf_assemble = stop - start; - - printf("step 10: analyze to extract parallelism \n"); - checkCudaErrors(cusolverRfAnalyze(cusolverRfH)); - - printf("step 11: import A to cusolverRf \n"); - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_P, h_P, sizeof(int) * rowsA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_Q, h_Q, sizeof(int) * colsA, cudaMemcpyHostToDevice)); - - start = second(); - start = second(); - - checkCudaErrors(cusolverRfResetValues(rowsA, nnzA, d_csrRowPtrA, d_csrColIndA, d_csrValA, d_P, d_Q, cusolverRfH)); - - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - time_rf_reset = stop - start; - - printf("step 12: refactorization \n"); - start = second(); - start = second(); - - checkCudaErrors(cusolverRfRefactor(cusolverRfH)); - - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - time_rf_refactor = stop - start; - - printf("step 13: solve A*x = b \n"); - checkCudaErrors(cudaMemcpy(d_x, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - start = second(); - start = second(); - - checkCudaErrors(cusolverRfSolve(cusolverRfH, d_P, d_Q, 1, d_T, rowsA, d_x, rowsA)); - - checkCudaErrors(cudaDeviceSynchronize()); - stop = second(); - time_rf_solve = stop - start; - - printf("step 14: evaluate residual r = b - A*x (result on GPU)\n"); - checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_x, d_x, sizeof(double) * colsA, cudaMemcpyDeviceToHost)); - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - printf("(GPU) |b - A*x| = %E \n", r_inf); - printf("(GPU) |A| = %E \n", A_inf); - printf("(GPU) |x| = %E \n", x_inf); - printf("(GPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - printf("===== statistics \n"); - printf(" nnz(A) = %d, nnz(L+U) = %d, zero fill-in ratio = %f\n", - nnzA, - nnzL + nnzU, - ((double)(nnzL + nnzU)) / (double)nnzA); - printf("\n"); - printf("===== timing profile \n"); - printf(" reorder A : %f sec\n", time_reorder); - printf(" B = Q*A*Q^T : %f sec\n", time_perm); - printf("\n"); - printf(" cusolverSp LU analysis: %f sec\n", time_sp_analysis); - printf(" cusolverSp LU factor : %f sec\n", time_sp_factor); - printf(" cusolverSp LU solve : %f sec\n", time_sp_solve); - printf(" cusolverSp LU extract : %f sec\n", time_sp_extract); - printf("\n"); - printf(" cusolverRf assemble : %f sec\n", time_rf_assemble); - printf(" cusolverRf reset : %f sec\n", time_rf_reset); - printf(" cusolverRf refactor : %f sec\n", time_rf_refactor); - printf(" cusolverRf solve : %f sec\n", time_rf_solve); - - if (cusolverRfH) { - checkCudaErrors(cusolverRfDestroy(cusolverRfH)); - } - if (cusolverSpH) { - checkCudaErrors(cusolverSpDestroy(cusolverSpH)); - } - if (cusparseH) { - checkCudaErrors(cusparseDestroy(cusparseH)); - } - if (stream) { - checkCudaErrors(cudaStreamDestroy(stream)); - } - if (descrA) { - checkCudaErrors(cusparseDestroyMatDescr(descrA)); - } - if (info) { - checkCudaErrors(cusolverSpDestroyCsrluInfoHost(info)); - } - - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - - if (h_csrValA) { - free(h_csrValA); - } - if (h_csrRowPtrA) { - free(h_csrRowPtrA); - } - if (h_csrColIndA) { - free(h_csrColIndA); - } - - if (h_Qreorder) { - free(h_Qreorder); - } - - if (h_csrRowPtrB) { - free(h_csrRowPtrB); - } - if (h_csrColIndB) { - free(h_csrColIndB); - } - if (h_csrValB) { - free(h_csrValB); - } - if (h_mapBfromA) { - free(h_mapBfromA); - } - - if (h_x) { - free(h_x); - } - if (h_b) { - free(h_b); - } - if (h_r) { - free(h_r); - } - if (h_xhat) { - free(h_xhat); - } - if (h_bhat) { - free(h_bhat); - } - - if (buffer_cpu) { - free(buffer_cpu); - } - - if (h_Plu) { - free(h_Plu); - } - if (h_Qlu) { - free(h_Qlu); - } - if (h_csrRowPtrL) { - free(h_csrRowPtrL); - } - if (h_csrColIndL) { - free(h_csrColIndL); - } - if (h_csrValL) { - free(h_csrValL); - } - if (h_csrRowPtrU) { - free(h_csrRowPtrU); - } - if (h_csrColIndU) { - free(h_csrColIndU); - } - if (h_csrValU) { - free(h_csrValU); - } - - if (h_P) { - free(h_P); - } - if (h_Q) { - free(h_Q); - } - - if (d_csrValA) { - checkCudaErrors(cudaFree(d_csrValA)); - } - if (d_csrRowPtrA) { - checkCudaErrors(cudaFree(d_csrRowPtrA)); - } - if (d_csrColIndA) { - checkCudaErrors(cudaFree(d_csrColIndA)); - } - if (d_x) { - checkCudaErrors(cudaFree(d_x)); - } - if (d_b) { - checkCudaErrors(cudaFree(d_b)); - } - if (d_r) { - checkCudaErrors(cudaFree(d_r)); - } - if (d_P) { - checkCudaErrors(cudaFree(d_P)); - } - if (d_Q) { - checkCudaErrors(cudaFree(d_Q)); - } - if (d_T) { - checkCudaErrors(cudaFree(d_T)); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/lap2D_5pt_n100.mtx b/cpp/4_CUDA_Libraries/cuSolverRf/lap2D_5pt_n100.mtx deleted file mode 100644 index dae3c1eb..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/lap2D_5pt_n100.mtx +++ /dev/null @@ -1,29803 +0,0 @@ -%%MatrixMarket matrix coordinate real symmetric -% Generated 20-Nov-2014 -10000 10000 29800 -1 1 4 -2 1 -1 -101 1 -1 -2 2 4 -3 2 -1 -102 2 -1 -3 3 4 -4 3 -1 -103 3 -1 -4 4 4 -5 4 -1 -104 4 -1 -5 5 4 -6 5 -1 -105 5 -1 -6 6 4 -7 6 -1 -106 6 -1 -7 7 4 -8 7 -1 -107 7 -1 -8 8 4 -9 8 -1 -108 8 -1 -9 9 4 -10 9 -1 -109 9 -1 -10 10 4 -11 10 -1 -110 10 -1 -11 11 4 -12 11 -1 -111 11 -1 -12 12 4 -13 12 -1 -112 12 -1 -13 13 4 -14 13 -1 -113 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-7993 7992 -1 -7993 7993 6 -7994 7993 -1 -7994 7994 6 -7995 7994 -1 -7995 7995 6 -7996 7995 -1 -7996 7996 6 -7997 7996 -1 -7997 7997 6 -7998 7997 -1 -7998 7998 6 -7999 7998 -1 -7999 7999 6 -8000 7999 -1 -8000 8000 6 diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/mmio.c b/cpp/4_CUDA_Libraries/cuSolverRf/mmio.c deleted file mode 100644 index de5528e8..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/mmio.c +++ /dev/null @@ -1,474 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -#include "mmio.h" - -#include -#include -#include -#include - -int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_) -{ - FILE *f; - MM_typecode matcode; - int M, N, nz; - int i; - double *val; - int *I, *J; - - if ((f = fopen(fname, "r")) == NULL) - return -1; - - - if (mm_read_banner(f, &matcode) != 0) { - printf("mm_read_unsymetric: Could not process Matrix Market banner "); - printf(" in file [%s]\n", fname); - return -1; - } - - - if (!(mm_is_real(matcode) && mm_is_matrix(matcode) && mm_is_sparse(matcode))) { - fprintf(stderr, "Sorry, this application does not support "); - fprintf(stderr, "Market Market type: [%s]\n", mm_typecode_to_str(matcode)); - return -1; - } - - /* find out size of sparse matrix: M, N, nz .... */ - - if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) { - fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n"); - return -1; - } - - *M_ = M; - *N_ = N; - *nz_ = nz; - - /* reserve memory for matrices */ - - I = (int *)malloc(nz * sizeof(int)); - J = (int *)malloc(nz * sizeof(int)); - val = (double *)malloc(nz * sizeof(double)); - - *val_ = val; - *I_ = I; - *J_ = J; - - /* NOTE: when reading in doubles, ANSI C requires the use of the "l" */ - /* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */ - /* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */ - - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) { - return -1; - } - I[i]--; /* adjust from 1-based to 0-based */ - J[i]--; - } - fclose(f); - - return 0; -} - -int mm_is_valid(MM_typecode matcode) -{ - if (!mm_is_matrix(matcode)) - return 0; - if (mm_is_dense(matcode) && mm_is_pattern(matcode)) - return 0; - if (mm_is_real(matcode) && mm_is_hermitian(matcode)) - return 0; - if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) || mm_is_skew(matcode))) - return 0; - return 1; -} - -int mm_read_banner(FILE *f, MM_typecode *matcode) -{ - char line[MM_MAX_LINE_LENGTH]; - char banner[MM_MAX_TOKEN_LENGTH]; - char mtx[MM_MAX_TOKEN_LENGTH]; - char crd[MM_MAX_TOKEN_LENGTH]; - char data_type[MM_MAX_TOKEN_LENGTH]; - char storage_scheme[MM_MAX_TOKEN_LENGTH]; - char *p; - - - mm_clear_typecode(matcode); - - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - - if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type, storage_scheme) != 5) - return MM_PREMATURE_EOF; - - for (p = mtx; *p != '\0'; *p = tolower(*p), p++) - ; /* convert to lower case */ - for (p = crd; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = data_type; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++) - ; - - /* check for banner */ - if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0) - return MM_NO_HEADER; - - /* first field should be "mtx" */ - if (strcmp(mtx, MM_MTX_STR) != 0) - return MM_UNSUPPORTED_TYPE; - mm_set_matrix(matcode); - - - /* second field describes whether this is a sparse matrix (in coordinate - storgae) or a dense array */ - - - if (strcmp(crd, MM_SPARSE_STR) == 0) - mm_set_sparse(matcode); - else if (strcmp(crd, MM_DENSE_STR) == 0) - mm_set_dense(matcode); - else - return MM_UNSUPPORTED_TYPE; - - /* third field */ - - if (strcmp(data_type, MM_REAL_STR) == 0) - mm_set_real(matcode); - else if (strcmp(data_type, MM_COMPLEX_STR) == 0) - mm_set_complex(matcode); - else if (strcmp(data_type, MM_PATTERN_STR) == 0) - mm_set_pattern(matcode); - else if (strcmp(data_type, MM_INT_STR) == 0) - mm_set_integer(matcode); - else - return MM_UNSUPPORTED_TYPE; - - /* fourth field */ - - if (strcmp(storage_scheme, MM_GENERAL_STR) == 0) - mm_set_general(matcode); - else if (strcmp(storage_scheme, MM_SYMM_STR) == 0) - mm_set_symmetric(matcode); - else if (strcmp(storage_scheme, MM_HERM_STR) == 0) - mm_set_hermitian(matcode); - else if (strcmp(storage_scheme, MM_SKEW_STR) == 0) - mm_set_skew(matcode); - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) -{ - if (fprintf(f, "%d %d %d\n", M, N, nz) != 3) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - - /* set return null parameter values, in case we exit with errors */ - *M = *N = *nz = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d %d", M, N, nz) == 3) - return 0; - - else - do { - num_items_read = fscanf(f, "%d %d %d", M, N, nz); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 3); - - return 0; -} - - -int mm_read_mtx_array_size(FILE *f, int *M, int *N) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - /* set return null parameter values, in case we exit with errors */ - *M = *N = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d", M, N) == 2) - return 0; - - else /* we have a blank line */ - do { - num_items_read = fscanf(f, "%d %d", M, N); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 2); - - return 0; -} - -int mm_write_mtx_array_size(FILE *f, int M, int N) -{ - if (fprintf(f, "%d %d\n", M, N) != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - - -/*-------------------------------------------------------------------------*/ - -/******************************************************************/ -/* use when I[], J[], and val[]J, and val[] are already allocated */ -/******************************************************************/ - -int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - int i; - if (mm_is_complex(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i], &val[2 * i + 1]) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) - return MM_PREMATURE_EOF; - } - } - - else if (mm_is_pattern(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag, MM_typecode matcode) -{ - if (mm_is_complex(matcode)) { - if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) - return MM_PREMATURE_EOF; - } - - else if (mm_is_pattern(matcode)) { - if (fscanf(f, "%d %d", I, J) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - - -/************************************************************************ - mm_read_mtx_crd() fills M, N, nz, array of values, and return - type code, e.g. 'MCRS' - - if matrix is complex, values[] is of size 2*nz, - (nz pairs of real/imaginary values) -************************************************************************/ - -int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode) -{ - int ret_code; - FILE *f; - - if (strcmp(fname, "stdin") == 0) - f = stdin; - else if ((f = fopen(fname, "r")) == NULL) - return MM_COULD_NOT_READ_FILE; - - - if ((ret_code = mm_read_banner(f, matcode)) != 0) - return ret_code; - - if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) && mm_is_matrix(*matcode))) - return MM_UNSUPPORTED_TYPE; - - if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) - return ret_code; - - - *I = (int *)malloc(*nz * sizeof(int)); - *J = (int *)malloc(*nz * sizeof(int)); - *val = NULL; - - if (mm_is_complex(*matcode)) { - *val = (double *)malloc(*nz * 2 * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - else if (mm_is_real(*matcode) || mm_is_integer(*matcode)) { - *val = (double *)malloc(*nz * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - else if (mm_is_pattern(*matcode)) { - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - if (f != stdin) - fclose(f); - return 0; -} - -int mm_write_banner(FILE *f, MM_typecode matcode) -{ - char *str = mm_typecode_to_str(matcode); - int ret_code; - - ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str); - free(str); - if (ret_code != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - FILE *f; - int i; - - if (strcmp(fname, "stdout") == 0) - f = stdout; - else if ((f = fopen(fname, "w")) == NULL) - return MM_COULD_NOT_WRITE_FILE; - - /* print banner followed by typecode */ - fprintf(f, "%s ", MatrixMarketBanner); - fprintf(f, "%s\n", mm_typecode_to_str(matcode)); - - /* print matrix sizes and nonzeros */ - fprintf(f, "%d %d %d\n", M, N, nz); - - /* print values */ - if (mm_is_pattern(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d\n", I[i], J[i]); - else if (mm_is_integer(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %d\n", I[i], J[i], (int)val[i]); - else if (mm_is_real(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]); - else if (mm_is_complex(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i], val[2 * i + 1]); - else { - if (f != stdout) - fclose(f); - return MM_UNSUPPORTED_TYPE; - } - - if (f != stdout) - fclose(f); - - return 0; -} - -/** - * Create a new copy of a string s. mm_strdup() is a common routine, but - * not part of ANSI C, so it is included here. Used by mm_typecode_to_str(). - * - */ -static char *mm_strdup(const char *s) -{ - size_t len = strlen(s); - char *s2 = (char *)malloc((len + 1) * sizeof(char)); - return strcpy(s2, s); -} - -char *mm_typecode_to_str(MM_typecode matcode) -{ - char buffer[MM_MAX_LINE_LENGTH]; - char *types[4]; - // char *mm_strdup(const char *); - // int error =0; - - /* check for MTX type */ - if (mm_is_matrix(matcode)) - types[0] = MM_MTX_STR; - else - return NULL; // error=1; - - /* check for CRD or ARR matrix */ - if (mm_is_sparse(matcode)) - types[1] = MM_SPARSE_STR; - else if (mm_is_dense(matcode)) - types[1] = MM_DENSE_STR; - else - return NULL; - - /* check for element data type */ - if (mm_is_real(matcode)) - types[2] = MM_REAL_STR; - else if (mm_is_complex(matcode)) - types[2] = MM_COMPLEX_STR; - else if (mm_is_pattern(matcode)) - types[2] = MM_PATTERN_STR; - else if (mm_is_integer(matcode)) - types[2] = MM_INT_STR; - else - return NULL; - - - /* check for symmetry type */ - if (mm_is_general(matcode)) - types[3] = MM_GENERAL_STR; - else if (mm_is_symmetric(matcode)) - types[3] = MM_SYMM_STR; - else if (mm_is_hermitian(matcode)) - types[3] = MM_HERM_STR; - else if (mm_is_skew(matcode)) - types[3] = MM_SKEW_STR; - else - return NULL; - - sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]); - return mm_strdup(buffer); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/mmio.h b/cpp/4_CUDA_Libraries/cuSolverRf/mmio.h deleted file mode 100644 index a05f6063..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/mmio.h +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -#ifndef MM_IO_H -#define MM_IO_H - -#include - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - -#define MM_MAX_LINE_LENGTH 1025 -#define MatrixMarketBanner "%%MatrixMarket" -#define MM_MAX_TOKEN_LENGTH 64 - - typedef char MM_typecode[4]; - - char *mm_typecode_to_str(MM_typecode matcode); - - int mm_read_banner(FILE *f, MM_typecode *matcode); - int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz); - int mm_read_mtx_array_size(FILE *f, int *M, int *N); - - int mm_write_banner(FILE *f, MM_typecode matcode); - int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz); - int mm_write_mtx_array_size(FILE *f, int M, int N); - - - /********************* MM_typecode query fucntions ***************************/ - -#define mm_is_matrix(typecode) ((typecode)[0] == 'M') - -#define mm_is_sparse(typecode) ((typecode)[1] == 'C') -#define mm_is_coordinate(typecode) ((typecode)[1] == 'C') -#define mm_is_dense(typecode) ((typecode)[1] == 'A') -#define mm_is_array(typecode) ((typecode)[1] == 'A') - -#define mm_is_complex(typecode) ((typecode)[2] == 'C') -#define mm_is_real(typecode) ((typecode)[2] == 'R') -#define mm_is_pattern(typecode) ((typecode)[2] == 'P') -#define mm_is_integer(typecode) ((typecode)[2] == 'I') - -#define mm_is_symmetric(typecode) ((typecode)[3] == 'S') -#define mm_is_general(typecode) ((typecode)[3] == 'G') -#define mm_is_skew(typecode) ((typecode)[3] == 'K') -#define mm_is_hermitian(typecode) ((typecode)[3] == 'H') - - int mm_is_valid(MM_typecode matcode); /* too complex for a macro */ - - - /********************* MM_typecode modify fucntions ***************************/ - -#define mm_set_matrix(typecode) ((*typecode)[0] = 'M') -#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C') -#define mm_set_array(typecode) ((*typecode)[1] = 'A') -#define mm_set_dense(typecode) mm_set_array(typecode) -#define mm_set_sparse(typecode) mm_set_coordinate(typecode) - -#define mm_set_complex(typecode) ((*typecode)[2] = 'C') -#define mm_set_real(typecode) ((*typecode)[2] = 'R') -#define mm_set_pattern(typecode) ((*typecode)[2] = 'P') -#define mm_set_integer(typecode) ((*typecode)[2] = 'I') - - -#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S') -#define mm_set_general(typecode) ((*typecode)[3] = 'G') -#define mm_set_skew(typecode) ((*typecode)[3] = 'K') -#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H') - -#define mm_clear_typecode(typecode) ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', (*typecode)[3] = 'G') - -#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode) - - - /********************* Matrix Market error codes ***************************/ - - -#define MM_COULD_NOT_READ_FILE 11 -#define MM_PREMATURE_EOF 12 -#define MM_NOT_MTX 13 -#define MM_NO_HEADER 14 -#define MM_UNSUPPORTED_TYPE 15 -#define MM_LINE_TOO_LONG 16 -#define MM_COULD_NOT_WRITE_FILE 17 - - - /******************** Matrix Market internal definitions ******************** - - MM_matrix_typecode: 4-character sequence - - ojbect sparse/ data storage - dense type scheme - - string position: [0] [1] [2] [3] - - Matrix typecode: M(atrix) C(oord) R(eal) G(eneral) - A(array) C(omplex) H(ermitian) - P(attern) S(ymmetric) - I(nteger) K(kew) - - ***********************************************************************/ - -#define MM_MTX_STR "matrix" -#define MM_ARRAY_STR "array" -#define MM_DENSE_STR "array" -#define MM_COORDINATE_STR "coordinate" -#define MM_SPARSE_STR "coordinate" -#define MM_COMPLEX_STR "complex" -#define MM_REAL_STR "real" -#define MM_INT_STR "integer" -#define MM_GENERAL_STR "general" -#define MM_SYMM_STR "symmetric" -#define MM_HERM_STR "hermitian" -#define MM_SKEW_STR "skew-symmetric" -#define MM_PATTERN_STR "pattern" - - - /* high level routines */ - int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode); - - int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img, MM_typecode matcode); - - int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif diff --git a/cpp/4_CUDA_Libraries/cuSolverRf/mmio_wrapper.cpp b/cpp/4_CUDA_Libraries/cuSolverRf/mmio_wrapper.cpp deleted file mode 100644 index 3f798d53..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverRf/mmio_wrapper.cpp +++ /dev/null @@ -1,492 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -#include "mmio.h" - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -/* various __inline__ __device__ function to initialize a T_ELEM */ -template __inline__ T_ELEM cuGet(int); -template <> __inline__ float cuGet(int x) { return float(x); } - -template <> __inline__ double cuGet(int x) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(int x) { return (make_cuDoubleComplex(double(x), 0.0)); } - -template __inline__ T_ELEM cuGet(int, int); -template <> __inline__ float cuGet(int x, int y) { return float(x); } - -template <> __inline__ double cuGet(int x, int y) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x, int y) { return make_cuComplex(float(x), float(y)); } - -template <> __inline__ cuDoubleComplex cuGet(int x, int y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -template __inline__ T_ELEM cuGet(float); -template <> __inline__ float cuGet(float x) { return float(x); } - -template <> __inline__ double cuGet(float x) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(float x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - -template __inline__ T_ELEM cuGet(float, float); -template <> __inline__ float cuGet(float x, float y) { return float(x); } - -template <> __inline__ double cuGet(float x, float y) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x, float y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(float x, float y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -template __inline__ T_ELEM cuGet(double); -template <> __inline__ float cuGet(double x) { return float(x); } - -template <> __inline__ double cuGet(double x) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(double x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - -template __inline__ T_ELEM cuGet(double, double); -template <> __inline__ float cuGet(double x, double y) { return float(x); } - -template <> __inline__ double cuGet(double x, double y) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x, double y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(double x, double y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -static void compress_index(const int *Ind, int nnz, int m, int *Ptr, int base) -{ - int i; - - /* initialize everything to zero */ - for (i = 0; i < m + 1; i++) { - Ptr[i] = 0; - } - /* count elements in every row */ - Ptr[0] = base; - for (i = 0; i < nnz; i++) { - Ptr[Ind[i] + (1 - base)]++; - } - /* add all the values */ - for (i = 0; i < m; i++) { - Ptr[i + 1] += Ptr[i]; - } -} - -struct cooFormat -{ - int i; - int j; - int p; // permutation -}; - -int cmp_cooFormat_csr(struct cooFormat *s, struct cooFormat *t) -{ - if (s->i < t->i) { - return -1; - } - else if (s->i > t->i) { - return 1; - } - else { - return s->j - t->j; - } -} - -int cmp_cooFormat_csc(struct cooFormat *s, struct cooFormat *t) -{ - if (s->j < t->j) { - return -1; - } - else if (s->j > t->j) { - return 1; - } - else { - return s->i - t->i; - } -} - -typedef int (*FUNPTR)(const void *, const void *); -typedef int (*FUNPTR2)(struct cooFormat *s, struct cooFormat *t); - -static FUNPTR2 fptr_array[2] = { - cmp_cooFormat_csr, - cmp_cooFormat_csc, -}; - -static int verify_pattern(int m, int nnz, int *csrRowPtr, int *csrColInd) -{ - int i, col, start, end, base_index; - int error_found = 0; - - if (nnz != (csrRowPtr[m] - csrRowPtr[0])) { - fprintf(stderr, - "Error (nnz check failed): (csrRowPtr[%d]=%d - csrRowPtr[%d]=%d) " - "!= (nnz=%d)\n", - 0, - csrRowPtr[0], - m, - csrRowPtr[m], - nnz); - error_found = 1; - } - - base_index = csrRowPtr[0]; - if ((0 != base_index) && (1 != base_index)) { - fprintf(stderr, "Error (base index check failed): base index = %d\n", base_index); - error_found = 1; - } - - for (i = 0; (!error_found) && (i < m); i++) { - start = csrRowPtr[i] - base_index; - end = csrRowPtr[i + 1] - base_index; - if (start > end) { - fprintf(stderr, - "Error (corrupted row): csrRowPtr[%d] (=%d) > csrRowPtr[%d] (=%d)\n", - i, - start + base_index, - i + 1, - end + base_index); - error_found = 1; - } - for (col = start; col < end; col++) { - if (csrColInd[col] < base_index) { - fprintf(stderr, "Error (column vs. base index check failed): csrColInd[%d] < %d\n", col, base_index); - error_found = 1; - } - if ((col < (end - 1)) && (csrColInd[col] >= csrColInd[col + 1])) { - fprintf(stderr, - "Error (sorting of the column indecis check failed): " - "(csrColInd[%d]=%d) >= (csrColInd[%d]=%d)\n", - col, - csrColInd[col], - col + 1, - csrColInd[col + 1]); - error_found = 1; - } - } - } - return error_found; -} - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix) -{ - MM_typecode matcode; - double *tempVal; - int *tempRowInd, *tempColInd; - double *tval; - int *trow, *tcol; - int *csrRowPtr, *cscColPtr; - int i, j, error, base, count; - struct cooFormat *work; - - /* read the matrix */ - error = mm_read_mtx_crd(filename, m, n, nnz, &trow, &tcol, &tval, &matcode); - if (error) { - fprintf(stderr, "!!!! can not open file: '%s'\n", filename); - return 1; - } - - /* start error checking */ - if (mm_is_complex(matcode) && ((elem_type != 'z') && (elem_type != 'c'))) { - fprintf(stderr, "!!!! complex matrix requires type 'z' or 'c'\n"); - return 1; - } - - if (mm_is_dense(matcode) || mm_is_array(matcode) || mm_is_pattern(matcode) /*|| mm_is_integer(matcode)*/) { - fprintf(stderr, "!!!! dense, array, pattern and integer matrices are not supported\n"); - return 1; - } - - /* if necessary symmetrize the pattern (transform from triangular to full) */ - if ((extendSymMatrix) && (mm_is_symmetric(matcode) || mm_is_hermitian(matcode) || mm_is_skew(matcode))) { - // count number of non-diagonal elements - count = 0; - for (i = 0; i < (*nnz); i++) { - if (trow[i] != tcol[i]) { - count++; - } - } - // allocate space for the symmetrized matrix - tempRowInd = (int *)malloc((*nnz + count) * sizeof(int)); - tempColInd = (int *)malloc((*nnz + count) * sizeof(int)); - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal = (double *)malloc((*nnz + count) * sizeof(double)); - } - else { - tempVal = (double *)malloc(2 * (*nnz + count) * sizeof(double)); - } - // copy the elements regular and transposed locations - for (j = 0, i = 0; i < (*nnz); i++) { - tempRowInd[j] = trow[i]; - tempColInd[j] = tcol[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal[j] = tval[i]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - j++; - if (trow[i] != tcol[i]) { - tempRowInd[j] = tcol[i]; - tempColInd[j] = trow[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (mm_is_skew(matcode)) { - tempVal[j] = -tval[i]; - } - else { - tempVal[j] = tval[i]; - } - } - else { - if (mm_is_hermitian(matcode)) { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = -tval[2 * i + 1]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - } - j++; - } - } - (*nnz) += count; - // free temporary storage - free(trow); - free(tcol); - free(tval); - } - else { - tempRowInd = trow; - tempColInd = tcol; - tempVal = tval; - } - // life time of (trow, tcol, tval) is over. - // please use COO format (tempRowInd, tempColInd, tempVal) - - // use qsort to sort COO format - work = (struct cooFormat *)malloc(sizeof(struct cooFormat) * (*nnz)); - if (NULL == work) { - fprintf(stderr, "!!!! allocation error, malloc failed\n"); - return 1; - } - for (i = 0; i < (*nnz); i++) { - work[i].i = tempRowInd[i]; - work[i].j = tempColInd[i]; - work[i].p = i; // permutation is identity - } - - if (csrFormat) { - /* create row-major ordering of indices (sorted by row and within each row - * by column) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[0]); - } - else { - /* create column-major ordering of indices (sorted by column and within each - * column by row) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[1]); - } - - // (tempRowInd, tempColInd) is sorted either by row-major or by col-major - for (i = 0; i < (*nnz); i++) { - tempRowInd[i] = work[i].i; - tempColInd[i] = work[i].j; - } - - // setup base - // check if there is any row/col 0, if so base-0 - // check if there is any row/col equal to matrix dimension m/n, if so base-1 - int base0 = 0; - int base1 = 0; - for (i = 0; i < (*nnz); i++) { - const int row = tempRowInd[i]; - const int col = tempColInd[i]; - if ((0 == row) || (0 == col)) { - base0 = 1; - } - if ((*m == row) || (*n == col)) { - base1 = 1; - } - } - if (base0 && base1) { - printf("Error: input matrix is base-0 and base-1 \n"); - return 1; - } - - base = 0; - if (base1) { - base = 1; - } - - /* compress the appropriate indices */ - if (csrFormat) { - /* CSR format (assuming row-major format) */ - csrRowPtr = (int *)malloc(((*m) + 1) * sizeof(csrRowPtr[0])); - if (!csrRowPtr) - return 1; - compress_index(tempRowInd, *nnz, *m, csrRowPtr, base); - - *aRowInd = csrRowPtr; - *aColInd = (int *)malloc((*nnz) * sizeof(int)); - } - else { - /* CSC format (assuming column-major format) */ - cscColPtr = (int *)malloc(((*n) + 1) * sizeof(cscColPtr[0])); - if (!cscColPtr) - return 1; - compress_index(tempColInd, *nnz, *n, cscColPtr, base); - - *aColInd = cscColPtr; - *aRowInd = (int *)malloc((*nnz) * sizeof(int)); - } - - /* transfrom the matrix values of type double into one of the cusparse library - * types */ - *aVal = (T_ELEM *)malloc((*nnz) * sizeof(T_ELEM)); - - for (i = 0; i < (*nnz); i++) { - if (csrFormat) { - (*aColInd)[i] = tempColInd[i]; - } - else { - (*aRowInd)[i] = tempRowInd[i]; - } - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - (*aVal)[i] = cuGet(tempVal[work[i].p]); - } - else { - (*aVal)[i] = cuGet(tempVal[2 * work[i].p], tempVal[2 * work[i].p + 1]); - } - } - - /* check for corruption */ - int error_found; - if (csrFormat) { - error_found = verify_pattern(*m, *nnz, *aRowInd, *aColInd); - } - else { - error_found = verify_pattern(*n, *nnz, *aColInd, *aRowInd); - } - if (error_found) { - fprintf(stderr, "!!!! verify_pattern failed\n"); - return 1; - } - - /* cleanup and exit */ - free(work); - free(tempVal); - free(tempColInd); - free(tempRowInd); - - return 0; -} - -/* specific instantiation */ -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - float **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - double **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuDoubleComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/CMakeLists.txt b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/CMakeLists.txt deleted file mode 100644 index fab672ff..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/CMakeLists.txt +++ /dev/null @@ -1,58 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cuSolverSp_LinearSolver LANGUAGES C CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for cuSolverSp_LinearSolver -add_executable(cuSolverSp_LinearSolver cuSolverSp_LinearSolver.cpp mmio.c mmio_wrapper.cpp) - -target_compile_options(cuSolverSp_LinearSolver PRIVATE $<$:--extended-lambda>) - -target_compile_features(cuSolverSp_LinearSolver PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(cuSolverSp_LinearSolver PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(cuSolverSp_LinearSolver PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(cuSolverSp_LinearSolver PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusolver -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverSp_LinearSolver POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap2D_5pt_n100.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverSp_LinearSolver POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap3D_7pt_n20.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/README.md b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/README.md deleted file mode 100644 index 210dd430..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# cuSolverSp_LinearSolver - cuSolverSp Linear Solver - -## Description - -A CUDA Sample that demonstrates cuSolverSP's LU, QR and Cholesky factorization. - -## Key Concepts - -Linear Algebra, CUSOLVER Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuDoubleComplex, cuComplex - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaStreamDestroy, cudaFree, cudaDeviceSynchronize, cudaMalloc, cudaStreamCreate, cudaMemcpyAsync - -## Dependencies needed to build/run -[CUSOLVER](../../../README.md#cusolver), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/cuSolverSp_LinearSolver.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/cuSolverSp_LinearSolver.cpp deleted file mode 100644 index dde0734a..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/cuSolverSp_LinearSolver.cpp +++ /dev/null @@ -1,738 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * Test three linear solvers, including Cholesky, LU and QR. - * The user has to prepare a sparse matrix of "matrix market format" (with - extension .mtx). - * For example, the user can download matrices in Florida Sparse Matrix - Collection. - * (http://www.cise.ufl.edu/research/sparse/matrices/) - * - * The user needs to choose a solver by the switch -R and - * to provide the path of the matrix by the switch -F, then - * the program solves - * A*x = b - * and reports relative error - * |b-A*x|/(|A|*|x|+|b|) - * - * How does it work? - * The example solves A*x = b by the following steps - * step 1: B = A(Q,Q) - * Q is the ordering to minimize zero fill-in. - * The user can choose symrcm or symamd. - * step 2: solve B*z = Q*b - * step 3: x = inv(Q)*z - * - * Above three steps can be combined by the formula - * (Q*A*Q')*(Q*x) = (Q*b) - * - * The elapsed time is also reported so the user can compare efficiency of - different solvers. - * - * How to use - /cuSolverSp_LinearSolver // Default: Cholesky, symrcm & - file=lap2D_5pt_n100.mtx - * ./cuSolverSp_LinearSolver -R=chol -file= // cholesky - factorization - * ./cuSolverSp_LinearSolver -R=lu -P=symrcm -file= // symrcm + LU - with partial pivoting - * ./cuSolverSp_LinearSolver -R=qr -P=symamd -file= // symamd + QR - factorization - * - * - * Remark: the absolute error on solution x is meaningless without knowing - condition number of A. - * The relative error on residual should be close to machine zero, - i.e. 1.e-15. - */ - -#include -#include -#include -#include -#include -#include - -#include "cusolverSp.h" -#include "cusparse.h" -#include "helper_cuda.h" -#include "helper_cusolver.h" - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -void UsageSP(void) -{ - printf("\n"); - printf("-h : display this help\n"); - printf("-R= : choose a linear solver\n"); - printf(" chol (cholesky factorization), this is default\n"); - printf(" qr (QR factorization)\n"); - printf(" lu (LU factorization)\n"); - printf("-P= : choose a reordering\n"); - printf(" symrcm (Reverse Cuthill-McKee)\n"); - printf(" symamd (Approximate Minimum Degree)\n"); - printf(" metis (nested dissection)\n"); - printf("-file= : filename containing a matrix in MM format\n"); - printf("-device= : if want to run on specific GPU\n"); - - exit(0); -} - -void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) -{ - memset(&opts, 0, sizeof(opts)); - - if (checkCmdLineFlag(argc, (const char **)argv, "-h")) { - UsageSP(); - } - - if (checkCmdLineFlag(argc, (const char **)argv, "R")) { - char *solverType = NULL; - getCmdLineArgumentString(argc, (const char **)argv, "R", &solverType); - - if (solverType) { - if ((STRCASECMP(solverType, "chol") != 0) && (STRCASECMP(solverType, "lu") != 0) - && (STRCASECMP(solverType, "qr") != 0)) { - printf("\nIncorrect argument passed to -R option\n"); - UsageSP(); - } - else { - opts.testFunc = solverType; - } - } - } - - if (checkCmdLineFlag(argc, (const char **)argv, "P")) { - char *reorderType = NULL; - getCmdLineArgumentString(argc, (const char **)argv, "P", &reorderType); - - if (reorderType) { - if ((STRCASECMP(reorderType, "symrcm") != 0) && (STRCASECMP(reorderType, "symamd") != 0) - && (STRCASECMP(reorderType, "metis") != 0)) { - printf("\nIncorrect argument passed to -P option\n"); - UsageSP(); - } - else { - opts.reorder = reorderType; - } - } - } - - if (checkCmdLineFlag(argc, (const char **)argv, "file")) { - char *fileName = 0; - getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName); - - if (fileName) { - opts.sparse_mat_filename = fileName; - } - else { - printf("\nIncorrect filename passed to -file \n "); - UsageSP(); - } - } -} - -int main(int argc, char *argv[]) -{ - struct testOpts opts; - cusolverSpHandle_t handle = NULL; - cusparseHandle_t cusparseHandle = NULL; /* used in residual evaluation */ - cudaStream_t stream = NULL; - cusparseMatDescr_t descrA = NULL; - - int rowsA = 0; /* number of rows of A */ - int colsA = 0; /* number of columns of A */ - int nnzA = 0; /* number of nonzeros of A */ - int baseA = 0; /* base index in CSR format */ - - /* CSR(A) from I/O */ - int *h_csrRowPtrA = NULL; - int *h_csrColIndA = NULL; - double *h_csrValA = NULL; - - double *h_z = NULL; /* z = B \ (Q*b) */ - double *h_x = NULL; /* x = A \ b */ - double *h_b = NULL; /* b = ones(n,1) */ - double *h_Qb = NULL; /* Q*b */ - double *h_r = NULL; /* r = b - A*x */ - - int *h_Q = NULL; /* n */ - /* reorder to reduce zero fill-in */ - /* Q = symrcm(A) or Q = symamd(A) */ - /* B = Q*A*Q' or B = A(Q,Q) by MATLAB notation */ - int *h_csrRowPtrB = NULL; /* n+1 */ - int *h_csrColIndB = NULL; /* nnzA */ - double *h_csrValB = NULL; /* nnzA */ - int *h_mapBfromA = NULL; /* nnzA */ - - size_t size_perm = 0; - void *buffer_cpu = NULL; /* working space for permutation: B = Q*A*Q^T */ - - /* device copy of A: used in residual evaluation */ - int *d_csrRowPtrA = NULL; - int *d_csrColIndA = NULL; - double *d_csrValA = NULL; - - /* device copy of B: used in B*z = Q*b */ - int *d_csrRowPtrB = NULL; - int *d_csrColIndB = NULL; - double *d_csrValB = NULL; - - int *d_Q = NULL; /* device copy of h_Q */ - double *d_z = NULL; /* z = B \ Q*b */ - double *d_x = NULL; /* x = A \ b */ - double *d_b = NULL; /* a copy of h_b */ - double *d_Qb = NULL; /* a copy of h_Qb */ - double *d_r = NULL; /* r = b - A*x */ - - double tol = 1.e-12; - const int reorder = 0; /* no reordering */ - int singularity = 0; /* -1 if A is invertible under tol. */ - - /* the constants are used in residual evaluation, r = b - A*x */ - const double minus_one = -1.0; - const double one = 1.0; - - double b_inf = 0.0; - double x_inf = 0.0; - double r_inf = 0.0; - double A_inf = 0.0; - int errors = 0; - int issym = 0; - - double start, stop; - double time_solve_cpu; - double time_solve_gpu; - - parseCommandLineArguments(argc, argv, opts); - - if (NULL == opts.testFunc) { - opts.testFunc = "chol"; /* By default running Cholesky as NO solver selected with -R - option. */ - } - - findCudaDevice(argc, (const char **)argv); - - if (opts.sparse_mat_filename == NULL) { - opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n100.mtx", argv[0]); - if (opts.sparse_mat_filename != NULL) - printf("Using default input file [%s]\n", opts.sparse_mat_filename); - else - printf("Could not find lap2D_5pt_n100.mtx\n"); - } - else { - printf("Using input file [%s]\n", opts.sparse_mat_filename); - } - - printf("step 1: read matrix market format\n"); - - if (opts.sparse_mat_filename == NULL) { - fprintf(stderr, "Error: input matrix is not provided\n"); - return EXIT_FAILURE; - } - - if (loadMMSparseMatrix(opts.sparse_mat_filename, - 'd', - true, - &rowsA, - &colsA, - &nnzA, - &h_csrValA, - &h_csrRowPtrA, - &h_csrColIndA, - true)) { - exit(EXIT_FAILURE); - } - baseA = h_csrRowPtrA[0]; // baseA = {0,1} - printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA); - - if (rowsA != colsA) { - fprintf(stderr, "Error: only support square matrix\n"); - return 1; - } - - checkCudaErrors(cusolverSpCreate(&handle)); - checkCudaErrors(cusparseCreate(&cusparseHandle)); - - checkCudaErrors(cudaStreamCreate(&stream)); - /* bind stream to cusparse and cusolver*/ - checkCudaErrors(cusolverSpSetStream(handle, stream)); - checkCudaErrors(cusparseSetStream(cusparseHandle, stream)); - - /* configure matrix descriptor*/ - checkCudaErrors(cusparseCreateMatDescr(&descrA)); - checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL)); - if (baseA) { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE)); - } - else { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO)); - } - - h_z = (double *)malloc(sizeof(double) * colsA); - h_x = (double *)malloc(sizeof(double) * colsA); - h_b = (double *)malloc(sizeof(double) * rowsA); - h_Qb = (double *)malloc(sizeof(double) * rowsA); - h_r = (double *)malloc(sizeof(double) * rowsA); - - h_Q = (int *)malloc(sizeof(int) * colsA); - h_csrRowPtrB = (int *)malloc(sizeof(int) * (rowsA + 1)); - h_csrColIndB = (int *)malloc(sizeof(int) * nnzA); - h_csrValB = (double *)malloc(sizeof(double) * nnzA); - h_mapBfromA = (int *)malloc(sizeof(int) * nnzA); - - assert(NULL != h_z); - assert(NULL != h_x); - assert(NULL != h_b); - assert(NULL != h_Qb); - assert(NULL != h_r); - assert(NULL != h_Q); - assert(NULL != h_csrRowPtrB); - assert(NULL != h_csrColIndB); - assert(NULL != h_csrValB); - assert(NULL != h_mapBfromA); - - checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int) * (rowsA + 1))); - checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrValA, sizeof(double) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrB, sizeof(int) * (rowsA + 1))); - checkCudaErrors(cudaMalloc((void **)&d_csrColIndB, sizeof(int) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrValB, sizeof(double) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_Q, sizeof(int) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_z, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_Qb, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA)); - - /* verify if A has symmetric pattern or not */ - checkCudaErrors( - cusolverSpXcsrissymHost(handle, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrRowPtrA + 1, h_csrColIndA, &issym)); - - if (0 == strcmp(opts.testFunc, "chol")) { - if (!issym) { - printf("Error: A has no symmetric pattern, please use LU or QR \n"); - exit(EXIT_FAILURE); - } - } - - printf("step 2: reorder the matrix A to minimize zero fill-in\n"); - printf(" if the user choose a reordering by -P=symrcm, -P=symamd or " - "-P=metis\n"); - - if (NULL != opts.reorder) { - if (0 == strcmp(opts.reorder, "symrcm")) { - printf("step 2.1: Q = symrcm(A) \n"); - checkCudaErrors(cusolverSpXcsrsymrcmHost(handle, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_Q)); - } - else if (0 == strcmp(opts.reorder, "symamd")) { - printf("step 2.1: Q = symamd(A) \n"); - checkCudaErrors(cusolverSpXcsrsymamdHost(handle, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_Q)); - } - else if (0 == strcmp(opts.reorder, "metis")) { - printf("step 2.1: Q = metis(A) \n"); - checkCudaErrors(cusolverSpXcsrmetisndHost(handle, - rowsA, - nnzA, - descrA, - h_csrRowPtrA, - h_csrColIndA, - NULL, /* default setting. */ - h_Q)); - } - else { - fprintf(stderr, "Error: %s is unknown reordering\n", opts.reorder); - return 1; - } - } - else { - printf("step 2.1: no reordering is chosen, Q = 0:n-1 \n"); - for (int j = 0; j < rowsA; j++) { - h_Q[j] = j; - } - } - - printf("step 2.2: B = A(Q,Q) \n"); - - memcpy(h_csrRowPtrB, h_csrRowPtrA, sizeof(int) * (rowsA + 1)); - memcpy(h_csrColIndB, h_csrColIndA, sizeof(int) * nnzA); - - checkCudaErrors(cusolverSpXcsrperm_bufferSizeHost( - handle, rowsA, colsA, nnzA, descrA, h_csrRowPtrB, h_csrColIndB, h_Q, h_Q, &size_perm)); - - if (buffer_cpu) { - free(buffer_cpu); - } - buffer_cpu = (void *)malloc(sizeof(char) * size_perm); - assert(NULL != buffer_cpu); - - /* h_mapBfromA = Identity */ - for (int j = 0; j < nnzA; j++) { - h_mapBfromA[j] = j; - } - checkCudaErrors(cusolverSpXcsrpermHost( - handle, rowsA, colsA, nnzA, descrA, h_csrRowPtrB, h_csrColIndB, h_Q, h_Q, h_mapBfromA, buffer_cpu)); - - /* B = A( mapBfromA ) */ - for (int j = 0; j < nnzA; j++) { - h_csrValB[j] = h_csrValA[h_mapBfromA[j]]; - } - - printf("step 3: b(j) = 1 + j/n \n"); - for (int row = 0; row < rowsA; row++) { - h_b[row] = 1.0 + ((double)row) / ((double)rowsA); - } - - /* h_Qb = b(Q) */ - for (int row = 0; row < rowsA; row++) { - h_Qb[row] = h_b[h_Q[row]]; - } - - printf("step 4: prepare data on device\n"); - checkCudaErrors( - cudaMemcpyAsync(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors( - cudaMemcpyAsync(d_csrRowPtrB, h_csrRowPtrB, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_csrColIndB, h_csrColIndB, sizeof(int) * nnzA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_csrValB, h_csrValB, sizeof(double) * nnzA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_b, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_Qb, h_Qb, sizeof(double) * rowsA, cudaMemcpyHostToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_Q, h_Q, sizeof(int) * rowsA, cudaMemcpyHostToDevice, stream)); - - printf("step 5: solve A*x = b on CPU \n"); - start = second(); - - /* solve B*z = Q*b */ - if (0 == strcmp(opts.testFunc, "chol")) { - checkCudaErrors(cusolverSpDcsrlsvcholHost( - handle, rowsA, nnzA, descrA, h_csrValB, h_csrRowPtrB, h_csrColIndB, h_Qb, tol, reorder, h_z, &singularity)); - } - else if (0 == strcmp(opts.testFunc, "lu")) { - checkCudaErrors(cusolverSpDcsrlsvluHost( - handle, rowsA, nnzA, descrA, h_csrValB, h_csrRowPtrB, h_csrColIndB, h_Qb, tol, reorder, h_z, &singularity)); - } - else if (0 == strcmp(opts.testFunc, "qr")) { - checkCudaErrors(cusolverSpDcsrlsvqrHost( - handle, rowsA, nnzA, descrA, h_csrValB, h_csrRowPtrB, h_csrColIndB, h_Qb, tol, reorder, h_z, &singularity)); - } - else { - fprintf(stderr, "Error: %s is unknown function\n", opts.testFunc); - return 1; - } - - /* Q*x = z */ - for (int row = 0; row < rowsA; row++) { - h_x[h_Q[row]] = h_z[row]; - } - - if (0 <= singularity) { - printf("WARNING: the matrix is singular at row %d under tol (%E)\n", singularity, tol); - } - - stop = second(); - time_solve_cpu = stop - start; - - printf("step 6: evaluate residual r = b - A*x (result on CPU)\n"); - checkCudaErrors(cudaMemcpyAsync(d_r, d_b, sizeof(double) * rowsA, cudaMemcpyDeviceToDevice, stream)); - checkCudaErrors(cudaMemcpyAsync(d_x, h_x, sizeof(double) * colsA, cudaMemcpyHostToDevice, stream)); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - if (baseA) { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ONE, - CUDA_R_64F)); - } - else { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_64F)); - } - - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F)); - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpyAsync(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost, stream)); - /* wait until h_r is ready */ - checkCudaErrors(cudaDeviceSynchronize()); - - b_inf = vec_norminf(rowsA, h_b); - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA); - - printf("(CPU) |b - A*x| = %E \n", r_inf); - printf("(CPU) |A| = %E \n", A_inf); - printf("(CPU) |x| = %E \n", x_inf); - printf("(CPU) |b| = %E \n", b_inf); - printf("(CPU) |b - A*x|/(|A|*|x| + |b|) = %E \n", r_inf / (A_inf * x_inf + b_inf)); - - printf("step 7: solve A*x = b on GPU\n"); - start = second(); - - /* solve B*z = Q*b */ - if (0 == strcmp(opts.testFunc, "chol")) { - checkCudaErrors(cusolverSpDcsrlsvchol( - handle, rowsA, nnzA, descrA, d_csrValB, d_csrRowPtrB, d_csrColIndB, d_Qb, tol, reorder, d_z, &singularity)); - } - else if (0 == strcmp(opts.testFunc, "lu")) { - printf("WARNING: no LU available on GPU \n"); - } - else if (0 == strcmp(opts.testFunc, "qr")) { - checkCudaErrors(cusolverSpDcsrlsvqr( - handle, rowsA, nnzA, descrA, d_csrValB, d_csrRowPtrB, d_csrColIndB, d_Qb, tol, reorder, d_z, &singularity)); - } - else { - fprintf(stderr, "Error: %s is unknow function\n", opts.testFunc); - return 1; - } - checkCudaErrors(cudaDeviceSynchronize()); - if (0 <= singularity) { - printf("WARNING: the matrix is singular at row %d under tol (%E)\n", singularity, tol); - } - /* Q*x = z */ - cusparseSpVecDescr_t vecz = NULL; - checkCudaErrors( - cusparseCreateSpVec(&vecz, colsA, rowsA, d_Q, d_z, CUSPARSE_INDEX_32I, CUSPARSE_INDEX_BASE_ZERO, CUDA_R_64F)); - checkCudaErrors(cusparseScatter(cusparseHandle, vecz, vecx)); - checkCudaErrors(cusparseDestroySpVec(vecz)); - - - checkCudaErrors(cudaDeviceSynchronize()); - - stop = second(); - time_solve_gpu = stop - start; - - printf("step 8: evaluate residual r = b - A*x (result on GPU)\n"); - checkCudaErrors(cudaMemcpyAsync(d_r, d_b, sizeof(double) * rowsA, cudaMemcpyDeviceToDevice, stream)); - - checkCudaErrors(cusparseSpMV(cusparseHandle, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpyAsync(h_x, d_x, sizeof(double) * colsA, cudaMemcpyDeviceToHost, stream)); - checkCudaErrors(cudaMemcpyAsync(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost, stream)); - /* wait until h_x and h_r are ready */ - checkCudaErrors(cudaDeviceSynchronize()); - - b_inf = vec_norminf(rowsA, h_b); - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - - if (0 != strcmp(opts.testFunc, "lu")) { - // only cholesky and qr have GPU version - printf("(GPU) |b - A*x| = %E \n", r_inf); - printf("(GPU) |A| = %E \n", A_inf); - printf("(GPU) |x| = %E \n", x_inf); - printf("(GPU) |b| = %E \n", b_inf); - printf("(GPU) |b - A*x|/(|A|*|x| + |b|) = %E \n", r_inf / (A_inf * x_inf + b_inf)); - } - - fprintf(stdout, "timing %s: CPU = %10.6f sec , GPU = %10.6f sec\n", opts.testFunc, time_solve_cpu, time_solve_gpu); - - if (0 != strcmp(opts.testFunc, "lu")) { - printf("show last 10 elements of solution vector (GPU) \n"); - printf("consistent result for different reordering and solver \n"); - for (int j = rowsA - 10; j < rowsA; j++) { - printf("x[%d] = %E\n", j, h_x[j]); - } - } - - if (handle) { - checkCudaErrors(cusolverSpDestroy(handle)); - } - if (cusparseHandle) { - checkCudaErrors(cusparseDestroy(cusparseHandle)); - } - if (stream) { - checkCudaErrors(cudaStreamDestroy(stream)); - } - if (descrA) { - checkCudaErrors(cusparseDestroyMatDescr(descrA)); - } - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - - if (h_csrValA) { - free(h_csrValA); - } - if (h_csrRowPtrA) { - free(h_csrRowPtrA); - } - if (h_csrColIndA) { - free(h_csrColIndA); - } - if (h_z) { - free(h_z); - } - if (h_x) { - free(h_x); - } - if (h_b) { - free(h_b); - } - if (h_Qb) { - free(h_Qb); - } - if (h_r) { - free(h_r); - } - - if (h_Q) { - free(h_Q); - } - - if (h_csrRowPtrB) { - free(h_csrRowPtrB); - } - if (h_csrColIndB) { - free(h_csrColIndB); - } - if (h_csrValB) { - free(h_csrValB); - } - if (h_mapBfromA) { - free(h_mapBfromA); - } - - if (buffer_cpu) { - free(buffer_cpu); - } - - if (buffer) { - checkCudaErrors(cudaFree(buffer)); - } - if (d_csrValA) { - checkCudaErrors(cudaFree(d_csrValA)); - } - if (d_csrRowPtrA) { - checkCudaErrors(cudaFree(d_csrRowPtrA)); - } - if (d_csrColIndA) { - checkCudaErrors(cudaFree(d_csrColIndA)); - } - if (d_csrValB) { - checkCudaErrors(cudaFree(d_csrValB)); - } - if (d_csrRowPtrB) { - checkCudaErrors(cudaFree(d_csrRowPtrB)); - } - if (d_csrColIndB) { - checkCudaErrors(cudaFree(d_csrColIndB)); - } - if (d_Q) { - checkCudaErrors(cudaFree(d_Q)); - } - if (d_z) { - checkCudaErrors(cudaFree(d_z)); - } - if (d_x) { - checkCudaErrors(cudaFree(d_x)); - } - if (d_b) { - checkCudaErrors(cudaFree(d_b)); - } - if (d_Qb) { - checkCudaErrors(cudaFree(d_Qb)); - } - if (d_r) { - checkCudaErrors(cudaFree(d_r)); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/lap2D_5pt_n100.mtx b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/lap2D_5pt_n100.mtx deleted file mode 100644 index dae3c1eb..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/lap2D_5pt_n100.mtx +++ /dev/null @@ -1,29803 +0,0 @@ -%%MatrixMarket matrix coordinate real symmetric -% Generated 20-Nov-2014 -10000 10000 29800 -1 1 4 -2 1 -1 -101 1 -1 -2 2 4 -3 2 -1 -102 2 -1 -3 3 4 -4 3 -1 -103 3 -1 -4 4 4 -5 4 -1 -104 4 -1 -5 5 4 -6 5 -1 -105 5 -1 -6 6 4 -7 6 -1 -106 6 -1 -7 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-#include -#include -#include -#include - -int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_) -{ - FILE *f; - MM_typecode matcode; - int M, N, nz; - int i; - double *val; - int *I, *J; - - if ((f = fopen(fname, "r")) == NULL) - return -1; - - - if (mm_read_banner(f, &matcode) != 0) { - printf("mm_read_unsymetric: Could not process Matrix Market banner "); - printf(" in file [%s]\n", fname); - return -1; - } - - - if (!(mm_is_real(matcode) && mm_is_matrix(matcode) && mm_is_sparse(matcode))) { - fprintf(stderr, "Sorry, this application does not support "); - fprintf(stderr, "Market Market type: [%s]\n", mm_typecode_to_str(matcode)); - return -1; - } - - /* find out size of sparse matrix: M, N, nz .... */ - - if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) { - fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n"); - return -1; - } - - *M_ = M; - *N_ = N; - *nz_ = nz; - - /* reserve memory for matrices */ - - I = (int *)malloc(nz * sizeof(int)); - J = (int *)malloc(nz * sizeof(int)); - val = (double *)malloc(nz * sizeof(double)); - - *val_ = val; - *I_ = I; - *J_ = J; - - /* NOTE: when reading in doubles, ANSI C requires the use of the "l" */ - /* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */ - /* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */ - - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) { - return -1; - } - I[i]--; /* adjust from 1-based to 0-based */ - J[i]--; - } - fclose(f); - - return 0; -} - -int mm_is_valid(MM_typecode matcode) -{ - if (!mm_is_matrix(matcode)) - return 0; - if (mm_is_dense(matcode) && mm_is_pattern(matcode)) - return 0; - if (mm_is_real(matcode) && mm_is_hermitian(matcode)) - return 0; - if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) || mm_is_skew(matcode))) - return 0; - return 1; -} - -int mm_read_banner(FILE *f, MM_typecode *matcode) -{ - char line[MM_MAX_LINE_LENGTH]; - char banner[MM_MAX_TOKEN_LENGTH]; - char mtx[MM_MAX_TOKEN_LENGTH]; - char crd[MM_MAX_TOKEN_LENGTH]; - char data_type[MM_MAX_TOKEN_LENGTH]; - char storage_scheme[MM_MAX_TOKEN_LENGTH]; - char *p; - - - mm_clear_typecode(matcode); - - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - - if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type, storage_scheme) != 5) - return MM_PREMATURE_EOF; - - for (p = mtx; *p != '\0'; *p = tolower(*p), p++) - ; /* convert to lower case */ - for (p = crd; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = data_type; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++) - ; - - /* check for banner */ - if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0) - return MM_NO_HEADER; - - /* first field should be "mtx" */ - if (strcmp(mtx, MM_MTX_STR) != 0) - return MM_UNSUPPORTED_TYPE; - mm_set_matrix(matcode); - - - /* second field describes whether this is a sparse matrix (in coordinate - storage) or a dense array */ - - - if (strcmp(crd, MM_SPARSE_STR) == 0) - mm_set_sparse(matcode); - else if (strcmp(crd, MM_DENSE_STR) == 0) - mm_set_dense(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* third field */ - - if (strcmp(data_type, MM_REAL_STR) == 0) - mm_set_real(matcode); - else if (strcmp(data_type, MM_COMPLEX_STR) == 0) - mm_set_complex(matcode); - else if (strcmp(data_type, MM_PATTERN_STR) == 0) - mm_set_pattern(matcode); - else if (strcmp(data_type, MM_INT_STR) == 0) - mm_set_integer(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* fourth field */ - - if (strcmp(storage_scheme, MM_GENERAL_STR) == 0) - mm_set_general(matcode); - else if (strcmp(storage_scheme, MM_SYMM_STR) == 0) - mm_set_symmetric(matcode); - else if (strcmp(storage_scheme, MM_HERM_STR) == 0) - mm_set_hermitian(matcode); - else if (strcmp(storage_scheme, MM_SKEW_STR) == 0) - mm_set_skew(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - return 0; -} - -int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) -{ - if (fprintf(f, "%d %d %d\n", M, N, nz) != 3) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - - /* set return null parameter values, in case we exit with errors */ - *M = *N = *nz = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d %d", M, N, nz) == 3) - return 0; - - else - do { - num_items_read = fscanf(f, "%d %d %d", M, N, nz); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 3); - - return 0; -} - - -int mm_read_mtx_array_size(FILE *f, int *M, int *N) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - /* set return null parameter values, in case we exit with errors */ - *M = *N = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d", M, N) == 2) - return 0; - - else /* we have a blank line */ - do { - num_items_read = fscanf(f, "%d %d", M, N); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 2); - - return 0; -} - -int mm_write_mtx_array_size(FILE *f, int M, int N) -{ - if (fprintf(f, "%d %d\n", M, N) != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - - -/*-------------------------------------------------------------------------*/ - -/******************************************************************/ -/* use when I[], J[], and val[]J, and val[] are already allocated */ -/******************************************************************/ - -int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - int i; - if (mm_is_complex(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i], &val[2 * i + 1]) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) - return MM_PREMATURE_EOF; - } - } - - else if (mm_is_pattern(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag, MM_typecode matcode) -{ - if (mm_is_complex(matcode)) { - if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) - return MM_PREMATURE_EOF; - } - - else if (mm_is_pattern(matcode)) { - if (fscanf(f, "%d %d", I, J) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - - -/************************************************************************ - mm_read_mtx_crd() fills M, N, nz, array of values, and return - type code, e.g. 'MCRS' - - if matrix is complex, values[] is of size 2*nz, - (nz pairs of real/imaginary values) -************************************************************************/ - -int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode) -{ - int ret_code; - FILE *f; - - if (strcmp(fname, "stdin") == 0) - f = stdin; - else if ((f = fopen(fname, "r")) == NULL) - return MM_COULD_NOT_READ_FILE; - - - if ((ret_code = mm_read_banner(f, matcode)) != 0) - return ret_code; - - if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) && mm_is_matrix(*matcode))) - return MM_UNSUPPORTED_TYPE; - - if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) - return ret_code; - - - *I = (int *)malloc(*nz * sizeof(int)); - *J = (int *)malloc(*nz * sizeof(int)); - *val = NULL; - - if (mm_is_complex(*matcode)) { - *val = (double *)malloc(*nz * 2 * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - else if (mm_is_real(*matcode) || mm_is_integer(*matcode)) { - *val = (double *)malloc(*nz * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - else if (mm_is_pattern(*matcode)) { - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - if (f != stdin) - fclose(f); - return 0; -} - -int mm_write_banner(FILE *f, MM_typecode matcode) -{ - char *str = mm_typecode_to_str(matcode); - int ret_code; - - ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str); - free(str); - if (ret_code != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - FILE *f; - int i; - - if (strcmp(fname, "stdout") == 0) - f = stdout; - else if ((f = fopen(fname, "w")) == NULL) - return MM_COULD_NOT_WRITE_FILE; - - /* print banner followed by typecode */ - fprintf(f, "%s ", MatrixMarketBanner); - fprintf(f, "%s\n", mm_typecode_to_str(matcode)); - - /* print matrix sizes and nonzeros */ - fprintf(f, "%d %d %d\n", M, N, nz); - - /* print values */ - if (mm_is_pattern(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d\n", I[i], J[i]); - else if (mm_is_integer(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %d\n", I[i], J[i], (int)val[i]); - else if (mm_is_real(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]); - else if (mm_is_complex(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i], val[2 * i + 1]); - else { - if (f != stdout) - fclose(f); - return MM_UNSUPPORTED_TYPE; - } - - if (f != stdout) - fclose(f); - - return 0; -} - -/** - * Create a new copy of a string s. mm_strdup() is a common routine, but - * not part of ANSI C, so it is included here. Used by mm_typecode_to_str(). - * - */ -static char *mm_strdup(const char *s) -{ - size_t len = strlen(s); - char *s2 = (char *)malloc((len + 1) * sizeof(char)); - return strcpy(s2, s); -} - -char *mm_typecode_to_str(MM_typecode matcode) -{ - char buffer[MM_MAX_LINE_LENGTH]; - char *types[4]; - // char *mm_strdup(const char *); - // int error =0; - - /* check for MTX type */ - if (mm_is_matrix(matcode)) - types[0] = MM_MTX_STR; - else - return NULL; // error=1; - - /* check for CRD or ARR matrix */ - if (mm_is_sparse(matcode)) - types[1] = MM_SPARSE_STR; - else if (mm_is_dense(matcode)) - types[1] = MM_DENSE_STR; - else - return NULL; - - /* check for element data type */ - if (mm_is_real(matcode)) - types[2] = MM_REAL_STR; - else if (mm_is_complex(matcode)) - types[2] = MM_COMPLEX_STR; - else if (mm_is_pattern(matcode)) - types[2] = MM_PATTERN_STR; - else if (mm_is_integer(matcode)) - types[2] = MM_INT_STR; - else - return NULL; - - - /* check for symmetry type */ - if (mm_is_general(matcode)) - types[3] = MM_GENERAL_STR; - else if (mm_is_symmetric(matcode)) - types[3] = MM_SYMM_STR; - else if (mm_is_hermitian(matcode)) - types[3] = MM_HERM_STR; - else if (mm_is_skew(matcode)) - types[3] = MM_SKEW_STR; - else - return NULL; - - sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]); - return mm_strdup(buffer); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio.h b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio.h deleted file mode 100644 index a05f6063..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio.h +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -#ifndef MM_IO_H -#define MM_IO_H - -#include - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - -#define MM_MAX_LINE_LENGTH 1025 -#define MatrixMarketBanner "%%MatrixMarket" -#define MM_MAX_TOKEN_LENGTH 64 - - typedef char MM_typecode[4]; - - char *mm_typecode_to_str(MM_typecode matcode); - - int mm_read_banner(FILE *f, MM_typecode *matcode); - int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz); - int mm_read_mtx_array_size(FILE *f, int *M, int *N); - - int mm_write_banner(FILE *f, MM_typecode matcode); - int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz); - int mm_write_mtx_array_size(FILE *f, int M, int N); - - - /********************* MM_typecode query fucntions ***************************/ - -#define mm_is_matrix(typecode) ((typecode)[0] == 'M') - -#define mm_is_sparse(typecode) ((typecode)[1] == 'C') -#define mm_is_coordinate(typecode) ((typecode)[1] == 'C') -#define mm_is_dense(typecode) ((typecode)[1] == 'A') -#define mm_is_array(typecode) ((typecode)[1] == 'A') - -#define mm_is_complex(typecode) ((typecode)[2] == 'C') -#define mm_is_real(typecode) ((typecode)[2] == 'R') -#define mm_is_pattern(typecode) ((typecode)[2] == 'P') -#define mm_is_integer(typecode) ((typecode)[2] == 'I') - -#define mm_is_symmetric(typecode) ((typecode)[3] == 'S') -#define mm_is_general(typecode) ((typecode)[3] == 'G') -#define mm_is_skew(typecode) ((typecode)[3] == 'K') -#define mm_is_hermitian(typecode) ((typecode)[3] == 'H') - - int mm_is_valid(MM_typecode matcode); /* too complex for a macro */ - - - /********************* MM_typecode modify fucntions ***************************/ - -#define mm_set_matrix(typecode) ((*typecode)[0] = 'M') -#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C') -#define mm_set_array(typecode) ((*typecode)[1] = 'A') -#define mm_set_dense(typecode) mm_set_array(typecode) -#define mm_set_sparse(typecode) mm_set_coordinate(typecode) - -#define mm_set_complex(typecode) ((*typecode)[2] = 'C') -#define mm_set_real(typecode) ((*typecode)[2] = 'R') -#define mm_set_pattern(typecode) ((*typecode)[2] = 'P') -#define mm_set_integer(typecode) ((*typecode)[2] = 'I') - - -#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S') -#define mm_set_general(typecode) ((*typecode)[3] = 'G') -#define mm_set_skew(typecode) ((*typecode)[3] = 'K') -#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H') - -#define mm_clear_typecode(typecode) ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', (*typecode)[3] = 'G') - -#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode) - - - /********************* Matrix Market error codes ***************************/ - - -#define MM_COULD_NOT_READ_FILE 11 -#define MM_PREMATURE_EOF 12 -#define MM_NOT_MTX 13 -#define MM_NO_HEADER 14 -#define MM_UNSUPPORTED_TYPE 15 -#define MM_LINE_TOO_LONG 16 -#define MM_COULD_NOT_WRITE_FILE 17 - - - /******************** Matrix Market internal definitions ******************** - - MM_matrix_typecode: 4-character sequence - - ojbect sparse/ data storage - dense type scheme - - string position: [0] [1] [2] [3] - - Matrix typecode: M(atrix) C(oord) R(eal) G(eneral) - A(array) C(omplex) H(ermitian) - P(attern) S(ymmetric) - I(nteger) K(kew) - - ***********************************************************************/ - -#define MM_MTX_STR "matrix" -#define MM_ARRAY_STR "array" -#define MM_DENSE_STR "array" -#define MM_COORDINATE_STR "coordinate" -#define MM_SPARSE_STR "coordinate" -#define MM_COMPLEX_STR "complex" -#define MM_REAL_STR "real" -#define MM_INT_STR "integer" -#define MM_GENERAL_STR "general" -#define MM_SYMM_STR "symmetric" -#define MM_HERM_STR "hermitian" -#define MM_SKEW_STR "skew-symmetric" -#define MM_PATTERN_STR "pattern" - - - /* high level routines */ - int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode); - - int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img, MM_typecode matcode); - - int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio_wrapper.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio_wrapper.cpp deleted file mode 100644 index f6f6c8e4..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LinearSolver/mmio_wrapper.cpp +++ /dev/null @@ -1,473 +0,0 @@ - -#include -#include -#include -#include - -#include "mmio.h" - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -/* various __inline__ __device__ function to initialize a T_ELEM */ -template __inline__ T_ELEM cuGet(int); -template <> __inline__ float cuGet(int x) { return float(x); } - -template <> __inline__ double cuGet(int x) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(int x) { return (make_cuDoubleComplex(double(x), 0.0)); } - - -template __inline__ T_ELEM cuGet(int, int); -template <> __inline__ float cuGet(int x, int y) { return float(x); } - -template <> __inline__ double cuGet(int x, int y) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x, int y) { return make_cuComplex(float(x), float(y)); } - -template <> __inline__ cuDoubleComplex cuGet(int x, int y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(float); -template <> __inline__ float cuGet(float x) { return float(x); } - -template <> __inline__ double cuGet(float x) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(float x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(float, float); -template <> __inline__ float cuGet(float x, float y) { return float(x); } - -template <> __inline__ double cuGet(float x, float y) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x, float y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(float x, float y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(double); -template <> __inline__ float cuGet(double x) { return float(x); } - -template <> __inline__ double cuGet(double x) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(double x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(double, double); -template <> __inline__ float cuGet(double x, double y) { return float(x); } - -template <> __inline__ double cuGet(double x, double y) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x, double y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(double x, double y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -static void compress_index(const int *Ind, int nnz, int m, int *Ptr, int base) -{ - int i; - - /* initialize everything to zero */ - for (i = 0; i < m + 1; i++) { - Ptr[i] = 0; - } - /* count elements in every row */ - Ptr[0] = base; - for (i = 0; i < nnz; i++) { - Ptr[Ind[i] + (1 - base)]++; - } - /* add all the values */ - for (i = 0; i < m; i++) { - Ptr[i + 1] += Ptr[i]; - } -} - - -struct cooFormat -{ - int i; - int j; - int p; // permutation -}; - - -int cmp_cooFormat_csr(struct cooFormat *s, struct cooFormat *t) -{ - if (s->i < t->i) { - return -1; - } - else if (s->i > t->i) { - return 1; - } - else { - return s->j - t->j; - } -} - -int cmp_cooFormat_csc(struct cooFormat *s, struct cooFormat *t) -{ - if (s->j < t->j) { - return -1; - } - else if (s->j > t->j) { - return 1; - } - else { - return s->i - t->i; - } -} - -typedef int (*FUNPTR)(const void *, const void *); -typedef int (*FUNPTR2)(struct cooFormat *s, struct cooFormat *t); - -static FUNPTR2 fptr_array[2] = { - cmp_cooFormat_csr, - cmp_cooFormat_csc, -}; - - -static int verify_pattern(int m, int nnz, int *csrRowPtr, int *csrColInd) -{ - int i, col, start, end, base_index; - int error_found = 0; - - if (nnz != (csrRowPtr[m] - csrRowPtr[0])) { - fprintf(stderr, - "Error (nnz check failed): (csrRowPtr[%d]=%d - csrRowPtr[%d]=%d) != (nnz=%d)\n", - 0, - csrRowPtr[0], - m, - csrRowPtr[m], - nnz); - error_found = 1; - } - - base_index = csrRowPtr[0]; - if ((0 != base_index) && (1 != base_index)) { - fprintf(stderr, "Error (base index check failed): base index = %d\n", base_index); - error_found = 1; - } - - for (i = 0; (!error_found) && (i < m); i++) { - start = csrRowPtr[i] - base_index; - end = csrRowPtr[i + 1] - base_index; - if (start > end) { - fprintf(stderr, - "Error (corrupted row): csrRowPtr[%d] (=%d) > csrRowPtr[%d] (=%d)\n", - i, - start + base_index, - i + 1, - end + base_index); - error_found = 1; - } - for (col = start; col < end; col++) { - if (csrColInd[col] < base_index) { - fprintf(stderr, "Error (column vs. base index check failed): csrColInd[%d] < %d\n", col, base_index); - error_found = 1; - } - if ((col < (end - 1)) && (csrColInd[col] >= csrColInd[col + 1])) { - fprintf( - stderr, - "Error (sorting of the column indecis check failed): (csrColInd[%d]=%d) >= (csrColInd[%d]=%d)\n", - col, - csrColInd[col], - col + 1, - csrColInd[col + 1]); - error_found = 1; - } - } - } - return error_found; -} - - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix) -{ - MM_typecode matcode; - double *tempVal; - int *tempRowInd, *tempColInd; - double *tval; - int *trow, *tcol; - int *csrRowPtr, *cscColPtr; - int i, j, error, base, count; - struct cooFormat *work; - - /* read the matrix */ - error = mm_read_mtx_crd(filename, m, n, nnz, &trow, &tcol, &tval, &matcode); - if (error) { - fprintf(stderr, "!!!! can not open file: '%s'\n", filename); - return 1; - } - - /* start error checking */ - if (mm_is_complex(matcode) && ((elem_type != 'z') && (elem_type != 'c'))) { - fprintf(stderr, "!!!! complex matrix requires type 'z' or 'c'\n"); - return 1; - } - - if (mm_is_dense(matcode) || mm_is_array(matcode) || mm_is_pattern(matcode) /*|| mm_is_integer(matcode)*/) { - fprintf(stderr, "!!!! dense, array, pattern and integer matrices are not supported\n"); - return 1; - } - - /* if necessary symmetrize the pattern (transform from triangular to full) */ - if ((extendSymMatrix) && (mm_is_symmetric(matcode) || mm_is_hermitian(matcode) || mm_is_skew(matcode))) { - // count number of non-diagonal elements - count = 0; - for (i = 0; i < (*nnz); i++) { - if (trow[i] != tcol[i]) { - count++; - } - } - // allocate space for the symmetrized matrix - tempRowInd = (int *)malloc((*nnz + count) * sizeof(int)); - tempColInd = (int *)malloc((*nnz + count) * sizeof(int)); - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal = (double *)malloc((*nnz + count) * sizeof(double)); - } - else { - tempVal = (double *)malloc(2 * (*nnz + count) * sizeof(double)); - } - // copy the elements regular and transposed locations - for (j = 0, i = 0; i < (*nnz); i++) { - tempRowInd[j] = trow[i]; - tempColInd[j] = tcol[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal[j] = tval[i]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - j++; - if (trow[i] != tcol[i]) { - tempRowInd[j] = tcol[i]; - tempColInd[j] = trow[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (mm_is_skew(matcode)) { - tempVal[j] = -tval[i]; - } - else { - tempVal[j] = tval[i]; - } - } - else { - if (mm_is_hermitian(matcode)) { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = -tval[2 * i + 1]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - } - j++; - } - } - (*nnz) += count; - // free temporary storage - free(trow); - free(tcol); - free(tval); - } - else { - tempRowInd = trow; - tempColInd = tcol; - tempVal = tval; - } - // life time of (trow, tcol, tval) is over. - // please use COO format (tempRowInd, tempColInd, tempVal) - - // use qsort to sort COO format - work = (struct cooFormat *)malloc(sizeof(struct cooFormat) * (*nnz)); - if (NULL == work) { - fprintf(stderr, "!!!! allocation error, malloc failed\n"); - return 1; - } - for (i = 0; i < (*nnz); i++) { - work[i].i = tempRowInd[i]; - work[i].j = tempColInd[i]; - work[i].p = i; // permutation is identity - } - - if (csrFormat) { - /* create row-major ordering of indices (sorted by row and within each row by column) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[0]); - } - else { - /* create column-major ordering of indices (sorted by column and within each column by row) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[1]); - } - - // (tempRowInd, tempColInd) is sorted either by row-major or by col-major - for (i = 0; i < (*nnz); i++) { - tempRowInd[i] = work[i].i; - tempColInd[i] = work[i].j; - } - - // setup base - // check if there is any row/col 0, if so base-0 - // check if there is any row/col equal to matrix dimension m/n, if so base-1 - int base0 = 0; - int base1 = 0; - for (i = 0; i < (*nnz); i++) { - const int row = tempRowInd[i]; - const int col = tempColInd[i]; - if ((0 == row) || (0 == col)) { - base0 = 1; - } - if ((*m == row) || (*n == col)) { - base1 = 1; - } - } - if (base0 && base1) { - printf("Error: input matrix is base-0 and base-1 \n"); - return 1; - } - - base = 0; - if (base1) { - base = 1; - } - - /* compress the appropriate indices */ - if (csrFormat) { - /* CSR format (assuming row-major format) */ - csrRowPtr = (int *)malloc(((*m) + 1) * sizeof(csrRowPtr[0])); - if (!csrRowPtr) - return 1; - compress_index(tempRowInd, *nnz, *m, csrRowPtr, base); - - *aRowInd = csrRowPtr; - *aColInd = (int *)malloc((*nnz) * sizeof(int)); - } - else { - /* CSC format (assuming column-major format) */ - cscColPtr = (int *)malloc(((*n) + 1) * sizeof(cscColPtr[0])); - if (!cscColPtr) - return 1; - compress_index(tempColInd, *nnz, *n, cscColPtr, base); - - *aColInd = cscColPtr; - *aRowInd = (int *)malloc((*nnz) * sizeof(int)); - } - - /* transfrom the matrix values of type double into one of the cusparse library types */ - *aVal = (T_ELEM *)malloc((*nnz) * sizeof(T_ELEM)); - - for (i = 0; i < (*nnz); i++) { - if (csrFormat) { - (*aColInd)[i] = tempColInd[i]; - } - else { - (*aRowInd)[i] = tempRowInd[i]; - } - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - (*aVal)[i] = cuGet(tempVal[work[i].p]); - } - else { - (*aVal)[i] = cuGet(tempVal[2 * work[i].p], tempVal[2 * work[i].p + 1]); - } - } - - /* check for corruption */ - int error_found; - if (csrFormat) { - error_found = verify_pattern(*m, *nnz, *aRowInd, *aColInd); - } - else { - error_found = verify_pattern(*n, *nnz, *aColInd, *aRowInd); - } - if (error_found) { - fprintf(stderr, "!!!! verify_pattern failed\n"); - return 1; - } - - /* cleanup and exit */ - free(work); - free(tempVal); - free(tempColInd); - free(tempRowInd); - - return 0; -} - - -/* specific instantiation */ -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - float **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - double **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuDoubleComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/CMakeLists.txt b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/CMakeLists.txt deleted file mode 100644 index 60f1b9ca..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/CMakeLists.txt +++ /dev/null @@ -1,58 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cuSolverSp_LowlevelCholesky LANGUAGES C CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for cuSolverSp_LowlevelCholesky -add_executable(cuSolverSp_LowlevelCholesky cuSolverSp_LowlevelCholesky.cpp mmio.c mmio_wrapper.cpp) - -target_compile_options(cuSolverSp_LowlevelCholesky PRIVATE $<$:--extended-lambda>) - -target_compile_features(cuSolverSp_LowlevelCholesky PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(cuSolverSp_LowlevelCholesky PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(cuSolverSp_LowlevelCholesky PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(cuSolverSp_LowlevelCholesky PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusolver -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverSp_LowlevelCholesky POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap2D_5pt_n100.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverSp_LowlevelCholesky POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap3D_7pt_n20.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/README.md b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/README.md deleted file mode 100644 index 623227d9..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# cuSolverSp_LowlevelCholesky - cuSolverSp LowlevelCholesky Solver - -## Description - -A CUDA Sample that demonstrates Cholesky factorization using cuSolverSP's low level APIs. - -## Key Concepts - -Linear Algebra, CUSOLVER Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuDoubleComplex, cuComplex - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaStreamDestroy, cudaFree, cudaMalloc, cudaStreamCreate - -## Dependencies needed to build/run -[CUSOLVER](../../../README.md#cusolver), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/cuSolverSp_LowlevelCholesky.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/cuSolverSp_LowlevelCholesky.cpp deleted file mode 100644 index baee4342..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/cuSolverSp_LowlevelCholesky.cpp +++ /dev/null @@ -1,446 +0,0 @@ -/* - * Copyright 2015 NVIDIA Corporation. All rights reserved. - * - * Please refer to the NVIDIA end user license agreement (EULA) associated - * with this source code for terms and conditions that govern your use of - * this software. Any use, reproduction, disclosure, or distribution of - * this software and related documentation outside the terms of the EULA - * is strictly prohibited. - * - */ - -#include -#include -#include -#include -#include -#include - -#include "cusolverSp.h" -#include "cusolverSp_LOWLEVEL_PREVIEW.h" -#include "helper_cuda.h" -#include "helper_cusolver.h" - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -void UsageSP(void) -{ - printf("\n"); - printf("-h : display this help\n"); - printf("-file= : filename containing a matrix in MM format\n"); - printf("-device= : if want to run on specific GPU\n"); - - exit(0); -} - -void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) -{ - memset(&opts, 0, sizeof(opts)); - - if (checkCmdLineFlag(argc, (const char **)argv, "-h")) { - UsageSP(); - } - - if (checkCmdLineFlag(argc, (const char **)argv, "file")) { - char *fileName = 0; - getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName); - - if (fileName) { - opts.sparse_mat_filename = fileName; - } - else { - printf("\nIncorrect filename passed to -file \n "); - UsageSP(); - } - } -} - - -int main(int argc, char *argv[]) -{ - struct testOpts opts; - cusolverSpHandle_t cusolverSpH = NULL; // reordering, permutation and 1st LU factorization - cusparseHandle_t cusparseH = NULL; // residual evaluation - cudaStream_t stream = NULL; - cusparseMatDescr_t descrA = NULL; // A is a base-0 general matrix - - csrcholInfoHost_t h_info = NULL; // opaque info structure for LU with parital pivoting - csrcholInfo_t d_info = NULL; // opaque info structure for LU with parital pivoting - - int rowsA = 0; // number of rows of A - int colsA = 0; // number of columns of A - int nnzA = 0; // number of nonzeros of A - int baseA = 0; // base index in CSR format - - // CSR(A) from I/O - int *h_csrRowPtrA = NULL; // n+1 - int *h_csrColIndA = NULL; // nnzA - double *h_csrValA = NULL; // nnzA - - double *h_x = NULL; // n, x = A \ b - double *h_b = NULL; // n, b = ones(m,1) - double *h_r = NULL; // n, r = b - A*x - - size_t size_internal = 0; - size_t size_chol = 0; // size of working space for csrlu - void *buffer_cpu = NULL; // working space for Cholesky - void *buffer_gpu = NULL; // working space for Cholesky - - int *d_csrRowPtrA = NULL; // n+1 - int *d_csrColIndA = NULL; // nnzA - double *d_csrValA = NULL; // nnzA - double *d_x = NULL; // n, x = A \ b - double *d_b = NULL; // n, a copy of h_b - double *d_r = NULL; // n, r = b - A*x - - // the constants used in residual evaluation, r = b - A*x - const double minus_one = -1.0; - const double one = 1.0; - // the constant used in cusolverSp - // singularity is -1 if A is invertible under tol - // tol determines the condition of singularity - int singularity = 0; - const double tol = 1.e-14; - - double x_inf = 0.0; // |x| - double r_inf = 0.0; // |r| - double A_inf = 0.0; // |A| - int errors = 0; - - parseCommandLineArguments(argc, argv, opts); - - findCudaDevice(argc, (const char **)argv); - - if (opts.sparse_mat_filename == NULL) { - opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n100.mtx", argv[0]); - if (opts.sparse_mat_filename != NULL) - printf("Using default input file [%s]\n", opts.sparse_mat_filename); - else - printf("Could not find lap2D_5pt_n100.mtx\n"); - } - else { - printf("Using input file [%s]\n", opts.sparse_mat_filename); - } - - printf("step 1: read matrix market format\n"); - - if (opts.sparse_mat_filename) { - if (loadMMSparseMatrix(opts.sparse_mat_filename, - 'd', - true, - &rowsA, - &colsA, - &nnzA, - &h_csrValA, - &h_csrRowPtrA, - &h_csrColIndA, - true)) { - return 1; - } - baseA = h_csrRowPtrA[0]; // baseA = {0,1} - } - else { - fprintf(stderr, "Error: input matrix is not provided\n"); - return 1; - } - - if (rowsA != colsA) { - fprintf(stderr, "Error: only support square matrix\n"); - return 1; - } - - printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA); - - checkCudaErrors(cusolverSpCreate(&cusolverSpH)); - - checkCudaErrors(cusparseCreate(&cusparseH)); - - checkCudaErrors(cudaStreamCreate(&stream)); - - checkCudaErrors(cusolverSpSetStream(cusolverSpH, stream)); - - checkCudaErrors(cusparseSetStream(cusparseH, stream)); - - checkCudaErrors(cusparseCreateMatDescr(&descrA)); - - checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL)); - if (baseA) { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE)); - } - else { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO)); - } - - h_x = (double *)malloc(sizeof(double) * colsA); - h_b = (double *)malloc(sizeof(double) * rowsA); - h_r = (double *)malloc(sizeof(double) * rowsA); - - assert(NULL != h_x); - assert(NULL != h_b); - assert(NULL != h_r); - - checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int) * (rowsA + 1))); - checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrValA, sizeof(double) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA)); - - for (int row = 0; row < rowsA; row++) { - h_b[row] = 1.0; - } - - printf("step 2: create opaque info structure\n"); - checkCudaErrors(cusolverSpCreateCsrcholInfoHost(&h_info)); - - printf("step 3: analyze chol(A) to know structure of L\n"); - checkCudaErrors( - cusolverSpXcsrcholAnalysisHost(cusolverSpH, rowsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_info)); - - printf("step 4: workspace for chol(A)\n"); - checkCudaErrors(cusolverSpDcsrcholBufferInfoHost( - cusolverSpH, rowsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA, h_info, &size_internal, &size_chol)); - - if (buffer_cpu) { - free(buffer_cpu); - } - buffer_cpu = (void *)malloc(sizeof(char) * size_chol); - assert(NULL != buffer_cpu); - - printf("step 5: compute A = L*L^T \n"); - checkCudaErrors(cusolverSpDcsrcholFactorHost( - cusolverSpH, rowsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA, h_info, buffer_cpu)); - - printf("step 6: check if the matrix is singular \n"); - checkCudaErrors(cusolverSpDcsrcholZeroPivotHost(cusolverSpH, h_info, tol, &singularity)); - - if (0 <= singularity) { - fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity); - return 1; - } - - printf("step 7: solve A*x = b \n"); - checkCudaErrors(cusolverSpDcsrcholSolveHost(cusolverSpH, rowsA, h_b, h_x, h_info, buffer_cpu)); - - printf("step 8: evaluate residual r = b - A*x (result on CPU)\n"); - // use GPU gemv to compute r = b - A*x - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_x, h_x, sizeof(double) * colsA, cudaMemcpyHostToDevice)); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - if (baseA) { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ONE, - CUDA_R_64F)); - } - else { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_64F)); - } - - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F)); - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA); - - printf("(CPU) |b - A*x| = %E \n", r_inf); - printf("(CPU) |A| = %E \n", A_inf); - printf("(CPU) |x| = %E \n", x_inf); - printf("(CPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - printf("step 9: create opaque info structure\n"); - checkCudaErrors(cusolverSpCreateCsrcholInfo(&d_info)); - - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_b, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - printf("step 10: analyze chol(A) to know structure of L\n"); - checkCudaErrors(cusolverSpXcsrcholAnalysis(cusolverSpH, rowsA, nnzA, descrA, d_csrRowPtrA, d_csrColIndA, d_info)); - - printf("step 11: workspace for chol(A)\n"); - checkCudaErrors(cusolverSpDcsrcholBufferInfo( - cusolverSpH, rowsA, nnzA, descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA, d_info, &size_internal, &size_chol)); - - if (buffer_gpu) { - checkCudaErrors(cudaFree(buffer_gpu)); - } - checkCudaErrors(cudaMalloc(&buffer_gpu, sizeof(char) * size_chol)); - - printf("step 12: compute A = L*L^T \n"); - checkCudaErrors(cusolverSpDcsrcholFactor( - cusolverSpH, rowsA, nnzA, descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA, d_info, buffer_gpu)); - - printf("step 13: check if the matrix is singular \n"); - checkCudaErrors(cusolverSpDcsrcholZeroPivot(cusolverSpH, d_info, tol, &singularity)); - - if (0 <= singularity) { - fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity); - return 1; - } - - printf("step 14: solve A*x = b \n"); - checkCudaErrors(cusolverSpDcsrcholSolve(cusolverSpH, rowsA, d_b, d_x, d_info, buffer_gpu)); - - checkCudaErrors(cudaMemcpy(d_r, h_b, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - r_inf = vec_norminf(rowsA, h_r); - - printf("(GPU) |b - A*x| = %E \n", r_inf); - printf("(GPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - - if (cusolverSpH) { - checkCudaErrors(cusolverSpDestroy(cusolverSpH)); - } - if (cusparseH) { - checkCudaErrors(cusparseDestroy(cusparseH)); - } - if (stream) { - checkCudaErrors(cudaStreamDestroy(stream)); - } - if (descrA) { - checkCudaErrors(cusparseDestroyMatDescr(descrA)); - } - if (h_info) { - checkCudaErrors(cusolverSpDestroyCsrcholInfoHost(h_info)); - } - if (d_info) { - checkCudaErrors(cusolverSpDestroyCsrcholInfo(d_info)); - } - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - - if (h_csrValA) { - free(h_csrValA); - } - if (h_csrRowPtrA) { - free(h_csrRowPtrA); - } - if (h_csrColIndA) { - free(h_csrColIndA); - } - - if (h_x) { - free(h_x); - } - if (h_b) { - free(h_b); - } - if (h_r) { - free(h_r); - } - - if (buffer_cpu) { - free(buffer_cpu); - } - if (buffer_gpu) { - checkCudaErrors(cudaFree(buffer_gpu)); - } - - if (d_csrValA) { - checkCudaErrors(cudaFree(d_csrValA)); - } - if (d_csrRowPtrA) { - checkCudaErrors(cudaFree(d_csrRowPtrA)); - } - if (d_csrColIndA) { - checkCudaErrors(cudaFree(d_csrColIndA)); - } - if (d_x) { - checkCudaErrors(cudaFree(d_x)); - } - if (d_b) { - checkCudaErrors(cudaFree(d_b)); - } - if (d_r) { - checkCudaErrors(cudaFree(d_r)); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/lap2D_5pt_n100.mtx b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/lap2D_5pt_n100.mtx deleted file mode 100644 index dae3c1eb..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/lap2D_5pt_n100.mtx +++ /dev/null @@ -1,29803 +0,0 @@ -%%MatrixMarket matrix coordinate real symmetric -% Generated 20-Nov-2014 -10000 10000 29800 -1 1 4 -2 1 -1 -101 1 -1 -2 2 4 -3 2 -1 -102 2 -1 -3 3 4 -4 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strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -#include "mmio.h" - -#include -#include -#include -#include - -int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_) -{ - FILE *f; - MM_typecode matcode; - int M, N, nz; - int i; - double *val; - int *I, *J; - - if ((f = fopen(fname, "r")) == NULL) - return -1; - - - if (mm_read_banner(f, &matcode) != 0) { - printf("mm_read_unsymetric: Could not process Matrix Market banner "); - printf(" in file [%s]\n", fname); - return -1; - } - - - if (!(mm_is_real(matcode) && mm_is_matrix(matcode) && mm_is_sparse(matcode))) { - fprintf(stderr, "Sorry, this application does not support "); - fprintf(stderr, "Market Market type: [%s]\n", mm_typecode_to_str(matcode)); - return -1; - } - - /* find out size of sparse matrix: M, N, nz .... */ - - if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) { - fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n"); - return -1; - } - - *M_ = M; - *N_ = N; - *nz_ = nz; - - /* reserve memory for matrices */ - - I = (int *)malloc(nz * sizeof(int)); - J = (int *)malloc(nz * sizeof(int)); - val = (double *)malloc(nz * sizeof(double)); - - *val_ = val; - *I_ = I; - *J_ = J; - - /* NOTE: when reading in doubles, ANSI C requires the use of the "l" */ - /* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */ - /* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */ - - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) { - return -1; - } - I[i]--; /* adjust from 1-based to 0-based */ - J[i]--; - } - fclose(f); - - return 0; -} - -int mm_is_valid(MM_typecode matcode) -{ - if (!mm_is_matrix(matcode)) - return 0; - if (mm_is_dense(matcode) && mm_is_pattern(matcode)) - return 0; - if (mm_is_real(matcode) && mm_is_hermitian(matcode)) - return 0; - if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) || mm_is_skew(matcode))) - return 0; - return 1; -} - -int mm_read_banner(FILE *f, MM_typecode *matcode) -{ - char line[MM_MAX_LINE_LENGTH]; - char banner[MM_MAX_TOKEN_LENGTH]; - char mtx[MM_MAX_TOKEN_LENGTH]; - char crd[MM_MAX_TOKEN_LENGTH]; - char data_type[MM_MAX_TOKEN_LENGTH]; - char storage_scheme[MM_MAX_TOKEN_LENGTH]; - char *p; - - - mm_clear_typecode(matcode); - - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - - if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type, storage_scheme) != 5) - return MM_PREMATURE_EOF; - - for (p = mtx; *p != '\0'; *p = tolower(*p), p++) - ; /* convert to lower case */ - for (p = crd; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = data_type; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++) - ; - - /* check for banner */ - if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0) - return MM_NO_HEADER; - - /* first field should be "mtx" */ - if (strcmp(mtx, MM_MTX_STR) != 0) - return MM_UNSUPPORTED_TYPE; - mm_set_matrix(matcode); - - - /* second field describes whether this is a sparse matrix (in coordinate - storage) or a dense array */ - - - if (strcmp(crd, MM_SPARSE_STR) == 0) - mm_set_sparse(matcode); - else if (strcmp(crd, MM_DENSE_STR) == 0) - mm_set_dense(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* third field */ - - if (strcmp(data_type, MM_REAL_STR) == 0) - mm_set_real(matcode); - else if (strcmp(data_type, MM_COMPLEX_STR) == 0) - mm_set_complex(matcode); - else if (strcmp(data_type, MM_PATTERN_STR) == 0) - mm_set_pattern(matcode); - else if (strcmp(data_type, MM_INT_STR) == 0) - mm_set_integer(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* fourth field */ - - if (strcmp(storage_scheme, MM_GENERAL_STR) == 0) - mm_set_general(matcode); - else if (strcmp(storage_scheme, MM_SYMM_STR) == 0) - mm_set_symmetric(matcode); - else if (strcmp(storage_scheme, MM_HERM_STR) == 0) - mm_set_hermitian(matcode); - else if (strcmp(storage_scheme, MM_SKEW_STR) == 0) - mm_set_skew(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - return 0; -} - -int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) -{ - if (fprintf(f, "%d %d %d\n", M, N, nz) != 3) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - - /* set return null parameter values, in case we exit with errors */ - *M = *N = *nz = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d %d", M, N, nz) == 3) - return 0; - - else - do { - num_items_read = fscanf(f, "%d %d %d", M, N, nz); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 3); - - return 0; -} - - -int mm_read_mtx_array_size(FILE *f, int *M, int *N) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - /* set return null parameter values, in case we exit with errors */ - *M = *N = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d", M, N) == 2) - return 0; - - else /* we have a blank line */ - do { - num_items_read = fscanf(f, "%d %d", M, N); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 2); - - return 0; -} - -int mm_write_mtx_array_size(FILE *f, int M, int N) -{ - if (fprintf(f, "%d %d\n", M, N) != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - - -/*-------------------------------------------------------------------------*/ - -/******************************************************************/ -/* use when I[], J[], and val[]J, and val[] are already allocated */ -/******************************************************************/ - -int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - int i; - if (mm_is_complex(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i], &val[2 * i + 1]) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) - return MM_PREMATURE_EOF; - } - } - - else if (mm_is_pattern(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag, MM_typecode matcode) -{ - if (mm_is_complex(matcode)) { - if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) - return MM_PREMATURE_EOF; - } - - else if (mm_is_pattern(matcode)) { - if (fscanf(f, "%d %d", I, J) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - - -/************************************************************************ - mm_read_mtx_crd() fills M, N, nz, array of values, and return - type code, e.g. 'MCRS' - - if matrix is complex, values[] is of size 2*nz, - (nz pairs of real/imaginary values) -************************************************************************/ - -int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode) -{ - int ret_code; - FILE *f; - - if (strcmp(fname, "stdin") == 0) - f = stdin; - else if ((f = fopen(fname, "r")) == NULL) - return MM_COULD_NOT_READ_FILE; - - - if ((ret_code = mm_read_banner(f, matcode)) != 0) - return ret_code; - - if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) && mm_is_matrix(*matcode))) - return MM_UNSUPPORTED_TYPE; - - if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) - return ret_code; - - - *I = (int *)malloc(*nz * sizeof(int)); - *J = (int *)malloc(*nz * sizeof(int)); - *val = NULL; - - if (mm_is_complex(*matcode)) { - *val = (double *)malloc(*nz * 2 * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - else if (mm_is_real(*matcode) || mm_is_integer(*matcode)) { - *val = (double *)malloc(*nz * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - else if (mm_is_pattern(*matcode)) { - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - if (f != stdin) - fclose(f); - return 0; -} - -int mm_write_banner(FILE *f, MM_typecode matcode) -{ - char *str = mm_typecode_to_str(matcode); - int ret_code; - - ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str); - free(str); - if (ret_code != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - FILE *f; - int i; - - if (strcmp(fname, "stdout") == 0) - f = stdout; - else if ((f = fopen(fname, "w")) == NULL) - return MM_COULD_NOT_WRITE_FILE; - - /* print banner followed by typecode */ - fprintf(f, "%s ", MatrixMarketBanner); - fprintf(f, "%s\n", mm_typecode_to_str(matcode)); - - /* print matrix sizes and nonzeros */ - fprintf(f, "%d %d %d\n", M, N, nz); - - /* print values */ - if (mm_is_pattern(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d\n", I[i], J[i]); - else if (mm_is_integer(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %d\n", I[i], J[i], (int)val[i]); - else if (mm_is_real(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]); - else if (mm_is_complex(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i], val[2 * i + 1]); - else { - if (f != stdout) - fclose(f); - return MM_UNSUPPORTED_TYPE; - } - - if (f != stdout) - fclose(f); - - return 0; -} - -/** - * Create a new copy of a string s. mm_strdup() is a common routine, but - * not part of ANSI C, so it is included here. Used by mm_typecode_to_str(). - * - */ -static char *mm_strdup(const char *s) -{ - size_t len = strlen(s); - char *s2 = (char *)malloc((len + 1) * sizeof(char)); - return strcpy(s2, s); -} - -char *mm_typecode_to_str(MM_typecode matcode) -{ - char buffer[MM_MAX_LINE_LENGTH]; - char *types[4]; - // char *mm_strdup(const char *); - // int error =0; - - /* check for MTX type */ - if (mm_is_matrix(matcode)) - types[0] = MM_MTX_STR; - else - return NULL; // error=1; - - /* check for CRD or ARR matrix */ - if (mm_is_sparse(matcode)) - types[1] = MM_SPARSE_STR; - else if (mm_is_dense(matcode)) - types[1] = MM_DENSE_STR; - else - return NULL; - - /* check for element data type */ - if (mm_is_real(matcode)) - types[2] = MM_REAL_STR; - else if (mm_is_complex(matcode)) - types[2] = MM_COMPLEX_STR; - else if (mm_is_pattern(matcode)) - types[2] = MM_PATTERN_STR; - else if (mm_is_integer(matcode)) - types[2] = MM_INT_STR; - else - return NULL; - - - /* check for symmetry type */ - if (mm_is_general(matcode)) - types[3] = MM_GENERAL_STR; - else if (mm_is_symmetric(matcode)) - types[3] = MM_SYMM_STR; - else if (mm_is_hermitian(matcode)) - types[3] = MM_HERM_STR; - else if (mm_is_skew(matcode)) - types[3] = MM_SKEW_STR; - else - return NULL; - - sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]); - return mm_strdup(buffer); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio.h b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio.h deleted file mode 100644 index a05f6063..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio.h +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -#ifndef MM_IO_H -#define MM_IO_H - -#include - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - -#define MM_MAX_LINE_LENGTH 1025 -#define MatrixMarketBanner "%%MatrixMarket" -#define MM_MAX_TOKEN_LENGTH 64 - - typedef char MM_typecode[4]; - - char *mm_typecode_to_str(MM_typecode matcode); - - int mm_read_banner(FILE *f, MM_typecode *matcode); - int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz); - int mm_read_mtx_array_size(FILE *f, int *M, int *N); - - int mm_write_banner(FILE *f, MM_typecode matcode); - int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz); - int mm_write_mtx_array_size(FILE *f, int M, int N); - - - /********************* MM_typecode query fucntions ***************************/ - -#define mm_is_matrix(typecode) ((typecode)[0] == 'M') - -#define mm_is_sparse(typecode) ((typecode)[1] == 'C') -#define mm_is_coordinate(typecode) ((typecode)[1] == 'C') -#define mm_is_dense(typecode) ((typecode)[1] == 'A') -#define mm_is_array(typecode) ((typecode)[1] == 'A') - -#define mm_is_complex(typecode) ((typecode)[2] == 'C') -#define mm_is_real(typecode) ((typecode)[2] == 'R') -#define mm_is_pattern(typecode) ((typecode)[2] == 'P') -#define mm_is_integer(typecode) ((typecode)[2] == 'I') - -#define mm_is_symmetric(typecode) ((typecode)[3] == 'S') -#define mm_is_general(typecode) ((typecode)[3] == 'G') -#define mm_is_skew(typecode) ((typecode)[3] == 'K') -#define mm_is_hermitian(typecode) ((typecode)[3] == 'H') - - int mm_is_valid(MM_typecode matcode); /* too complex for a macro */ - - - /********************* MM_typecode modify fucntions ***************************/ - -#define mm_set_matrix(typecode) ((*typecode)[0] = 'M') -#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C') -#define mm_set_array(typecode) ((*typecode)[1] = 'A') -#define mm_set_dense(typecode) mm_set_array(typecode) -#define mm_set_sparse(typecode) mm_set_coordinate(typecode) - -#define mm_set_complex(typecode) ((*typecode)[2] = 'C') -#define mm_set_real(typecode) ((*typecode)[2] = 'R') -#define mm_set_pattern(typecode) ((*typecode)[2] = 'P') -#define mm_set_integer(typecode) ((*typecode)[2] = 'I') - - -#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S') -#define mm_set_general(typecode) ((*typecode)[3] = 'G') -#define mm_set_skew(typecode) ((*typecode)[3] = 'K') -#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H') - -#define mm_clear_typecode(typecode) ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', (*typecode)[3] = 'G') - -#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode) - - - /********************* Matrix Market error codes ***************************/ - - -#define MM_COULD_NOT_READ_FILE 11 -#define MM_PREMATURE_EOF 12 -#define MM_NOT_MTX 13 -#define MM_NO_HEADER 14 -#define MM_UNSUPPORTED_TYPE 15 -#define MM_LINE_TOO_LONG 16 -#define MM_COULD_NOT_WRITE_FILE 17 - - - /******************** Matrix Market internal definitions ******************** - - MM_matrix_typecode: 4-character sequence - - ojbect sparse/ data storage - dense type scheme - - string position: [0] [1] [2] [3] - - Matrix typecode: M(atrix) C(oord) R(eal) G(eneral) - A(array) C(omplex) H(ermitian) - P(attern) S(ymmetric) - I(nteger) K(kew) - - ***********************************************************************/ - -#define MM_MTX_STR "matrix" -#define MM_ARRAY_STR "array" -#define MM_DENSE_STR "array" -#define MM_COORDINATE_STR "coordinate" -#define MM_SPARSE_STR "coordinate" -#define MM_COMPLEX_STR "complex" -#define MM_REAL_STR "real" -#define MM_INT_STR "integer" -#define MM_GENERAL_STR "general" -#define MM_SYMM_STR "symmetric" -#define MM_HERM_STR "hermitian" -#define MM_SKEW_STR "skew-symmetric" -#define MM_PATTERN_STR "pattern" - - - /* high level routines */ - int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode); - - int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img, MM_typecode matcode); - - int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio_wrapper.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio_wrapper.cpp deleted file mode 100644 index f6f6c8e4..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelCholesky/mmio_wrapper.cpp +++ /dev/null @@ -1,473 +0,0 @@ - -#include -#include -#include -#include - -#include "mmio.h" - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -/* various __inline__ __device__ function to initialize a T_ELEM */ -template __inline__ T_ELEM cuGet(int); -template <> __inline__ float cuGet(int x) { return float(x); } - -template <> __inline__ double cuGet(int x) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(int x) { return (make_cuDoubleComplex(double(x), 0.0)); } - - -template __inline__ T_ELEM cuGet(int, int); -template <> __inline__ float cuGet(int x, int y) { return float(x); } - -template <> __inline__ double cuGet(int x, int y) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x, int y) { return make_cuComplex(float(x), float(y)); } - -template <> __inline__ cuDoubleComplex cuGet(int x, int y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(float); -template <> __inline__ float cuGet(float x) { return float(x); } - -template <> __inline__ double cuGet(float x) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(float x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(float, float); -template <> __inline__ float cuGet(float x, float y) { return float(x); } - -template <> __inline__ double cuGet(float x, float y) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x, float y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(float x, float y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -template __inline__ T_ELEM cuGet(double); -template <> __inline__ float cuGet(double x) { return float(x); } - -template <> __inline__ double cuGet(double x) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(double x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - - -template __inline__ T_ELEM cuGet(double, double); -template <> __inline__ float cuGet(double x, double y) { return float(x); } - -template <> __inline__ double cuGet(double x, double y) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x, double y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(double x, double y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - - -static void compress_index(const int *Ind, int nnz, int m, int *Ptr, int base) -{ - int i; - - /* initialize everything to zero */ - for (i = 0; i < m + 1; i++) { - Ptr[i] = 0; - } - /* count elements in every row */ - Ptr[0] = base; - for (i = 0; i < nnz; i++) { - Ptr[Ind[i] + (1 - base)]++; - } - /* add all the values */ - for (i = 0; i < m; i++) { - Ptr[i + 1] += Ptr[i]; - } -} - - -struct cooFormat -{ - int i; - int j; - int p; // permutation -}; - - -int cmp_cooFormat_csr(struct cooFormat *s, struct cooFormat *t) -{ - if (s->i < t->i) { - return -1; - } - else if (s->i > t->i) { - return 1; - } - else { - return s->j - t->j; - } -} - -int cmp_cooFormat_csc(struct cooFormat *s, struct cooFormat *t) -{ - if (s->j < t->j) { - return -1; - } - else if (s->j > t->j) { - return 1; - } - else { - return s->i - t->i; - } -} - -typedef int (*FUNPTR)(const void *, const void *); -typedef int (*FUNPTR2)(struct cooFormat *s, struct cooFormat *t); - -static FUNPTR2 fptr_array[2] = { - cmp_cooFormat_csr, - cmp_cooFormat_csc, -}; - - -static int verify_pattern(int m, int nnz, int *csrRowPtr, int *csrColInd) -{ - int i, col, start, end, base_index; - int error_found = 0; - - if (nnz != (csrRowPtr[m] - csrRowPtr[0])) { - fprintf(stderr, - "Error (nnz check failed): (csrRowPtr[%d]=%d - csrRowPtr[%d]=%d) != (nnz=%d)\n", - 0, - csrRowPtr[0], - m, - csrRowPtr[m], - nnz); - error_found = 1; - } - - base_index = csrRowPtr[0]; - if ((0 != base_index) && (1 != base_index)) { - fprintf(stderr, "Error (base index check failed): base index = %d\n", base_index); - error_found = 1; - } - - for (i = 0; (!error_found) && (i < m); i++) { - start = csrRowPtr[i] - base_index; - end = csrRowPtr[i + 1] - base_index; - if (start > end) { - fprintf(stderr, - "Error (corrupted row): csrRowPtr[%d] (=%d) > csrRowPtr[%d] (=%d)\n", - i, - start + base_index, - i + 1, - end + base_index); - error_found = 1; - } - for (col = start; col < end; col++) { - if (csrColInd[col] < base_index) { - fprintf(stderr, "Error (column vs. base index check failed): csrColInd[%d] < %d\n", col, base_index); - error_found = 1; - } - if ((col < (end - 1)) && (csrColInd[col] >= csrColInd[col + 1])) { - fprintf( - stderr, - "Error (sorting of the column indecis check failed): (csrColInd[%d]=%d) >= (csrColInd[%d]=%d)\n", - col, - csrColInd[col], - col + 1, - csrColInd[col + 1]); - error_found = 1; - } - } - } - return error_found; -} - - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix) -{ - MM_typecode matcode; - double *tempVal; - int *tempRowInd, *tempColInd; - double *tval; - int *trow, *tcol; - int *csrRowPtr, *cscColPtr; - int i, j, error, base, count; - struct cooFormat *work; - - /* read the matrix */ - error = mm_read_mtx_crd(filename, m, n, nnz, &trow, &tcol, &tval, &matcode); - if (error) { - fprintf(stderr, "!!!! can not open file: '%s'\n", filename); - return 1; - } - - /* start error checking */ - if (mm_is_complex(matcode) && ((elem_type != 'z') && (elem_type != 'c'))) { - fprintf(stderr, "!!!! complex matrix requires type 'z' or 'c'\n"); - return 1; - } - - if (mm_is_dense(matcode) || mm_is_array(matcode) || mm_is_pattern(matcode) /*|| mm_is_integer(matcode)*/) { - fprintf(stderr, "!!!! dense, array, pattern and integer matrices are not supported\n"); - return 1; - } - - /* if necessary symmetrize the pattern (transform from triangular to full) */ - if ((extendSymMatrix) && (mm_is_symmetric(matcode) || mm_is_hermitian(matcode) || mm_is_skew(matcode))) { - // count number of non-diagonal elements - count = 0; - for (i = 0; i < (*nnz); i++) { - if (trow[i] != tcol[i]) { - count++; - } - } - // allocate space for the symmetrized matrix - tempRowInd = (int *)malloc((*nnz + count) * sizeof(int)); - tempColInd = (int *)malloc((*nnz + count) * sizeof(int)); - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal = (double *)malloc((*nnz + count) * sizeof(double)); - } - else { - tempVal = (double *)malloc(2 * (*nnz + count) * sizeof(double)); - } - // copy the elements regular and transposed locations - for (j = 0, i = 0; i < (*nnz); i++) { - tempRowInd[j] = trow[i]; - tempColInd[j] = tcol[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal[j] = tval[i]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - j++; - if (trow[i] != tcol[i]) { - tempRowInd[j] = tcol[i]; - tempColInd[j] = trow[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (mm_is_skew(matcode)) { - tempVal[j] = -tval[i]; - } - else { - tempVal[j] = tval[i]; - } - } - else { - if (mm_is_hermitian(matcode)) { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = -tval[2 * i + 1]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - } - j++; - } - } - (*nnz) += count; - // free temporary storage - free(trow); - free(tcol); - free(tval); - } - else { - tempRowInd = trow; - tempColInd = tcol; - tempVal = tval; - } - // life time of (trow, tcol, tval) is over. - // please use COO format (tempRowInd, tempColInd, tempVal) - - // use qsort to sort COO format - work = (struct cooFormat *)malloc(sizeof(struct cooFormat) * (*nnz)); - if (NULL == work) { - fprintf(stderr, "!!!! allocation error, malloc failed\n"); - return 1; - } - for (i = 0; i < (*nnz); i++) { - work[i].i = tempRowInd[i]; - work[i].j = tempColInd[i]; - work[i].p = i; // permutation is identity - } - - if (csrFormat) { - /* create row-major ordering of indices (sorted by row and within each row by column) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[0]); - } - else { - /* create column-major ordering of indices (sorted by column and within each column by row) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[1]); - } - - // (tempRowInd, tempColInd) is sorted either by row-major or by col-major - for (i = 0; i < (*nnz); i++) { - tempRowInd[i] = work[i].i; - tempColInd[i] = work[i].j; - } - - // setup base - // check if there is any row/col 0, if so base-0 - // check if there is any row/col equal to matrix dimension m/n, if so base-1 - int base0 = 0; - int base1 = 0; - for (i = 0; i < (*nnz); i++) { - const int row = tempRowInd[i]; - const int col = tempColInd[i]; - if ((0 == row) || (0 == col)) { - base0 = 1; - } - if ((*m == row) || (*n == col)) { - base1 = 1; - } - } - if (base0 && base1) { - printf("Error: input matrix is base-0 and base-1 \n"); - return 1; - } - - base = 0; - if (base1) { - base = 1; - } - - /* compress the appropriate indices */ - if (csrFormat) { - /* CSR format (assuming row-major format) */ - csrRowPtr = (int *)malloc(((*m) + 1) * sizeof(csrRowPtr[0])); - if (!csrRowPtr) - return 1; - compress_index(tempRowInd, *nnz, *m, csrRowPtr, base); - - *aRowInd = csrRowPtr; - *aColInd = (int *)malloc((*nnz) * sizeof(int)); - } - else { - /* CSC format (assuming column-major format) */ - cscColPtr = (int *)malloc(((*n) + 1) * sizeof(cscColPtr[0])); - if (!cscColPtr) - return 1; - compress_index(tempColInd, *nnz, *n, cscColPtr, base); - - *aColInd = cscColPtr; - *aRowInd = (int *)malloc((*nnz) * sizeof(int)); - } - - /* transfrom the matrix values of type double into one of the cusparse library types */ - *aVal = (T_ELEM *)malloc((*nnz) * sizeof(T_ELEM)); - - for (i = 0; i < (*nnz); i++) { - if (csrFormat) { - (*aColInd)[i] = tempColInd[i]; - } - else { - (*aRowInd)[i] = tempRowInd[i]; - } - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - (*aVal)[i] = cuGet(tempVal[work[i].p]); - } - else { - (*aVal)[i] = cuGet(tempVal[2 * work[i].p], tempVal[2 * work[i].p + 1]); - } - } - - /* check for corruption */ - int error_found; - if (csrFormat) { - error_found = verify_pattern(*m, *nnz, *aRowInd, *aColInd); - } - else { - error_found = verify_pattern(*n, *nnz, *aColInd, *aRowInd); - } - if (error_found) { - fprintf(stderr, "!!!! verify_pattern failed\n"); - return 1; - } - - /* cleanup and exit */ - free(work); - free(tempVal); - free(tempColInd); - free(tempRowInd); - - return 0; -} - - -/* specific instantiation */ -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - float **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - double **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuDoubleComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/CMakeLists.txt b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/CMakeLists.txt deleted file mode 100644 index f37971f8..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/CMakeLists.txt +++ /dev/null @@ -1,61 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(cuSolverSp_LowlevelQR LANGUAGES C CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for cuSolverSp_LowlevelQR -add_executable(cuSolverSp_LowlevelQR cuSolverSp_LowlevelQR.cpp mmio.c mmio_wrapper.cpp) - -target_compile_options(cuSolverSp_LowlevelQR PRIVATE $<$:--extended-lambda>) - -target_compile_features(cuSolverSp_LowlevelQR PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(cuSolverSp_LowlevelQR PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(cuSolverSp_LowlevelQR PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(cuSolverSp_LowlevelQR PRIVATE - CUDA::cudart - CUDA::cublas - CUDA::cusolver -) - -# Copy data files to output directory -add_custom_command(TARGET cuSolverSp_LowlevelQR POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap2D_5pt_n32.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) -add_custom_command(TARGET cuSolverSp_LowlevelQR POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap2D_5pt_n100.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) -add_custom_command(TARGET cuSolverSp_LowlevelQR POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/lap3D_7pt_n20.mtx - ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/README.md b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/README.md deleted file mode 100644 index 4c3fa0e1..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# cuSolverSp_LowlevelQR - cuSolverSp Lowlevel QR Solver - -## Description - -A CUDA Sample that demonstrates QR factorization using cuSolverSP's low level APIs. - -## Key Concepts - -Linear Algebra, CUSOLVER Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuGet, cuDoubleComplex, cuComplex - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaStreamDestroy, cudaFree, cudaMalloc, cudaStreamCreate - -## Dependencies needed to build/run -[CUSOLVER](../../../README.md#cusolver), [CUSPARSE](../../../README.md#cusparse) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/cuSolverSp_LowlevelQR.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/cuSolverSp_LowlevelQR.cpp deleted file mode 100644 index b1e336b9..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/cuSolverSp_LowlevelQR.cpp +++ /dev/null @@ -1,490 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include -#include -#include - -#include "cusolverSp.h" -#include "cusolverSp_LOWLEVEL_PREVIEW.h" -#include "helper_cuda.h" -#include "helper_cusolver.h" - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -void UsageSP(void) -{ - printf("\n"); - printf("-h : display this help\n"); - printf("-file= : filename containing a matrix in MM format\n"); - printf("-device= : if want to run on specific GPU\n"); - - exit(0); -} - -void parseCommandLineArguments(int argc, char *argv[], struct testOpts &opts) -{ - memset(&opts, 0, sizeof(opts)); - - if (checkCmdLineFlag(argc, (const char **)argv, "-h")) { - UsageSP(); - } - - if (checkCmdLineFlag(argc, (const char **)argv, "file")) { - char *fileName = 0; - getCmdLineArgumentString(argc, (const char **)argv, "file", &fileName); - - if (fileName) { - opts.sparse_mat_filename = fileName; - } - else { - printf("\nIncorrect filename passed to -file \n "); - UsageSP(); - } - } -} - -int main(int argc, char *argv[]) -{ - struct testOpts opts; - cusolverSpHandle_t cusolverSpH = NULL; // reordering, permutation and 1st LU factorization - cusparseHandle_t cusparseH = NULL; // residual evaluation - cudaStream_t stream = NULL; - cusparseMatDescr_t descrA = NULL; // A is a base-0 general matrix - - csrqrInfoHost_t h_info = NULL; // opaque info structure for LU with parital pivoting - csrqrInfo_t d_info = NULL; // opaque info structure for LU with parital pivoting - - int rowsA = 0; // number of rows of A - int colsA = 0; // number of columns of A - int nnzA = 0; // number of nonzeros of A - int baseA = 0; // base index in CSR format - - // CSR(A) from I/O - int *h_csrRowPtrA = NULL; // n+1 - int *h_csrColIndA = NULL; // nnzA - double *h_csrValA = NULL; // nnzA - - double *h_x = NULL; // n, x = A \ b - double *h_b = NULL; // n, b = ones(m,1) - double *h_bcopy = NULL; // n, b = ones(m,1) - double *h_r = NULL; // n, r = b - A*x - - size_t size_internal = 0; - size_t size_chol = 0; // size of working space for csrlu - void *buffer_cpu = NULL; // working space for Cholesky - void *buffer_gpu = NULL; // working space for Cholesky - - int *d_csrRowPtrA = NULL; // n+1 - int *d_csrColIndA = NULL; // nnzA - double *d_csrValA = NULL; // nnzA - double *d_x = NULL; // n, x = A \ b - double *d_b = NULL; // n, a copy of h_b - double *d_r = NULL; // n, r = b - A*x - - // the constants used in residual evaluation, r = b - A*x - const double minus_one = -1.0; - const double one = 1.0; - const double zero = 0.0; - // the constant used in cusolverSp - // singularity is -1 if A is invertible under tol - // tol determines the condition of singularity - int singularity = 0; - const double tol = 1.e-14; - - double x_inf = 0.0; // |x| - double r_inf = 0.0; // |r| - double A_inf = 0.0; // |A| - - parseCommandLineArguments(argc, argv, opts); - - findCudaDevice(argc, (const char **)argv); - - if (opts.sparse_mat_filename == NULL) { - opts.sparse_mat_filename = sdkFindFilePath("lap2D_5pt_n32.mtx", argv[0]); - if (opts.sparse_mat_filename != NULL) - printf("Using default input file [%s]\n", opts.sparse_mat_filename); - else - printf("Could not find lap2D_5pt_n32.mtx\n"); - } - else { - printf("Using input file [%s]\n", opts.sparse_mat_filename); - } - - printf("step 1: read matrix market format\n"); - - if (opts.sparse_mat_filename) { - if (loadMMSparseMatrix(opts.sparse_mat_filename, - 'd', - true, - &rowsA, - &colsA, - &nnzA, - &h_csrValA, - &h_csrRowPtrA, - &h_csrColIndA, - true)) { - return 1; - } - baseA = h_csrRowPtrA[0]; // baseA = {0,1} - } - else { - fprintf(stderr, "Error: input matrix is not provided\n"); - return 1; - } - - if (rowsA != colsA) { - fprintf(stderr, "Error: only support square matrix\n"); - return 1; - } - - printf("sparse matrix A is %d x %d with %d nonzeros, base=%d\n", rowsA, colsA, nnzA, baseA); - - checkCudaErrors(cusolverSpCreate(&cusolverSpH)); - checkCudaErrors(cusparseCreate(&cusparseH)); - checkCudaErrors(cudaStreamCreate(&stream)); - checkCudaErrors(cusolverSpSetStream(cusolverSpH, stream)); - checkCudaErrors(cusparseSetStream(cusparseH, stream)); - - checkCudaErrors(cusparseCreateMatDescr(&descrA)); - - checkCudaErrors(cusparseSetMatType(descrA, CUSPARSE_MATRIX_TYPE_GENERAL)); - - if (baseA) { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ONE)); - } - else { - checkCudaErrors(cusparseSetMatIndexBase(descrA, CUSPARSE_INDEX_BASE_ZERO)); - } - - h_x = (double *)malloc(sizeof(double) * colsA); - h_b = (double *)malloc(sizeof(double) * rowsA); - h_bcopy = (double *)malloc(sizeof(double) * rowsA); - h_r = (double *)malloc(sizeof(double) * rowsA); - - assert(NULL != h_x); - assert(NULL != h_b); - assert(NULL != h_bcopy); - assert(NULL != h_r); - - checkCudaErrors(cudaMalloc((void **)&d_csrRowPtrA, sizeof(int) * (rowsA + 1))); - checkCudaErrors(cudaMalloc((void **)&d_csrColIndA, sizeof(int) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_csrValA, sizeof(double) * nnzA)); - checkCudaErrors(cudaMalloc((void **)&d_x, sizeof(double) * colsA)); - checkCudaErrors(cudaMalloc((void **)&d_b, sizeof(double) * rowsA)); - checkCudaErrors(cudaMalloc((void **)&d_r, sizeof(double) * rowsA)); - - for (int row = 0; row < rowsA; row++) { - h_b[row] = 1.0; - } - - memcpy(h_bcopy, h_b, sizeof(double) * rowsA); - - printf("step 2: create opaque info structure\n"); - checkCudaErrors(cusolverSpCreateCsrqrInfoHost(&h_info)); - - printf("step 3: analyze qr(A) to know structure of L\n"); - checkCudaErrors( - cusolverSpXcsrqrAnalysisHost(cusolverSpH, rowsA, colsA, nnzA, descrA, h_csrRowPtrA, h_csrColIndA, h_info)); - - printf("step 4: workspace for qr(A)\n"); - checkCudaErrors(cusolverSpDcsrqrBufferInfoHost(cusolverSpH, - rowsA, - colsA, - nnzA, - descrA, - h_csrValA, - h_csrRowPtrA, - h_csrColIndA, - h_info, - &size_internal, - &size_chol)); - - if (buffer_cpu) { - free(buffer_cpu); - } - buffer_cpu = (void *)malloc(sizeof(char) * size_chol); - assert(NULL != buffer_cpu); - - printf("step 5: compute A = L*L^T \n"); - checkCudaErrors(cusolverSpDcsrqrSetupHost( - cusolverSpH, rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA, zero, h_info)); - - checkCudaErrors(cusolverSpDcsrqrFactorHost(cusolverSpH, rowsA, colsA, nnzA, NULL, NULL, h_info, buffer_cpu)); - - printf("step 6: check if the matrix is singular \n"); - checkCudaErrors(cusolverSpDcsrqrZeroPivotHost(cusolverSpH, h_info, tol, &singularity)); - - if (0 <= singularity) { - fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity); - return 1; - } - - printf("step 7: solve A*x = b \n"); - checkCudaErrors(cusolverSpDcsrqrSolveHost(cusolverSpH, rowsA, colsA, h_b, h_x, h_info, buffer_cpu)); - - printf("step 8: evaluate residual r = b - A*x (result on CPU)\n"); - // use GPU gemv to compute r = b - A*x - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cudaMemcpy(d_r, h_bcopy, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_x, h_x, sizeof(double) * colsA, cudaMemcpyHostToDevice)); - - /* Wrap raw data into cuSPARSE generic API objects */ - cusparseSpMatDescr_t matA = NULL; - if (baseA) { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ONE, - CUDA_R_64F)); - } - else { - checkCudaErrors(cusparseCreateCsr(&matA, - rowsA, - colsA, - nnzA, - d_csrRowPtrA, - d_csrColIndA, - d_csrValA, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_32I, - CUSPARSE_INDEX_BASE_ZERO, - CUDA_R_64F)); - } - - cusparseDnVecDescr_t vecx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecx, colsA, d_x, CUDA_R_64F)); - cusparseDnVecDescr_t vecAx = NULL; - checkCudaErrors(cusparseCreateDnVec(&vecAx, rowsA, d_r, CUDA_R_64F)); - - /* Allocate workspace for cuSPARSE */ - size_t bufferSize = 0; - checkCudaErrors(cusparseSpMV_bufferSize(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - &bufferSize)); - void *buffer = NULL; - checkCudaErrors(cudaMalloc(&buffer, bufferSize)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - x_inf = vec_norminf(colsA, h_x); - r_inf = vec_norminf(rowsA, h_r); - A_inf = csr_mat_norminf(rowsA, colsA, nnzA, descrA, h_csrValA, h_csrRowPtrA, h_csrColIndA); - - printf("(CPU) |b - A*x| = %E \n", r_inf); - printf("(CPU) |A| = %E \n", A_inf); - printf("(CPU) |x| = %E \n", x_inf); - printf("(CPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - printf("step 9: create opaque info structure\n"); - checkCudaErrors(cusolverSpCreateCsrqrInfo(&d_info)); - - checkCudaErrors(cudaMemcpy(d_csrRowPtrA, h_csrRowPtrA, sizeof(int) * (rowsA + 1), cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrColIndA, h_csrColIndA, sizeof(int) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_csrValA, h_csrValA, sizeof(double) * nnzA, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_b, h_bcopy, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - printf("step 10: analyze qr(A) to know structure of L\n"); - checkCudaErrors( - cusolverSpXcsrqrAnalysis(cusolverSpH, rowsA, colsA, nnzA, descrA, d_csrRowPtrA, d_csrColIndA, d_info)); - - printf("step 11: workspace for qr(A)\n"); - checkCudaErrors(cusolverSpDcsrqrBufferInfo(cusolverSpH, - rowsA, - colsA, - nnzA, - descrA, - d_csrValA, - d_csrRowPtrA, - d_csrColIndA, - d_info, - &size_internal, - &size_chol)); - - printf("GPU buffer size = %lld bytes\n", (signed long long)size_chol); - if (buffer_gpu) { - checkCudaErrors(cudaFree(buffer_gpu)); - } - checkCudaErrors(cudaMalloc(&buffer_gpu, sizeof(char) * size_chol)); - - printf("step 12: compute A = L*L^T \n"); - checkCudaErrors(cusolverSpDcsrqrSetup( - cusolverSpH, rowsA, colsA, nnzA, descrA, d_csrValA, d_csrRowPtrA, d_csrColIndA, zero, d_info)); - - checkCudaErrors(cusolverSpDcsrqrFactor(cusolverSpH, rowsA, colsA, nnzA, NULL, NULL, d_info, buffer_gpu)); - - printf("step 13: check if the matrix is singular \n"); - checkCudaErrors(cusolverSpDcsrqrZeroPivot(cusolverSpH, d_info, tol, &singularity)); - - if (0 <= singularity) { - fprintf(stderr, "Error: A is not invertible, singularity=%d\n", singularity); - return 1; - } - - printf("step 14: solve A*x = b \n"); - checkCudaErrors(cusolverSpDcsrqrSolve(cusolverSpH, rowsA, colsA, d_b, d_x, d_info, buffer_gpu)); - - checkCudaErrors(cudaMemcpy(d_r, h_bcopy, sizeof(double) * rowsA, cudaMemcpyHostToDevice)); - - checkCudaErrors(cusparseSpMV(cusparseH, - CUSPARSE_OPERATION_NON_TRANSPOSE, - &minus_one, - matA, - vecx, - &one, - vecAx, - CUDA_R_64F, - CUSPARSE_SPMV_ALG_DEFAULT, - buffer)); - - checkCudaErrors(cudaMemcpy(h_r, d_r, sizeof(double) * rowsA, cudaMemcpyDeviceToHost)); - - r_inf = vec_norminf(rowsA, h_r); - - printf("(GPU) |b - A*x| = %E \n", r_inf); - printf("(GPU) |b - A*x|/(|A|*|x|) = %E \n", r_inf / (A_inf * x_inf)); - - if (cusolverSpH) { - checkCudaErrors(cusolverSpDestroy(cusolverSpH)); - } - if (cusparseH) { - checkCudaErrors(cusparseDestroy(cusparseH)); - } - if (stream) { - checkCudaErrors(cudaStreamDestroy(stream)); - } - if (descrA) { - checkCudaErrors(cusparseDestroyMatDescr(descrA)); - } - if (h_info) { - checkCudaErrors(cusolverSpDestroyCsrqrInfoHost(h_info)); - } - if (d_info) { - checkCudaErrors(cusolverSpDestroyCsrqrInfo(d_info)); - } - - if (matA) { - checkCudaErrors(cusparseDestroySpMat(matA)); - } - if (vecx) { - checkCudaErrors(cusparseDestroyDnVec(vecx)); - } - if (vecAx) { - checkCudaErrors(cusparseDestroyDnVec(vecAx)); - } - - if (h_csrValA) { - free(h_csrValA); - } - if (h_csrRowPtrA) { - free(h_csrRowPtrA); - } - if (h_csrColIndA) { - free(h_csrColIndA); - } - - if (h_x) { - free(h_x); - } - if (h_b) { - free(h_b); - } - if (h_bcopy) { - free(h_bcopy); - } - if (h_r) { - free(h_r); - } - - if (buffer_cpu) { - free(buffer_cpu); - } - if (buffer_gpu) { - checkCudaErrors(cudaFree(buffer_gpu)); - } - - if (d_csrValA) { - checkCudaErrors(cudaFree(d_csrValA)); - } - if (d_csrRowPtrA) { - checkCudaErrors(cudaFree(d_csrRowPtrA)); - } - if (d_csrColIndA) { - checkCudaErrors(cudaFree(d_csrColIndA)); - } - if (d_x) { - checkCudaErrors(cudaFree(d_x)); - } - if (d_b) { - checkCudaErrors(cudaFree(d_b)); - } - if (d_r) { - checkCudaErrors(cudaFree(d_r)); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/lap2D_5pt_n100.mtx b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/lap2D_5pt_n100.mtx deleted file mode 100644 index dae3c1eb..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/lap2D_5pt_n100.mtx +++ /dev/null @@ -1,29803 +0,0 @@ -%%MatrixMarket matrix coordinate real symmetric -% Generated 20-Nov-2014 -10000 10000 29800 -1 1 4 -2 1 -1 -101 1 -1 -2 2 4 -3 2 -1 -102 2 -1 -3 3 4 -4 3 -1 -103 3 -1 -4 4 4 -5 4 -1 -104 4 -1 -5 5 4 -6 5 -1 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-/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -#include "mmio.h" - -#include -#include -#include -#include - -int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_) -{ - FILE *f; - MM_typecode matcode; - int M, N, nz; - int i; - double *val; - int *I, *J; - - if ((f = fopen(fname, "r")) == NULL) - return -1; - - - if (mm_read_banner(f, &matcode) != 0) { - printf("mm_read_unsymetric: Could not process Matrix Market banner "); - printf(" in file [%s]\n", fname); - return -1; - } - - - if (!(mm_is_real(matcode) && mm_is_matrix(matcode) && mm_is_sparse(matcode))) { - fprintf(stderr, "Sorry, this application does not support "); - fprintf(stderr, "Market Market type: [%s]\n", mm_typecode_to_str(matcode)); - return -1; - } - - /* find out size of sparse matrix: M, N, nz .... */ - - if (mm_read_mtx_crd_size(f, &M, &N, &nz) != 0) { - fprintf(stderr, "read_unsymmetric_sparse(): could not parse matrix size.\n"); - return -1; - } - - *M_ = M; - *N_ = N; - *nz_ = nz; - - /* reserve memory for matrices */ - - I = (int *)malloc(nz * sizeof(int)); - J = (int *)malloc(nz * sizeof(int)); - val = (double *)malloc(nz * sizeof(double)); - - *val_ = val; - *I_ = I; - *J_ = J; - - /* NOTE: when reading in doubles, ANSI C requires the use of the "l" */ - /* specifier as in "%lg", "%lf", "%le", otherwise errors will occur */ - /* (ANSI C X3.159-1989, Sec. 4.9.6.2, p. 136 lines 13-15) */ - - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) { - return -1; - } - I[i]--; /* adjust from 1-based to 0-based */ - J[i]--; - } - fclose(f); - - return 0; -} - -int mm_is_valid(MM_typecode matcode) -{ - if (!mm_is_matrix(matcode)) - return 0; - if (mm_is_dense(matcode) && mm_is_pattern(matcode)) - return 0; - if (mm_is_real(matcode) && mm_is_hermitian(matcode)) - return 0; - if (mm_is_pattern(matcode) && (mm_is_hermitian(matcode) || mm_is_skew(matcode))) - return 0; - return 1; -} - -int mm_read_banner(FILE *f, MM_typecode *matcode) -{ - char line[MM_MAX_LINE_LENGTH]; - char banner[MM_MAX_TOKEN_LENGTH]; - char mtx[MM_MAX_TOKEN_LENGTH]; - char crd[MM_MAX_TOKEN_LENGTH]; - char data_type[MM_MAX_TOKEN_LENGTH]; - char storage_scheme[MM_MAX_TOKEN_LENGTH]; - char *p; - - - mm_clear_typecode(matcode); - - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - - if (sscanf(line, "%s %s %s %s %s", banner, mtx, crd, data_type, storage_scheme) != 5) - return MM_PREMATURE_EOF; - - for (p = mtx; *p != '\0'; *p = tolower(*p), p++) - ; /* convert to lower case */ - for (p = crd; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = data_type; *p != '\0'; *p = tolower(*p), p++) - ; - for (p = storage_scheme; *p != '\0'; *p = tolower(*p), p++) - ; - - /* check for banner */ - if (strncmp(banner, MatrixMarketBanner, strlen(MatrixMarketBanner)) != 0) - return MM_NO_HEADER; - - /* first field should be "mtx" */ - if (strcmp(mtx, MM_MTX_STR) != 0) - return MM_UNSUPPORTED_TYPE; - mm_set_matrix(matcode); - - - /* second field describes whether this is a sparse matrix (in coordinate - storage) or a dense array */ - - - if (strcmp(crd, MM_SPARSE_STR) == 0) - mm_set_sparse(matcode); - else if (strcmp(crd, MM_DENSE_STR) == 0) - mm_set_dense(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* third field */ - - if (strcmp(data_type, MM_REAL_STR) == 0) - mm_set_real(matcode); - else if (strcmp(data_type, MM_COMPLEX_STR) == 0) - mm_set_complex(matcode); - else if (strcmp(data_type, MM_PATTERN_STR) == 0) - mm_set_pattern(matcode); - else if (strcmp(data_type, MM_INT_STR) == 0) - mm_set_integer(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - /* fourth field */ - - if (strcmp(storage_scheme, MM_GENERAL_STR) == 0) - mm_set_general(matcode); - else if (strcmp(storage_scheme, MM_SYMM_STR) == 0) - mm_set_symmetric(matcode); - else if (strcmp(storage_scheme, MM_HERM_STR) == 0) - mm_set_hermitian(matcode); - else if (strcmp(storage_scheme, MM_SKEW_STR) == 0) - mm_set_skew(matcode); - else - return MM_UNSUPPORTED_TYPE; - - - return 0; -} - -int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz) -{ - if (fprintf(f, "%d %d %d\n", M, N, nz) != 3) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - - /* set return null parameter values, in case we exit with errors */ - *M = *N = *nz = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d %d", M, N, nz) == 3) - return 0; - - else - do { - num_items_read = fscanf(f, "%d %d %d", M, N, nz); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 3); - - return 0; -} - - -int mm_read_mtx_array_size(FILE *f, int *M, int *N) -{ - char line[MM_MAX_LINE_LENGTH]; - int num_items_read; - /* set return null parameter values, in case we exit with errors */ - *M = *N = 0; - - /* now continue scanning until you reach the end-of-comments */ - do { - if (fgets(line, MM_MAX_LINE_LENGTH, f) == NULL) - return MM_PREMATURE_EOF; - } while (line[0] == '%'); - - /* line[] is either blank or has M,N, nz */ - if (sscanf(line, "%d %d", M, N) == 2) - return 0; - - else /* we have a blank line */ - do { - num_items_read = fscanf(f, "%d %d", M, N); - if (num_items_read == EOF) - return MM_PREMATURE_EOF; - } while (num_items_read != 2); - - return 0; -} - -int mm_write_mtx_array_size(FILE *f, int M, int N) -{ - if (fprintf(f, "%d %d\n", M, N) != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - - -/*-------------------------------------------------------------------------*/ - -/******************************************************************/ -/* use when I[], J[], and val[]J, and val[] are already allocated */ -/******************************************************************/ - -int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - int i; - if (mm_is_complex(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d %lg %lg", &I[i], &J[i], &val[2 * i], &val[2 * i + 1]) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - for (i = 0; i < nz; i++) { - if (fscanf(f, "%d %d %lg\n", &I[i], &J[i], &val[i]) != 3) - return MM_PREMATURE_EOF; - } - } - - else if (mm_is_pattern(matcode)) { - for (i = 0; i < nz; i++) - if (fscanf(f, "%d %d", &I[i], &J[i]) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - -int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *imag, MM_typecode matcode) -{ - if (mm_is_complex(matcode)) { - if (fscanf(f, "%d %d %lg %lg", I, J, real, imag) != 4) - return MM_PREMATURE_EOF; - } - else if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (fscanf(f, "%d %d %lg\n", I, J, real) != 3) - return MM_PREMATURE_EOF; - } - - else if (mm_is_pattern(matcode)) { - if (fscanf(f, "%d %d", I, J) != 2) - return MM_PREMATURE_EOF; - } - else - return MM_UNSUPPORTED_TYPE; - - return 0; -} - - -/************************************************************************ - mm_read_mtx_crd() fills M, N, nz, array of values, and return - type code, e.g. 'MCRS' - - if matrix is complex, values[] is of size 2*nz, - (nz pairs of real/imaginary values) -************************************************************************/ - -int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode) -{ - int ret_code; - FILE *f; - - if (strcmp(fname, "stdin") == 0) - f = stdin; - else if ((f = fopen(fname, "r")) == NULL) - return MM_COULD_NOT_READ_FILE; - - - if ((ret_code = mm_read_banner(f, matcode)) != 0) - return ret_code; - - if (!(mm_is_valid(*matcode) && mm_is_sparse(*matcode) && mm_is_matrix(*matcode))) - return MM_UNSUPPORTED_TYPE; - - if ((ret_code = mm_read_mtx_crd_size(f, M, N, nz)) != 0) - return ret_code; - - - *I = (int *)malloc(*nz * sizeof(int)); - *J = (int *)malloc(*nz * sizeof(int)); - *val = NULL; - - if (mm_is_complex(*matcode)) { - *val = (double *)malloc(*nz * 2 * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - else if (mm_is_real(*matcode) || mm_is_integer(*matcode)) { - *val = (double *)malloc(*nz * sizeof(double)); - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - else if (mm_is_pattern(*matcode)) { - ret_code = mm_read_mtx_crd_data(f, *M, *N, *nz, *I, *J, *val, *matcode); - if (ret_code != 0) - return ret_code; - } - - if (f != stdin) - fclose(f); - return 0; -} - -int mm_write_banner(FILE *f, MM_typecode matcode) -{ - char *str = mm_typecode_to_str(matcode); - int ret_code; - - ret_code = fprintf(f, "%s %s\n", MatrixMarketBanner, str); - free(str); - if (ret_code != 2) - return MM_COULD_NOT_WRITE_FILE; - else - return 0; -} - -int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode) -{ - FILE *f; - int i; - - if (strcmp(fname, "stdout") == 0) - f = stdout; - else if ((f = fopen(fname, "w")) == NULL) - return MM_COULD_NOT_WRITE_FILE; - - /* print banner followed by typecode */ - fprintf(f, "%s ", MatrixMarketBanner); - fprintf(f, "%s\n", mm_typecode_to_str(matcode)); - - /* print matrix sizes and nonzeros */ - fprintf(f, "%d %d %d\n", M, N, nz); - - /* print values */ - if (mm_is_pattern(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d\n", I[i], J[i]); - else if (mm_is_integer(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %d\n", I[i], J[i], (int)val[i]); - else if (mm_is_real(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g\n", I[i], J[i], val[i]); - else if (mm_is_complex(matcode)) - for (i = 0; i < nz; i++) - fprintf(f, "%d %d %20.16g %20.16g\n", I[i], J[i], val[2 * i], val[2 * i + 1]); - else { - if (f != stdout) - fclose(f); - return MM_UNSUPPORTED_TYPE; - } - - if (f != stdout) - fclose(f); - - return 0; -} - -/** - * Create a new copy of a string s. mm_strdup() is a common routine, but - * not part of ANSI C, so it is included here. Used by mm_typecode_to_str(). - * - */ -static char *mm_strdup(const char *s) -{ - size_t len = strlen(s); - char *s2 = (char *)malloc((len + 1) * sizeof(char)); - return strcpy(s2, s); -} - -char *mm_typecode_to_str(MM_typecode matcode) -{ - char buffer[MM_MAX_LINE_LENGTH]; - char *types[4]; - // char *mm_strdup(const char *); - // int error =0; - - /* check for MTX type */ - if (mm_is_matrix(matcode)) - types[0] = MM_MTX_STR; - else - return NULL; // error=1; - - /* check for CRD or ARR matrix */ - if (mm_is_sparse(matcode)) - types[1] = MM_SPARSE_STR; - else if (mm_is_dense(matcode)) - types[1] = MM_DENSE_STR; - else - return NULL; - - /* check for element data type */ - if (mm_is_real(matcode)) - types[2] = MM_REAL_STR; - else if (mm_is_complex(matcode)) - types[2] = MM_COMPLEX_STR; - else if (mm_is_pattern(matcode)) - types[2] = MM_PATTERN_STR; - else if (mm_is_integer(matcode)) - types[2] = MM_INT_STR; - else - return NULL; - - - /* check for symmetry type */ - if (mm_is_general(matcode)) - types[3] = MM_GENERAL_STR; - else if (mm_is_symmetric(matcode)) - types[3] = MM_SYMM_STR; - else if (mm_is_hermitian(matcode)) - types[3] = MM_HERM_STR; - else if (mm_is_skew(matcode)) - types[3] = MM_SKEW_STR; - else - return NULL; - - sprintf(buffer, "%s %s %s %s", types[0], types[1], types[2], types[3]); - return mm_strdup(buffer); -} diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio.h b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio.h deleted file mode 100644 index a05f6063..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio.h +++ /dev/null @@ -1,138 +0,0 @@ -/* - * Matrix Market I/O library for ANSI C - * - * See http://math.nist.gov/MatrixMarket for details. - * - * - */ - -#ifndef MM_IO_H -#define MM_IO_H - -#include - -#if defined(__cplusplus) -extern "C" -{ -#endif /* __cplusplus */ - -#define MM_MAX_LINE_LENGTH 1025 -#define MatrixMarketBanner "%%MatrixMarket" -#define MM_MAX_TOKEN_LENGTH 64 - - typedef char MM_typecode[4]; - - char *mm_typecode_to_str(MM_typecode matcode); - - int mm_read_banner(FILE *f, MM_typecode *matcode); - int mm_read_mtx_crd_size(FILE *f, int *M, int *N, int *nz); - int mm_read_mtx_array_size(FILE *f, int *M, int *N); - - int mm_write_banner(FILE *f, MM_typecode matcode); - int mm_write_mtx_crd_size(FILE *f, int M, int N, int nz); - int mm_write_mtx_array_size(FILE *f, int M, int N); - - - /********************* MM_typecode query fucntions ***************************/ - -#define mm_is_matrix(typecode) ((typecode)[0] == 'M') - -#define mm_is_sparse(typecode) ((typecode)[1] == 'C') -#define mm_is_coordinate(typecode) ((typecode)[1] == 'C') -#define mm_is_dense(typecode) ((typecode)[1] == 'A') -#define mm_is_array(typecode) ((typecode)[1] == 'A') - -#define mm_is_complex(typecode) ((typecode)[2] == 'C') -#define mm_is_real(typecode) ((typecode)[2] == 'R') -#define mm_is_pattern(typecode) ((typecode)[2] == 'P') -#define mm_is_integer(typecode) ((typecode)[2] == 'I') - -#define mm_is_symmetric(typecode) ((typecode)[3] == 'S') -#define mm_is_general(typecode) ((typecode)[3] == 'G') -#define mm_is_skew(typecode) ((typecode)[3] == 'K') -#define mm_is_hermitian(typecode) ((typecode)[3] == 'H') - - int mm_is_valid(MM_typecode matcode); /* too complex for a macro */ - - - /********************* MM_typecode modify fucntions ***************************/ - -#define mm_set_matrix(typecode) ((*typecode)[0] = 'M') -#define mm_set_coordinate(typecode) ((*typecode)[1] = 'C') -#define mm_set_array(typecode) ((*typecode)[1] = 'A') -#define mm_set_dense(typecode) mm_set_array(typecode) -#define mm_set_sparse(typecode) mm_set_coordinate(typecode) - -#define mm_set_complex(typecode) ((*typecode)[2] = 'C') -#define mm_set_real(typecode) ((*typecode)[2] = 'R') -#define mm_set_pattern(typecode) ((*typecode)[2] = 'P') -#define mm_set_integer(typecode) ((*typecode)[2] = 'I') - - -#define mm_set_symmetric(typecode) ((*typecode)[3] = 'S') -#define mm_set_general(typecode) ((*typecode)[3] = 'G') -#define mm_set_skew(typecode) ((*typecode)[3] = 'K') -#define mm_set_hermitian(typecode) ((*typecode)[3] = 'H') - -#define mm_clear_typecode(typecode) ((*typecode)[0] = (*typecode)[1] = (*typecode)[2] = ' ', (*typecode)[3] = 'G') - -#define mm_initialize_typecode(typecode) mm_clear_typecode(typecode) - - - /********************* Matrix Market error codes ***************************/ - - -#define MM_COULD_NOT_READ_FILE 11 -#define MM_PREMATURE_EOF 12 -#define MM_NOT_MTX 13 -#define MM_NO_HEADER 14 -#define MM_UNSUPPORTED_TYPE 15 -#define MM_LINE_TOO_LONG 16 -#define MM_COULD_NOT_WRITE_FILE 17 - - - /******************** Matrix Market internal definitions ******************** - - MM_matrix_typecode: 4-character sequence - - ojbect sparse/ data storage - dense type scheme - - string position: [0] [1] [2] [3] - - Matrix typecode: M(atrix) C(oord) R(eal) G(eneral) - A(array) C(omplex) H(ermitian) - P(attern) S(ymmetric) - I(nteger) K(kew) - - ***********************************************************************/ - -#define MM_MTX_STR "matrix" -#define MM_ARRAY_STR "array" -#define MM_DENSE_STR "array" -#define MM_COORDINATE_STR "coordinate" -#define MM_SPARSE_STR "coordinate" -#define MM_COMPLEX_STR "complex" -#define MM_REAL_STR "real" -#define MM_INT_STR "integer" -#define MM_GENERAL_STR "general" -#define MM_SYMM_STR "symmetric" -#define MM_HERM_STR "hermitian" -#define MM_SKEW_STR "skew-symmetric" -#define MM_PATTERN_STR "pattern" - - - /* high level routines */ - int mm_read_mtx_crd(char *fname, int *M, int *N, int *nz, int **I, int **J, double **val, MM_typecode *matcode); - - int mm_write_mtx_crd(char fname[], int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_data(FILE *f, int M, int N, int nz, int I[], int J[], double val[], MM_typecode matcode); - int mm_read_mtx_crd_entry(FILE *f, int *I, int *J, double *real, double *img, MM_typecode matcode); - - int mm_read_unsymmetric_sparse(const char *fname, int *M_, int *N_, int *nz_, double **val_, int **I_, int **J_); - -#if defined(__cplusplus) -} -#endif /* __cplusplus */ - -#endif diff --git a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio_wrapper.cpp b/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio_wrapper.cpp deleted file mode 100644 index 3f798d53..00000000 --- a/cpp/4_CUDA_Libraries/cuSolverSp_LowlevelQR/mmio_wrapper.cpp +++ /dev/null @@ -1,492 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -#include "mmio.h" - -/* avoid Windows warnings (for example: strcpy, fscanf, etc.) */ -#if defined(_WIN32) -#define _CRT_SECURE_NO_WARNINGS -#endif - -/* various __inline__ __device__ function to initialize a T_ELEM */ -template __inline__ T_ELEM cuGet(int); -template <> __inline__ float cuGet(int x) { return float(x); } - -template <> __inline__ double cuGet(int x) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(int x) { return (make_cuDoubleComplex(double(x), 0.0)); } - -template __inline__ T_ELEM cuGet(int, int); -template <> __inline__ float cuGet(int x, int y) { return float(x); } - -template <> __inline__ double cuGet(int x, int y) { return double(x); } - -template <> __inline__ cuComplex cuGet(int x, int y) { return make_cuComplex(float(x), float(y)); } - -template <> __inline__ cuDoubleComplex cuGet(int x, int y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -template __inline__ T_ELEM cuGet(float); -template <> __inline__ float cuGet(float x) { return float(x); } - -template <> __inline__ double cuGet(float x) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(float x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - -template __inline__ T_ELEM cuGet(float, float); -template <> __inline__ float cuGet(float x, float y) { return float(x); } - -template <> __inline__ double cuGet(float x, float y) { return double(x); } - -template <> __inline__ cuComplex cuGet(float x, float y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(float x, float y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -template __inline__ T_ELEM cuGet(double); -template <> __inline__ float cuGet(double x) { return float(x); } - -template <> __inline__ double cuGet(double x) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x) { return (make_cuComplex(float(x), 0.0f)); } - -template <> __inline__ cuDoubleComplex cuGet(double x) -{ - return (make_cuDoubleComplex(double(x), 0.0)); -} - -template __inline__ T_ELEM cuGet(double, double); -template <> __inline__ float cuGet(double x, double y) { return float(x); } - -template <> __inline__ double cuGet(double x, double y) { return double(x); } - -template <> __inline__ cuComplex cuGet(double x, double y) { return (make_cuComplex(float(x), float(y))); } - -template <> __inline__ cuDoubleComplex cuGet(double x, double y) -{ - return (make_cuDoubleComplex(double(x), double(y))); -} - -static void compress_index(const int *Ind, int nnz, int m, int *Ptr, int base) -{ - int i; - - /* initialize everything to zero */ - for (i = 0; i < m + 1; i++) { - Ptr[i] = 0; - } - /* count elements in every row */ - Ptr[0] = base; - for (i = 0; i < nnz; i++) { - Ptr[Ind[i] + (1 - base)]++; - } - /* add all the values */ - for (i = 0; i < m; i++) { - Ptr[i + 1] += Ptr[i]; - } -} - -struct cooFormat -{ - int i; - int j; - int p; // permutation -}; - -int cmp_cooFormat_csr(struct cooFormat *s, struct cooFormat *t) -{ - if (s->i < t->i) { - return -1; - } - else if (s->i > t->i) { - return 1; - } - else { - return s->j - t->j; - } -} - -int cmp_cooFormat_csc(struct cooFormat *s, struct cooFormat *t) -{ - if (s->j < t->j) { - return -1; - } - else if (s->j > t->j) { - return 1; - } - else { - return s->i - t->i; - } -} - -typedef int (*FUNPTR)(const void *, const void *); -typedef int (*FUNPTR2)(struct cooFormat *s, struct cooFormat *t); - -static FUNPTR2 fptr_array[2] = { - cmp_cooFormat_csr, - cmp_cooFormat_csc, -}; - -static int verify_pattern(int m, int nnz, int *csrRowPtr, int *csrColInd) -{ - int i, col, start, end, base_index; - int error_found = 0; - - if (nnz != (csrRowPtr[m] - csrRowPtr[0])) { - fprintf(stderr, - "Error (nnz check failed): (csrRowPtr[%d]=%d - csrRowPtr[%d]=%d) " - "!= (nnz=%d)\n", - 0, - csrRowPtr[0], - m, - csrRowPtr[m], - nnz); - error_found = 1; - } - - base_index = csrRowPtr[0]; - if ((0 != base_index) && (1 != base_index)) { - fprintf(stderr, "Error (base index check failed): base index = %d\n", base_index); - error_found = 1; - } - - for (i = 0; (!error_found) && (i < m); i++) { - start = csrRowPtr[i] - base_index; - end = csrRowPtr[i + 1] - base_index; - if (start > end) { - fprintf(stderr, - "Error (corrupted row): csrRowPtr[%d] (=%d) > csrRowPtr[%d] (=%d)\n", - i, - start + base_index, - i + 1, - end + base_index); - error_found = 1; - } - for (col = start; col < end; col++) { - if (csrColInd[col] < base_index) { - fprintf(stderr, "Error (column vs. base index check failed): csrColInd[%d] < %d\n", col, base_index); - error_found = 1; - } - if ((col < (end - 1)) && (csrColInd[col] >= csrColInd[col + 1])) { - fprintf(stderr, - "Error (sorting of the column indecis check failed): " - "(csrColInd[%d]=%d) >= (csrColInd[%d]=%d)\n", - col, - csrColInd[col], - col + 1, - csrColInd[col + 1]); - error_found = 1; - } - } - } - return error_found; -} - -template -int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - T_ELEM **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix) -{ - MM_typecode matcode; - double *tempVal; - int *tempRowInd, *tempColInd; - double *tval; - int *trow, *tcol; - int *csrRowPtr, *cscColPtr; - int i, j, error, base, count; - struct cooFormat *work; - - /* read the matrix */ - error = mm_read_mtx_crd(filename, m, n, nnz, &trow, &tcol, &tval, &matcode); - if (error) { - fprintf(stderr, "!!!! can not open file: '%s'\n", filename); - return 1; - } - - /* start error checking */ - if (mm_is_complex(matcode) && ((elem_type != 'z') && (elem_type != 'c'))) { - fprintf(stderr, "!!!! complex matrix requires type 'z' or 'c'\n"); - return 1; - } - - if (mm_is_dense(matcode) || mm_is_array(matcode) || mm_is_pattern(matcode) /*|| mm_is_integer(matcode)*/) { - fprintf(stderr, "!!!! dense, array, pattern and integer matrices are not supported\n"); - return 1; - } - - /* if necessary symmetrize the pattern (transform from triangular to full) */ - if ((extendSymMatrix) && (mm_is_symmetric(matcode) || mm_is_hermitian(matcode) || mm_is_skew(matcode))) { - // count number of non-diagonal elements - count = 0; - for (i = 0; i < (*nnz); i++) { - if (trow[i] != tcol[i]) { - count++; - } - } - // allocate space for the symmetrized matrix - tempRowInd = (int *)malloc((*nnz + count) * sizeof(int)); - tempColInd = (int *)malloc((*nnz + count) * sizeof(int)); - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal = (double *)malloc((*nnz + count) * sizeof(double)); - } - else { - tempVal = (double *)malloc(2 * (*nnz + count) * sizeof(double)); - } - // copy the elements regular and transposed locations - for (j = 0, i = 0; i < (*nnz); i++) { - tempRowInd[j] = trow[i]; - tempColInd[j] = tcol[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - tempVal[j] = tval[i]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - j++; - if (trow[i] != tcol[i]) { - tempRowInd[j] = tcol[i]; - tempColInd[j] = trow[i]; - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - if (mm_is_skew(matcode)) { - tempVal[j] = -tval[i]; - } - else { - tempVal[j] = tval[i]; - } - } - else { - if (mm_is_hermitian(matcode)) { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = -tval[2 * i + 1]; - } - else { - tempVal[2 * j] = tval[2 * i]; - tempVal[2 * j + 1] = tval[2 * i + 1]; - } - } - j++; - } - } - (*nnz) += count; - // free temporary storage - free(trow); - free(tcol); - free(tval); - } - else { - tempRowInd = trow; - tempColInd = tcol; - tempVal = tval; - } - // life time of (trow, tcol, tval) is over. - // please use COO format (tempRowInd, tempColInd, tempVal) - - // use qsort to sort COO format - work = (struct cooFormat *)malloc(sizeof(struct cooFormat) * (*nnz)); - if (NULL == work) { - fprintf(stderr, "!!!! allocation error, malloc failed\n"); - return 1; - } - for (i = 0; i < (*nnz); i++) { - work[i].i = tempRowInd[i]; - work[i].j = tempColInd[i]; - work[i].p = i; // permutation is identity - } - - if (csrFormat) { - /* create row-major ordering of indices (sorted by row and within each row - * by column) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[0]); - } - else { - /* create column-major ordering of indices (sorted by column and within each - * column by row) */ - qsort(work, *nnz, sizeof(struct cooFormat), (FUNPTR)fptr_array[1]); - } - - // (tempRowInd, tempColInd) is sorted either by row-major or by col-major - for (i = 0; i < (*nnz); i++) { - tempRowInd[i] = work[i].i; - tempColInd[i] = work[i].j; - } - - // setup base - // check if there is any row/col 0, if so base-0 - // check if there is any row/col equal to matrix dimension m/n, if so base-1 - int base0 = 0; - int base1 = 0; - for (i = 0; i < (*nnz); i++) { - const int row = tempRowInd[i]; - const int col = tempColInd[i]; - if ((0 == row) || (0 == col)) { - base0 = 1; - } - if ((*m == row) || (*n == col)) { - base1 = 1; - } - } - if (base0 && base1) { - printf("Error: input matrix is base-0 and base-1 \n"); - return 1; - } - - base = 0; - if (base1) { - base = 1; - } - - /* compress the appropriate indices */ - if (csrFormat) { - /* CSR format (assuming row-major format) */ - csrRowPtr = (int *)malloc(((*m) + 1) * sizeof(csrRowPtr[0])); - if (!csrRowPtr) - return 1; - compress_index(tempRowInd, *nnz, *m, csrRowPtr, base); - - *aRowInd = csrRowPtr; - *aColInd = (int *)malloc((*nnz) * sizeof(int)); - } - else { - /* CSC format (assuming column-major format) */ - cscColPtr = (int *)malloc(((*n) + 1) * sizeof(cscColPtr[0])); - if (!cscColPtr) - return 1; - compress_index(tempColInd, *nnz, *n, cscColPtr, base); - - *aColInd = cscColPtr; - *aRowInd = (int *)malloc((*nnz) * sizeof(int)); - } - - /* transfrom the matrix values of type double into one of the cusparse library - * types */ - *aVal = (T_ELEM *)malloc((*nnz) * sizeof(T_ELEM)); - - for (i = 0; i < (*nnz); i++) { - if (csrFormat) { - (*aColInd)[i] = tempColInd[i]; - } - else { - (*aRowInd)[i] = tempRowInd[i]; - } - if (mm_is_real(matcode) || mm_is_integer(matcode)) { - (*aVal)[i] = cuGet(tempVal[work[i].p]); - } - else { - (*aVal)[i] = cuGet(tempVal[2 * work[i].p], tempVal[2 * work[i].p + 1]); - } - } - - /* check for corruption */ - int error_found; - if (csrFormat) { - error_found = verify_pattern(*m, *nnz, *aRowInd, *aColInd); - } - else { - error_found = verify_pattern(*n, *nnz, *aColInd, *aRowInd); - } - if (error_found) { - fprintf(stderr, "!!!! verify_pattern failed\n"); - return 1; - } - - /* cleanup and exit */ - free(work); - free(tempVal); - free(tempColInd); - free(tempRowInd); - - return 0; -} - -/* specific instantiation */ -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - float **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - double **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); - -template int loadMMSparseMatrix(char *filename, - char elem_type, - bool csrFormat, - int *m, - int *n, - int *nnz, - cuDoubleComplex **aVal, - int **aRowInd, - int **aColInd, - int extendSymMatrix); diff --git a/cpp/4_CUDA_Libraries/cubDeviceFind/CMakeLists.txt b/cpp/4_CUDA_Libraries/cubDeviceFind/CMakeLists.txt index 2937b837..303e44b0 100644 --- a/cpp/4_CUDA_Libraries/cubDeviceFind/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/cubDeviceFind/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cubDeviceFind LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -11,16 +18,6 @@ endif() find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Fetch CCCL via CPM. # Override with -DCCCL_SOURCE_DIR=/path/to/cccl to use a local checkout set(CCCL_SAMPLES_CCCL_TAG "v3.3.3" CACHE STRING @@ -39,24 +36,18 @@ if(NOT TARGET CCCL::CCCL) endif() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cubDeviceFind add_executable(cubDeviceFind cubDeviceFind.cu) target_compile_options(cubDeviceFind PRIVATE $<$:--extended-lambda>) target_compile_features(cubDeviceFind PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cubDeviceFind PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(cubDeviceFind PRIVATE CUDA::cudart CCCL::CCCL ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cubDeviceSegmentedScan/CMakeLists.txt b/cpp/4_CUDA_Libraries/cubDeviceSegmentedScan/CMakeLists.txt index 60a402c1..b22a8a68 100644 --- a/cpp/4_CUDA_Libraries/cubDeviceSegmentedScan/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/cubDeviceSegmentedScan/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cubDeviceSegmentedScan LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -11,16 +18,6 @@ endif() find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Fetch CCCL via CPM. # Override with -DCCCL_SOURCE_DIR=/path/to/cccl to use a local checkout # instead of fetching from GitHub. @@ -40,24 +37,18 @@ if(NOT TARGET CCCL::CCCL) endif() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cubDeviceSegmentedScan add_executable(cubDeviceSegmentedScan cubDeviceSegmentedScan.cu) target_compile_options(cubDeviceSegmentedScan PRIVATE $<$:--extended-lambda>) target_compile_features(cubDeviceSegmentedScan PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cubDeviceSegmentedScan PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(cubDeviceSegmentedScan PRIVATE CUDA::cudart CCCL::CCCL ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cubDeviceTransform/CMakeLists.txt b/cpp/4_CUDA_Libraries/cubDeviceTransform/CMakeLists.txt index ecacbd10..6a0abec6 100644 --- a/cpp/4_CUDA_Libraries/cubDeviceTransform/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/cubDeviceTransform/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cubDeviceTransform LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -11,16 +18,6 @@ endif() find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Fetch CCCL via CPM. # Override with -DCCCL_SOURCE_DIR=/path/to/cccl to use a local checkout set(CCCL_SAMPLES_CCCL_TAG "v3.3.3" CACHE STRING @@ -39,24 +36,18 @@ if(NOT TARGET CCCL::CCCL) endif() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cubDeviceTransform add_executable(cubDeviceTransform cubDeviceTransform.cu) target_compile_options(cubDeviceTransform PRIVATE $<$:--extended-lambda>) target_compile_features(cubDeviceTransform PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cubDeviceTransform PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(cubDeviceTransform PRIVATE CUDA::cudart CCCL::CCCL ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/extensions.json b/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/cudaNvSci/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/cudaNvSci/CMakeLists.txt b/cpp/4_CUDA_Libraries/cudaNvSci/CMakeLists.txt index 005501c6..4784f4f1 100644 --- a/cpp/4_CUDA_Libraries/cudaNvSci/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/cudaNvSci/CMakeLists.txt @@ -1,46 +1,32 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaNvSci LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 90 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(NVSCI) -if(CMAKE_SYSTEM_NAME STREQUAL "Linux") +if(CMAKE_SYSTEM_NAME STREQUAL "Linux" OR CMAKE_SYSTEM_NAME STREQUAL "QNX") if(NVSCI_FOUND) message(STATUS "FOUND NVSCI libs: ${NVSCIBUF_LIB} ${NVSCISYNC_LIB}") message(STATUS "Using NVSCI headers path: ${NVSCIBUF_INCLUDE_DIR} ${NVSCIBUF_INCLUDE_DIR}") - # Source file - # Add target for cudaNvSci add_executable(cudaNvSci imageKernels.cu cudaNvSci.cpp main.cpp) - target_compile_options(cudaNvSci PRIVATE $<$:--extended-lambda>) - target_compile_features(cudaNvSci PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cudaNvSci PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cudaNvSci PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${NVSCI_INCLUDE_DIRS} ) - target_link_libraries(cudaNvSci CUDA::cuda_driver ${NVSCI_LIBRARIES} @@ -50,18 +36,15 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/teapot1024.ppm ${CMAKE_CURRENT_BINARY_DIR}/teapot1024.ppm ) - # Specify additional clean files set_target_properties(cudaNvSci PROPERTIES ADDITIONAL_CLEAN_FILES "teapot1024_out.ppm" ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "NvSCI not found - will not build sample 'cudaNvSci'") endif() else() - message(STATUS "Will not build sample cudaNvSci - requires Linux OS") + message(STATUS "Will not build sample cudaNvSci - requires Linux or QNX") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/cudaNvSci/README.md b/cpp/4_CUDA_Libraries/cudaNvSci/README.md index ac7ff197..d0d1d484 100644 --- a/cpp/4_CUDA_Libraries/cudaNvSci/README.md +++ b/cpp/4_CUDA_Libraries/cudaNvSci/README.md @@ -2,7 +2,7 @@ ## Description -This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. Currently only supported on Ubuntu 18.04 +This sample demonstrates CUDA-NvSciBuf/NvSciSync Interop. Two CPU threads import the NvSciBuf and NvSciSync into CUDA to perform two image processing algorithms on a ppm image - image rotation in 1st thread & rgba to grayscale conversion of rotated image in 2nd thread. ## Key Concepts @@ -14,7 +14,7 @@ CUDA NvSci Interop, Data Parallel Algorithms, Image Processing ## Supported OSes -Linux +Linux, QNX (standard and safety cross-builds) ## Supported CPU Architecture @@ -36,4 +36,6 @@ cudaExternalMemoryGetMappedBuffer, cudaImportExternalSemaphore, cudaDeviceGetAtt Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. Make sure the dependencies mentioned in [Dependencies]() section above are installed. +On a QNX cross-build the NvSci headers and libraries come from the target filesystem, so pass `-DTARGET_FS=/path/to/qnx/targetfs` to cmake. Without it the build reports `NvSCI not found` and skips this sample. See the [QNX build instructions](../../../README.md#qnx). + ## References (for more details) diff --git a/cpp/4_CUDA_Libraries/cudaNvSci/imageKernels.cu b/cpp/4_CUDA_Libraries/cudaNvSci/imageKernels.cu index 1d86d23b..8eddb407 100644 --- a/cpp/4_CUDA_Libraries/cudaNvSci/imageKernels.cu +++ b/cpp/4_CUDA_Libraries/cudaNvSci/imageKernels.cu @@ -91,7 +91,7 @@ void launchGrayScaleKernel(unsigned int *d_rgbaImage, rgbToGrayscaleKernel<<>>(d_rgbaImage, imageWidth, imageHeight); unsigned int *outputData; - checkCudaErrors(cudaMallocHost((void **)&outputData, sizeof(unsigned int) * imageWidth * imageHeight)); + checkCudaErrors(cudaMallocHost((void **)&outputData, sizeof(unsigned int) * imageWidth * imageHeight, 0)); checkCudaErrors(cudaMemcpyAsync( outputData, d_rgbaImage, sizeof(unsigned int) * imageWidth * imageHeight, cudaMemcpyDeviceToHost, stream)); checkCudaErrors(cudaStreamSynchronize(stream)); diff --git a/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/freeImageInteropNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/freeImageInteropNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/freeImageInteropNPP/CMakeLists.txt deleted file mode 100644 index b3e89dc6..00000000 --- a/cpp/4_CUDA_Libraries/freeImageInteropNPP/CMakeLists.txt +++ /dev/null @@ -1,71 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(freeImageInteropNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories( - ../../../Common - ../../../Common/UtilNPP -) - -# Source file -find_package(FreeImage) - -if(${FreeImage_FOUND}) - # Add target for freeImageInteropNPP - add_executable(freeImageInteropNPP freeImageInteropNPP.cpp) - - target_compile_options(freeImageInteropNPP PRIVATE $<$:--extended-lambda>) - - target_compile_features(freeImageInteropNPP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(freeImageInteropNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(freeImageInteropNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} - ${FreeImage_INCLUDE_DIRS} - ) - - target_link_libraries(freeImageInteropNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppif - CUDA::cudart - ${FreeImage_LIBRARIES} - ) - - # Copy data files to output directory - add_custom_command(TARGET freeImageInteropNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/data/teapot512.pgm - ${CMAKE_CURRENT_BINARY_DIR}/ - ) - if(WIN32) - add_custom_command(TARGET freeImageInteropNPP - POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy - ${FreeImage_LIBRARY}/../FreeImage.dll - ${CMAKE_CURRENT_BINARY_DIR}/$ - ) - endif() -else() - message(STATUS "FreeImage not found - will not build sample 'freeImageInteropNPP'") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/freeImageInteropNPP/README.md b/cpp/4_CUDA_Libraries/freeImageInteropNPP/README.md deleted file mode 100644 index d59152e3..00000000 --- a/cpp/4_CUDA_Libraries/freeImageInteropNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# freeImageInteropNPP - FreeImage and NPP Interopability - -## Description - -A simple CUDA Sample demonstrate how to use FreeImage library with NPP. - -## Key Concepts - -Performance Strategies, Image Processing, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaSetDevice, cudaGetDeviceCount, cudaDeviceInit, cudaDriverGetVersion, cudaGetDeviceProperties - -## Dependencies needed to build/run -[FreeImage](../../../README.md#freeimage), [NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/freeImageInteropNPP/freeImageInteropNPP.cpp b/cpp/4_CUDA_Libraries/freeImageInteropNPP/freeImageInteropNPP.cpp deleted file mode 100644 index 36987695..00000000 --- a/cpp/4_CUDA_Libraries/freeImageInteropNPP/freeImageInteropNPP.cpp +++ /dev/null @@ -1,325 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// A simple CUDA Sample demonstrates how to use FreeImage library with NPP. -// Detailed description of this example can be found as comments in the source -// code. - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#pragma warning(disable : 4819) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#endif - -// Common Helpers - -#include "Exceptions.h" -#include "FreeImage.h" - -// Other Includes -#include -#include -#include -#include // CUDA NPP Definitions -#include - -// Other helpers -#include // helper for CUDA Error handling and initialization -#include // helper for string parsing - -inline int cudaDeviceInit(int argc, const char **argv) -{ - int deviceCount; - checkCudaErrors(cudaGetDeviceCount(&deviceCount)); - - if (deviceCount == 0) { - std::cerr << "CUDA error: no devices supporting CUDA." << std::endl; - exit(EXIT_FAILURE); - } - - int dev = findCudaDevice(argc, argv); - - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, dev); - std::cerr << "cudaSetDevice GPU" << dev << " = " << deviceProp.name << std::endl; - - checkCudaErrors(cudaSetDevice(dev)); - - return dev; -} - -// Error handler for FreeImage library. -// In case this handler is invoked, it throws an NPP exception. -extern "C" void FreeImageErrorHandler(FREE_IMAGE_FORMAT oFif, const char *zMessage) { throw npp::Exception(zMessage); } - -std::ostream &operator<<(std::ostream &rOutputStream, const FIBITMAP &rBitmap) -{ - unsigned int nImageWidth = FreeImage_GetWidth(const_cast(&rBitmap)); - unsigned int nImageHeight = FreeImage_GetHeight(const_cast(&rBitmap)); - unsigned int nPitch = FreeImage_GetPitch(const_cast(&rBitmap)); - unsigned int nBPP = FreeImage_GetBPP(const_cast(&rBitmap)); - - FREE_IMAGE_COLOR_TYPE eType = FreeImage_GetColorType(const_cast(&rBitmap)); - - rOutputStream << "Size (" << nImageWidth << ", " << nImageHeight << ")\n"; - rOutputStream << "Pitch " << nPitch << "\n"; - rOutputStream << "Type "; - - switch (eType) { - case FIC_MINISWHITE: - rOutputStream << "FIC_MINISWHITE\n"; - break; - - case FIC_MINISBLACK: - rOutputStream << "FIC_MINISBLACK\n"; - break; - - case FIC_RGB: - rOutputStream << "FIC_RGB\n"; - break; - - case FIC_PALETTE: - rOutputStream << "FIC_PALETTE\n"; - break; - - case FIC_RGBALPHA: - rOutputStream << "FIC_RGBALPHA\n"; - break; - - case FIC_CMYK: - rOutputStream << "FIC_CMYK\n"; - break; - - default: - rOutputStream << "Unknown pixel format.\n"; - } - - rOutputStream << "BPP " << nBPP << std::endl; - - return rOutputStream; -} - -int main(int argc, char *argv[]) -{ - printf("%s Starting...\n\n", argv[0]); - - try { - std::string sFilename; - char *filePath; - - // set your own FreeImage error handler - FreeImage_SetOutputMessage(FreeImageErrorHandler); - - cudaDeviceInit(argc, (const char **)argv); - - NppStreamContext nppStreamCtx; - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError_t cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - if (checkCmdLineFlag(argc, (const char **)argv, "input")) { - getCmdLineArgumentString(argc, (const char **)argv, "input", &filePath); - } - else { - filePath = sdkFindFilePath("teapot512.pgm", argv[0]); - } - - if (filePath) { - sFilename = filePath; - } - else { - sFilename = "teapot512.pgm"; - } - - // if we specify the filename at the command line, then we only test - // sFilename otherwise we will check both sFilename[0,1] - int file_errors = 0; - std::ifstream infile(sFilename.data(), std::ifstream::in); - - if (infile.good()) { - std::cout << "freeImageInteropNPP opened: <" << sFilename.data() << "> successfully!" << std::endl; - file_errors = 0; - infile.close(); - } - else { - std::cout << "freeImageInteropNPP unable to open: <" << sFilename.data() << ">" << std::endl; - file_errors++; - infile.close(); - } - - if (file_errors > 0) { - exit(EXIT_FAILURE); - } - - std::string sResultFilename = sFilename; - - std::string::size_type dot = sResultFilename.rfind('.'); - - if (dot != std::string::npos) { - sResultFilename = sResultFilename.substr(0, dot); - } - - sResultFilename += "_boxFilterFII.pgm"; - - if (checkCmdLineFlag(argc, (const char **)argv, "output")) { - char *outputFilePath; - getCmdLineArgumentString(argc, (const char **)argv, "output", &outputFilePath); - sResultFilename = outputFilePath; - } - - FREE_IMAGE_FORMAT eFormat = FreeImage_GetFileType(sFilename.c_str()); - - // no signature? try to guess the file format from the file extension - if (eFormat == FIF_UNKNOWN) { - eFormat = FreeImage_GetFIFFromFilename(sFilename.c_str()); - } - - NPP_ASSERT(eFormat != FIF_UNKNOWN); - // check that the plugin has reading capabilities ... - FIBITMAP *pBitmap; - - if (FreeImage_FIFSupportsReading(eFormat)) { - pBitmap = FreeImage_Load(eFormat, sFilename.c_str()); - } - - NPP_ASSERT(pBitmap != 0); - // Dump the bitmap information to the console - std::cout << (*pBitmap) << std::endl; - // make sure this is an 8-bit single channel image - NPP_ASSERT(FreeImage_GetColorType(pBitmap) == FIC_MINISBLACK); - NPP_ASSERT(FreeImage_GetBPP(pBitmap) == 8); - - unsigned int nImageWidth = FreeImage_GetWidth(pBitmap); - unsigned int nImageHeight = FreeImage_GetHeight(pBitmap); - unsigned int nSrcPitch = FreeImage_GetPitch(pBitmap); - unsigned char *pSrcData = FreeImage_GetBits(pBitmap); - - int nSrcPitchCUDA; - Npp8u *pSrcImageCUDA = nppiMalloc_8u_C1(nImageWidth, nImageHeight, &nSrcPitchCUDA); - NPP_ASSERT_NOT_NULL(pSrcImageCUDA); - // copy image loaded via FreeImage to into CUDA device memory, i.e. - // transfer the image-data up to the GPU's video-memory - NPP_CHECK_CUDA(cudaMemcpy2D( - pSrcImageCUDA, nSrcPitchCUDA, pSrcData, nSrcPitch, nImageWidth, nImageHeight, cudaMemcpyHostToDevice)); - - // define size of the box filter - const NppiSize oMaskSize = {7, 7}; - const NppiPoint oMaskAchnor = {0, 0}; - // compute maximal result image size - const NppiSize oSizeROI = {(int)nImageWidth - (oMaskSize.width - 1), - (int)nImageHeight - (oMaskSize.height - 1)}; - // allocate result image memory - int nDstPitchCUDA; - Npp8u *pDstImageCUDA = nppiMalloc_8u_C1(oSizeROI.width, oSizeROI.height, &nDstPitchCUDA); - NPP_ASSERT_NOT_NULL(pDstImageCUDA); - NPP_CHECK_NPP(nppiFilterBox_8u_C1R_Ctx(pSrcImageCUDA, - nSrcPitchCUDA, - pDstImageCUDA, - nDstPitchCUDA, - oSizeROI, - oMaskSize, - oMaskAchnor, - nppStreamCtx)); - // create the result image storage using FreeImage so we can easily - // save - FIBITMAP *pResultBitmap = FreeImage_Allocate(oSizeROI.width, oSizeROI.height, 8 /* bits per pixel */); - NPP_ASSERT_NOT_NULL(pResultBitmap); - unsigned int nResultPitch = FreeImage_GetPitch(pResultBitmap); - unsigned char *pResultData = FreeImage_GetBits(pResultBitmap); - - NPP_CHECK_CUDA(cudaMemcpy2D(pResultData, - nResultPitch, - pDstImageCUDA, - nDstPitchCUDA, - oSizeROI.width, - oSizeROI.height, - cudaMemcpyDeviceToHost)); - // now save the result image - bool bSuccess; - bSuccess = FreeImage_Save(FIF_PGM, pResultBitmap, sResultFilename.c_str(), 0) == TRUE; - NPP_ASSERT_MSG(bSuccess, "Failed to save result image."); - - // free nppiImage - nppiFree(pSrcImageCUDA); - nppiFree(pDstImageCUDA); - - exit(EXIT_SUCCESS); - } - catch (npp::Exception &rException) { - std::cerr << "Program error! The following exception occurred: \n"; - std::cerr << rException << std::endl; - std::cerr << "Aborting." << std::endl; - exit(EXIT_FAILURE); - } - catch (...) { - std::cerr << "Program error! An unknow type of exception occurred. \n"; - std::cerr << "Aborting." << std::endl; - exit(EXIT_FAILURE); - } - - exit(EXIT_SUCCESS); -} diff --git a/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/histEqualizationNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/histEqualizationNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/histEqualizationNPP/CMakeLists.txt deleted file mode 100644 index 616d2c8a..00000000 --- a/cpp/4_CUDA_Libraries/histEqualizationNPP/CMakeLists.txt +++ /dev/null @@ -1,72 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(histEqualizationNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories( - ../../../Common - ../../../Common/UtilNPP -) - -# Source file -find_package(FreeImage) - -if(${FreeImage_FOUND}) - # Add target for histEqualizationNPP - add_executable(histEqualizationNPP histEqualizationNPP.cpp) - - target_compile_options(histEqualizationNPP PRIVATE $<$:--extended-lambda>) - - target_compile_features(histEqualizationNPP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(histEqualizationNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_include_directories(histEqualizationNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} - ${FreeImage_INCLUDE_DIRS} - ) - - target_link_libraries(histEqualizationNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppist - CUDA::nppicc - CUDA::cudart - ${FreeImage_LIBRARIES} - ) - - # Copy data files to output directory - add_custom_command(TARGET histEqualizationNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/data/teapot512.pgm - ${CMAKE_CURRENT_BINARY_DIR}/ - ) - if(WIN32) - add_custom_command(TARGET histEqualizationNPP - POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy - ${FreeImage_LIBRARY}/../FreeImage.dll - ${CMAKE_CURRENT_BINARY_DIR}/$ - ) - endif() -else() - message(STATUS "FreeImage not found - will not build sample 'histEqualizationNPP'") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/histEqualizationNPP/README.md b/cpp/4_CUDA_Libraries/histEqualizationNPP/README.md deleted file mode 100644 index e27ee2d5..00000000 --- a/cpp/4_CUDA_Libraries/histEqualizationNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# histEqualizationNPP - Histogram Equalization with NPP - -## Description - -This CUDA Sample demonstrates how to use NPP for histogram equalization for image data. - -## Key Concepts - -Image Processing, Performance Strategies, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaMemcpy, cudaFree, cudaSetDevice, cudaGetDeviceCount, cudaDeviceInit, cudaDriverGetVersion, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[FreeImage](../../../README.md#freeimage), [NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/histEqualizationNPP/histEqualizationNPP.cpp b/cpp/4_CUDA_Libraries/histEqualizationNPP/histEqualizationNPP.cpp deleted file mode 100644 index a0a43412..00000000 --- a/cpp/4_CUDA_Libraries/histEqualizationNPP/histEqualizationNPP.cpp +++ /dev/null @@ -1,326 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#pragma warning(disable : 4819) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#endif - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#define STRCASECMP _stricmp -#define STRNCASECMP _strnicmp -#else -#define STRCASECMP strcasecmp -#define STRNCASECMP strncasecmp -#endif - -inline int cudaDeviceInit(int argc, const char **argv) -{ - int deviceCount; - checkCudaErrors(cudaGetDeviceCount(&deviceCount)); - - if (deviceCount == 0) { - std::cerr << "CUDA error: no devices supporting CUDA." << std::endl; - exit(EXIT_FAILURE); - } - - int dev = findCudaDevice(argc, argv); - - cudaDeviceProp deviceProp; - cudaGetDeviceProperties(&deviceProp, dev); - std::cerr << "cudaSetDevice GPU" << dev << " = " << deviceProp.name << std::endl; - - checkCudaErrors(cudaSetDevice(dev)); - - return dev; -} - -int main(int argc, char *argv[]) -{ - printf("%s Starting...\n\n", argv[0]); - - try { - std::string sFilename; - char *filePath; - - cudaDeviceInit(argc, (const char **)argv); - - NppStreamContext nppStreamCtx; - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError_t cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - if (checkCmdLineFlag(argc, (const char **)argv, "input")) { - getCmdLineArgumentString(argc, (const char **)argv, "input", &filePath); - } - else { - filePath = sdkFindFilePath("teapot512.pgm", argv[0]); - } - - if (filePath) { - sFilename = filePath; - } - else { - sFilename = "teapot512.pgm"; - } - - // if we specify the filename at the command line, then we only test - // sFilename. - int file_errors = 0; - std::ifstream infile(sFilename.data(), std::ifstream::in); - - if (infile.good()) { - std::cout << "histEqualizationNPP opened: <" << sFilename.data() << "> successfully!" << std::endl; - file_errors = 0; - infile.close(); - } - else { - std::cout << "histEqualizationNPP unable to open: <" << sFilename.data() << ">" << std::endl; - file_errors++; - infile.close(); - } - - if (file_errors > 0) { - exit(EXIT_FAILURE); - } - - std::string dstFileName = sFilename; - - std::string::size_type dot = dstFileName.rfind('.'); - - if (dot != std::string::npos) { - dstFileName = dstFileName.substr(0, dot); - } - - dstFileName += "_histEqualization.pgm"; - - if (checkCmdLineFlag(argc, (const char **)argv, "output")) { - char *outputFilePath; - getCmdLineArgumentString(argc, (const char **)argv, "output", &outputFilePath); - dstFileName = outputFilePath; - } - - npp::ImageCPU_8u_C1 oHostSrc; - npp::loadImage(sFilename, oHostSrc); - npp::ImageNPP_8u_C1 oDeviceSrc(oHostSrc); - - // - // allocate arrays for histogram and levels - // - - const int binCount = 255; - const int levelCount = binCount + 1; // levels array has one more element - - Npp32s *histDevice = 0; - Npp32s *levelsDevice = 0; - - NPP_CHECK_CUDA(cudaMalloc((void **)&histDevice, binCount * sizeof(Npp32s))); - NPP_CHECK_CUDA(cudaMalloc((void **)&levelsDevice, levelCount * sizeof(Npp32s))); - - // - // compute histogram - // - - NppiSize oSizeROI = {(int)oDeviceSrc.width(), (int)oDeviceSrc.height()}; // full image - // create device scratch buffer for nppiHistogram - size_t nDeviceBufferSize; - nppiHistogramEvenGetBufferSize_8u_C1R_Ctx(oSizeROI, levelCount, &nDeviceBufferSize, nppStreamCtx); - Npp8u *pDeviceBuffer; - NPP_CHECK_CUDA(cudaMalloc((void **)&pDeviceBuffer, nDeviceBufferSize)); - - // compute levels values on host - Npp32s levelsHost[levelCount]; - NPP_CHECK_NPP(nppiEvenLevelsHost_32s(levelsHost, levelCount, 0, binCount)); - // compute the histogram - NPP_CHECK_NPP(nppiHistogramEven_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oSizeROI, - histDevice, - levelCount, - 0, - binCount, - pDeviceBuffer, - nppStreamCtx)); - // copy histogram and levels to host memory - Npp32s histHost[binCount]; - NPP_CHECK_CUDA(cudaMemcpy(histHost, histDevice, binCount * sizeof(Npp32s), cudaMemcpyDeviceToHost)); - - Npp32s lutHost[levelCount]; - - // fill LUT - { - Npp32s *pHostHistogram = histHost; - Npp32s totalSum = 0; - - for (; pHostHistogram < histHost + binCount; ++pHostHistogram) { - totalSum += *pHostHistogram; - } - - NPP_ASSERT(totalSum <= oSizeROI.width * oSizeROI.height); - - if (totalSum == 0) { - totalSum = 1; - } - - float multiplier = 1.0f / float(oSizeROI.width * oSizeROI.height) * 0xFF; - - Npp32s runningSum = 0; - Npp32s *pLookupTable = lutHost; - - for (pHostHistogram = histHost; pHostHistogram < histHost + binCount; ++pHostHistogram) { - *pLookupTable = (Npp32s)(runningSum * multiplier + 0.5f); - pLookupTable++; - runningSum += *pHostHistogram; - } - - lutHost[binCount] = 0xFF; // last element is always 1 - } - - // - // apply LUT transformation to the image - // - // Create a device image for the result. - npp::ImageNPP_8u_C1 oDeviceDst(oDeviceSrc.size()); - -#if CUDART_VERSION >= 5000 - // Note for CUDA 5.0, that nppiLUT_Linear_8u_C1R requires these pointers to - // be in GPU device memory - Npp32s *lutDevice = 0; - Npp32s *lvlsDevice = 0; - - NPP_CHECK_CUDA(cudaMalloc((void **)&lutDevice, sizeof(Npp32s) * (levelCount))); - NPP_CHECK_CUDA(cudaMalloc((void **)&lvlsDevice, sizeof(Npp32s) * (levelCount))); - - NPP_CHECK_CUDA(cudaMemcpy(lutDevice, lutHost, sizeof(Npp32s) * (levelCount), cudaMemcpyHostToDevice)); - NPP_CHECK_CUDA(cudaMemcpy(lvlsDevice, levelsHost, sizeof(Npp32s) * (levelCount), cudaMemcpyHostToDevice)); - - NPP_CHECK_NPP(nppiLUT_Linear_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oDeviceDst.data(), - oDeviceDst.pitch(), - oSizeROI, - lutDevice, // value and level arrays are in host memory - lvlsDevice, - levelCount, - nppStreamCtx)); - - NPP_CHECK_CUDA(cudaFree(lutDevice)); - NPP_CHECK_CUDA(cudaFree(lvlsDevice)); -#else - NPP_CHECK_NPP(nppiLUT_Linear_8u_C1R_Ctx(oDeviceSrc.data(), - oDeviceSrc.pitch(), - oDeviceDst.data(), - oDeviceDst.pitch(), - oSizeROI, - lutHost, // value and level arrays are in host memory - levelsHost, - levelCount, - nppStreamCtx)); -#endif - - // copy the result image back into the storage that contained the - // input image - npp::ImageCPU_8u_C1 oHostDst(oDeviceDst.size()); - oDeviceDst.copyTo(oHostDst.data(), oHostDst.pitch()); - - cudaFree(histDevice); - cudaFree(levelsDevice); - cudaFree(pDeviceBuffer); - nppiFree(oDeviceSrc.data()); - nppiFree(oDeviceDst.data()); - - // save the result - npp::saveImage(dstFileName.c_str(), oHostDst); - std::cout << "Saved image file " << dstFileName << std::endl; - exit(EXIT_SUCCESS); - } - catch (npp::Exception &rException) { - std::cerr << "Program error! The following exception occurred: \n"; - std::cerr << rException << std::endl; - std::cerr << "Aborting." << std::endl; - exit(EXIT_FAILURE); - } - catch (...) { - std::cerr << "Program error! An unknow type of exception occurred. \n"; - std::cerr << "Aborting." << std::endl; - exit(EXIT_FAILURE); - } - - return 0; -} diff --git a/cpp/4_CUDA_Libraries/jitLto/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/jitLto/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/jitLto/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/jitLto/.vscode/extensions.json b/cpp/4_CUDA_Libraries/jitLto/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/jitLto/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/jitLto/CMakeLists.txt b/cpp/4_CUDA_Libraries/jitLto/CMakeLists.txt index 9baef1d8..19a2a4d3 100644 --- a/cpp/4_CUDA_Libraries/jitLto/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/jitLto/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(jitLto LANGUAGES CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # This sample is not supported on QNX @@ -25,16 +21,12 @@ if(CMAKE_SYSTEM_NAME STREQUAL "QNX") return() endif() -# Source file -# Add target for jitLto add_executable(jitLto jitLto.cpp) target_compile_options(jitLto PRIVATE $<$:--extended-lambda>) target_compile_features(jitLto PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(jitLto PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - find_library(NVJITLINK_LIB NAME nvJitLink PATHS "${CUDAToolkit_LIBRARY_DIR}" "${CMAKE_LIBRARY_PATH}") @@ -49,6 +41,5 @@ target_link_libraries(jitLto PRIVATE ${NVJITLINK_LIB} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/libcuxxMdspan/CMakeLists.txt b/cpp/4_CUDA_Libraries/libcuxxMdspan/CMakeLists.txt index 5f66f5f1..bc748ac6 100644 --- a/cpp/4_CUDA_Libraries/libcuxxMdspan/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/libcuxxMdspan/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(libcuxxMdspan LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -11,16 +18,6 @@ endif() find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Fetch CCCL (CUB + libcu++ + Thrust) via CPM. The toolkit that ships # with CUDA 13.2 bundles CCCL 3.2, but this sample uses APIs added in # CCCL 3.3. Pinning the tag here lets the sample build on any toolkit @@ -68,25 +65,19 @@ if(NOT TARGET dlpack::dlpack) endif() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for libcuxxMdspan add_executable(libcuxxMdspan libcuxxMdspan.cu) target_compile_options(libcuxxMdspan PRIVATE $<$:--extended-lambda>) target_compile_features(libcuxxMdspan PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(libcuxxMdspan PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(libcuxxMdspan PRIVATE CUDA::cudart CCCL::CCCL dlpack::dlpack ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/libcuxxMdspan/libcuxxMdspan.cu b/cpp/4_CUDA_Libraries/libcuxxMdspan/libcuxxMdspan.cu index 90016faa..f47145c0 100644 --- a/cpp/4_CUDA_Libraries/libcuxxMdspan/libcuxxMdspan.cu +++ b/cpp/4_CUDA_Libraries/libcuxxMdspan/libcuxxMdspan.cu @@ -113,6 +113,26 @@ struct DLTensorStorage ::DLTensor tensor{}; cuda::std::array shape{}; cuda::std::array strides{}; + + DLTensorStorage() = default; + + /* tensor.shape / tensor.strides point back into this object's own shape / + * strides members, so the object is self-referential. Any copy or move + * (including the by-value return from make_row_major_dltensor) must re-seat + * those pointers at the new location. Relying on NRVO to elide the copy is + * not portable -- NRVO is not guaranteed for named returns -- and a stale + * pointer into a moved-from temporary makes cuda::to_device_mdspan read + * garbage extents and abort. */ + DLTensorStorage(const DLTensorStorage &other) { *this = other; } + DLTensorStorage &operator=(const DLTensorStorage &other) + { + tensor = other.tensor; + shape = other.shape; + strides = other.strides; + tensor.shape = shape.data(); + tensor.strides = strides.data(); + return *this; + } }; static DLTensorStorage make_row_major_dltensor(float *device_ptr, int rows, int cols, int device_ordinal) diff --git a/cpp/4_CUDA_Libraries/libcuxxRandom/CMakeLists.txt b/cpp/4_CUDA_Libraries/libcuxxRandom/CMakeLists.txt index 1164c560..470e2408 100644 --- a/cpp/4_CUDA_Libraries/libcuxxRandom/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/libcuxxRandom/CMakeLists.txt @@ -1,9 +1,16 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(libcuxxRandom LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + # Disable response file for libraries on QNX as qcc does not support lib paths with double quotes if(CMAKE_SYSTEM_NAME STREQUAL "QNX") set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) @@ -11,16 +18,6 @@ endif() find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - # Fetch CCCL (CUB + libcu++ + Thrust) via CPM. The toolkit that ships # with CUDA 13.2 bundles CCCL 3.2, but this sample uses APIs added in # CCCL 3.3. Pinning the tag here lets the sample build on any toolkit @@ -42,24 +39,18 @@ if(NOT TARGET CCCL::CCCL) endif() endif() -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for libcuxxRandom add_executable(libcuxxRandom libcuxxRandom.cu) target_compile_options(libcuxxRandom PRIVATE $<$:--extended-lambda>) target_compile_features(libcuxxRandom PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(libcuxxRandom PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_link_libraries(libcuxxRandom PRIVATE CUDA::cudart CCCL::CCCL ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/lineOfSight/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/lineOfSight/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/lineOfSight/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/lineOfSight/.vscode/extensions.json b/cpp/4_CUDA_Libraries/lineOfSight/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/lineOfSight/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/lineOfSight/CMakeLists.txt b/cpp/4_CUDA_Libraries/lineOfSight/CMakeLists.txt index a77c1b53..c6d4d620 100644 --- a/cpp/4_CUDA_Libraries/lineOfSight/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/lineOfSight/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(lineOfSight LANGUAGES CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for lineOfSight add_executable(lineOfSight lineOfSight.cu) target_compile_options(lineOfSight PRIVATE $<$:--extended-lambda>) target_compile_features(lineOfSight PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(lineOfSight PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/lineOfSight/lineOfSight.cu b/cpp/4_CUDA_Libraries/lineOfSight/lineOfSight.cu index 0bb46993..d3319012 100644 --- a/cpp/4_CUDA_Libraries/lineOfSight/lineOfSight.cu +++ b/cpp/4_CUDA_Libraries/lineOfSight/lineOfSight.cu @@ -215,6 +215,9 @@ int runTest(int argc, char **argv) ////////////////////////////////////////////////////////////////////////////// // Device solution + // Enlarge the per-thread device stack: under -G the CUB warpspeed inclusive_scan is not inlined and its deep call chain overflows the default 1KB stack. + checkCudaErrors(cudaDeviceSetLimit(cudaLimitStackSize, 8192)); + // Execution configuration dim3 block(256); dim3 grid((uint)ceil(ray.length / (double)block.x)); diff --git a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/extensions.json b/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/CMakeLists.txt b/cpp/4_CUDA_Libraries/matrixMulCUBLAS/CMakeLists.txt deleted file mode 100644 index 260cf239..00000000 --- a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(matrixMulCUBLAS LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for matrixMulCUBLAS -add_executable(matrixMulCUBLAS matrixMulCUBLAS.cpp) - -target_compile_options(matrixMulCUBLAS PRIVATE $<$:--extended-lambda>) - -target_compile_features(matrixMulCUBLAS PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(matrixMulCUBLAS PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(matrixMulCUBLAS PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(matrixMulCUBLAS PRIVATE - CUDA::cudart - CUDA::cublas -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/README.md b/cpp/4_CUDA_Libraries/matrixMulCUBLAS/README.md deleted file mode 100644 index 2d4b9b8e..00000000 --- a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# matrixMulCUBLAS - Matrix Multiplication (CUBLAS) - -## Description - -This sample implements matrix multiplication from Chapter 3 of the programming guide. To illustrate GPU performance for matrix multiply, this sample also shows how to use the new CUDA 4.0 interface for CUBLAS to demonstrate high-performance performance for matrix multiplication. - -## Key Concepts - -CUDA Runtime API, Performance Strategies, Linear Algebra, CUBLAS - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaFree, cudaEventSynchronize, cudaEventRecord, cudaMalloc, cudaEventElapsedTime, cudaGetDeviceProperties, cudaEventCreate - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/matrixMulCUBLAS.cpp b/cpp/4_CUDA_Libraries/matrixMulCUBLAS/matrixMulCUBLAS.cpp deleted file mode 100644 index 0a5adb0b..00000000 --- a/cpp/4_CUDA_Libraries/matrixMulCUBLAS/matrixMulCUBLAS.cpp +++ /dev/null @@ -1,388 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* -* Matrix multiplication: C = A * B. -* Host code. -* -* This sample implements matrix multiplication as described in Chapter 3 -* of the programming guide and uses the CUBLAS library to demonstrate -* the best performance. - -* SOME PRECAUTIONS: -* IF WE WANT TO CALCULATE ROW-MAJOR MATRIX MULTIPLY C = A * B, -* WE JUST NEED CALL CUBLAS API IN A REVERSE ORDER: cublasSegemm(B, A)! -* The reason is explained as follows: - -* CUBLAS library uses column-major storage, but C/C++ use row-major storage. -* When passing the matrix pointer to CUBLAS, the memory layout alters from -* row-major to column-major, which is equivalent to an implicit transpose. - -* In the case of row-major C/C++ matrix A, B, and a simple matrix multiplication -* C = A * B, we can't use the input order like cublasSgemm(A, B) because of -* implicit transpose. The actual result of cublasSegemm(A, B) is A(T) * B(T). -* If col(A(T)) != row(B(T)), equal to row(A) != col(B), A(T) and B(T) are not -* multipliable. Moreover, even if A(T) and B(T) are multipliable, the result C -* is a column-based cublas matrix, which means C(T) in C/C++, we need extra -* transpose code to convert it to a row-based C/C++ matrix. - -* To solve the problem, let's consider our desired result C, a row-major matrix. -* In cublas format, it is C(T) actually (because of the implicit transpose). -* C = A * B, so C(T) = (A * B) (T) = B(T) * A(T). Cublas matrice B(T) and A(T) -* happen to be C/C++ matrice B and A (still because of the implicit transpose)! -* We don't need extra transpose code, we only need alter the input order! -* -* CUBLAS provides high-performance matrix multiplication. -* See also: -* V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra," -* in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08), -* Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11. -*/ - -// Utilities and system includes -#include -#include // helper for shared functions common to CUDA Samples - -// CUDA runtime -#include -#include - -// CUDA and CUBLAS functions -#include -#include - -#ifndef min -#define min(a, b) ((a < b) ? a : b) -#endif -#ifndef max -#define max(a, b) ((a > b) ? a : b) -#endif - -// Optional Command-line multiplier for matrix sizes -typedef struct _matrixSize -{ - unsigned int uiWA, uiHA, uiWB, uiHB, uiWC, uiHC; -} sMatrixSize; - -//////////////////////////////////////////////////////////////////////////////// -//! Compute reference data set matrix multiply on CPU -//! C = A * B -//! @param C reference data, computed but preallocated -//! @param A matrix A as provided to device -//! @param B matrix B as provided to device -//! @param hA height of matrix A -//! @param wB width of matrix B -//////////////////////////////////////////////////////////////////////////////// -void matrixMulCPU(float *C, const float *A, const float *B, unsigned int hA, unsigned int wA, unsigned int wB) -{ - for (unsigned int i = 0; i < hA; ++i) - for (unsigned int j = 0; j < wB; ++j) { - double sum = 0; - - for (unsigned int k = 0; k < wA; ++k) { - double a = A[i * wA + k]; - double b = B[k * wB + j]; - sum += a * b; - } - - C[i * wB + j] = (float)sum; - } -} - -// Allocates a matrix with random float entries. -void randomInit(float *data, int size) -{ - for (int i = 0; i < size; ++i) - data[i] = rand() / (float)RAND_MAX; -} - -void printDiff(float *data1, float *data2, int width, int height, int iListLength, float fListTol) -{ - printf("Listing first %d Differences > %.6f...\n", iListLength, fListTol); - int i, j, k; - int error_count = 0; - - for (j = 0; j < height; j++) { - if (error_count < iListLength) { - printf("\n Row %d:\n", j); - } - - for (i = 0; i < width; i++) { - k = j * width + i; - float fDiff = fabs(data1[k] - data2[k]); - - if (fDiff > fListTol) { - if (error_count < iListLength) { - printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j, data1[k], data2[k], fDiff); - } - - error_count++; - } - } - } - - printf(" \n Total Errors = %d\n", error_count); -} - -void initializeCUDA(int argc, char **argv, int &devID, int &iSizeMultiple, sMatrixSize &matrix_size) -{ - // By default, we use device 0, otherwise we override the device ID based on - // what is provided at the command line - cudaError_t error; - devID = 0; - - devID = findCudaDevice(argc, (const char **)argv); - - if (checkCmdLineFlag(argc, (const char **)argv, "sizemult")) { - iSizeMultiple = getCmdLineArgumentInt(argc, (const char **)argv, "sizemult"); - } - - iSizeMultiple = min(iSizeMultiple, 10); - iSizeMultiple = max(iSizeMultiple, 1); - - cudaDeviceProp deviceProp; - - error = cudaGetDeviceProperties(&deviceProp, devID); - - if (error != cudaSuccess) { - printf("cudaGetDeviceProperties returned error code %d, line(%d)\n", error, __LINE__); - exit(EXIT_FAILURE); - } - - printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", - devID, - deviceProp.name, - deviceProp.major, - deviceProp.minor); - - int block_size = 32; - - matrix_size.uiWA = 3 * block_size * iSizeMultiple; - matrix_size.uiHA = 4 * block_size * iSizeMultiple; - matrix_size.uiWB = 2 * block_size * iSizeMultiple; - matrix_size.uiHB = 3 * block_size * iSizeMultiple; - matrix_size.uiWC = 2 * block_size * iSizeMultiple; - matrix_size.uiHC = 4 * block_size * iSizeMultiple; - - printf("MatrixA(%u,%u), MatrixB(%u,%u), MatrixC(%u,%u)\n", - matrix_size.uiHA, - matrix_size.uiWA, - matrix_size.uiHB, - matrix_size.uiWB, - matrix_size.uiHC, - matrix_size.uiWC); - - if (matrix_size.uiWA != matrix_size.uiHB || matrix_size.uiHA != matrix_size.uiHC - || matrix_size.uiWB != matrix_size.uiWC) { - printf("ERROR: Matrix sizes do not match!\n"); - exit(-1); - } -} - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test matrix multiply using CUBLAS -//////////////////////////////////////////////////////////////////////////////// -int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size) -{ - cudaDeviceProp deviceProp; - - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID)); - - int block_size = 32; - - // set seed for rand() - srand(2006); - - // allocate host memory for matrices A and B - unsigned int size_A = matrix_size.uiWA * matrix_size.uiHA; - unsigned int mem_size_A = sizeof(float) * size_A; - float *h_A = (float *)malloc(mem_size_A); - unsigned int size_B = matrix_size.uiWB * matrix_size.uiHB; - unsigned int mem_size_B = sizeof(float) * size_B; - float *h_B = (float *)malloc(mem_size_B); - - // set seed for rand() - srand(2006); - - // initialize host memory - randomInit(h_A, size_A); - randomInit(h_B, size_B); - - // allocate device memory - float *d_A, *d_B, *d_C; - unsigned int size_C = matrix_size.uiWC * matrix_size.uiHC; - unsigned int mem_size_C = sizeof(float) * size_C; - - // allocate host memory for the result - float *h_C = (float *)malloc(mem_size_C); - float *h_CUBLAS = (float *)malloc(mem_size_C); - - checkCudaErrors(cudaMalloc((void **)&d_A, mem_size_A)); - checkCudaErrors(cudaMalloc((void **)&d_B, mem_size_B)); - checkCudaErrors(cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMemcpy(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice)); - checkCudaErrors(cudaMalloc((void **)&d_C, mem_size_C)); - - // setup execution parameters - dim3 threads(block_size, block_size); - dim3 grid(matrix_size.uiWC / threads.x, matrix_size.uiHC / threads.y); - - // create and start timer - printf("Computing result using CUBLAS..."); - - // execute the kernel - int nIter = 30; - - // CUBLAS version 2.0 - { - const float alpha = 1.0f; - const float beta = 0.0f; - cublasHandle_t handle; - cudaEvent_t start, stop; - - checkCudaErrors(cublasCreate(&handle)); - - // Perform warmup operation with cublas - checkCudaErrors(cublasSgemm(handle, - CUBLAS_OP_N, - CUBLAS_OP_N, - matrix_size.uiWB, - matrix_size.uiHA, - matrix_size.uiWA, - &alpha, - d_B, - matrix_size.uiWB, - d_A, - matrix_size.uiWA, - &beta, - d_C, - matrix_size.uiWB)); - - // Allocate CUDA events that we'll use for timing - checkCudaErrors(cudaEventCreate(&start)); - checkCudaErrors(cudaEventCreate(&stop)); - - // Record the start event - checkCudaErrors(cudaEventRecord(start, NULL)); - - for (int j = 0; j < nIter; j++) { - // note cublas is column primary! - // need to transpose the order - checkCudaErrors(cublasSgemm(handle, - CUBLAS_OP_N, - CUBLAS_OP_N, - matrix_size.uiWB, - matrix_size.uiHA, - matrix_size.uiWA, - &alpha, - d_B, - matrix_size.uiWB, - d_A, - matrix_size.uiWA, - &beta, - d_C, - matrix_size.uiWB)); - } - - printf("done.\n"); - - // Record the stop event - checkCudaErrors(cudaEventRecord(stop, NULL)); - - // Wait for the stop event to complete - checkCudaErrors(cudaEventSynchronize(stop)); - - float msecTotal = 0.0f; - checkCudaErrors(cudaEventElapsedTime(&msecTotal, start, stop)); - - // Compute and print the performance - float msecPerMatrixMul = msecTotal / nIter; - double flopsPerMatrixMul = 2.0 * (double)matrix_size.uiHC * (double)matrix_size.uiWC * (double)matrix_size.uiHB; - double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f); - printf("Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops\n", - gigaFlops, - msecPerMatrixMul, - flopsPerMatrixMul); - - // copy result from device to host - checkCudaErrors(cudaMemcpy(h_CUBLAS, d_C, mem_size_C, cudaMemcpyDeviceToHost)); - - // Destroy the handle - checkCudaErrors(cublasDestroy(handle)); - } - - // compute reference solution - printf("Computing result using host CPU..."); - float *reference = (float *)malloc(mem_size_C); - matrixMulCPU(reference, h_A, h_B, matrix_size.uiHA, matrix_size.uiWA, matrix_size.uiWB); - printf("done.\n"); - - // check result (CUBLAS) - bool resCUBLAS = sdkCompareL2fe(reference, h_CUBLAS, size_C, 1.0e-6f); - - if (resCUBLAS != true) { - printDiff(reference, h_CUBLAS, matrix_size.uiWC, matrix_size.uiHC, 100, 1.0e-5f); - } - - printf("Comparing CUBLAS Matrix Multiply with CPU results: %s\n", (true == resCUBLAS) ? "PASS" : "FAIL"); - - printf("\nNOTE: The CUDA Samples are not meant for performance measurements. " - "Results may vary when GPU Boost is enabled.\n"); - - // clean up memory - free(h_A); - free(h_B); - free(h_C); - free(reference); - checkCudaErrors(cudaFree(d_A)); - checkCudaErrors(cudaFree(d_B)); - checkCudaErrors(cudaFree(d_C)); - - if (resCUBLAS == true) { - return EXIT_SUCCESS; // return value = 1 - } - else { - return EXIT_FAILURE; // return value = 0 - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - printf("[Matrix Multiply CUBLAS] - Starting...\n"); - - int devID = 0, sizeMult = 5; - sMatrixSize matrix_size; - - initializeCUDA(argc, argv, devID, sizeMult, matrix_size); - - int matrix_result = matrixMultiply(argc, argv, devID, matrix_size); - - return matrix_result; -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/nvJPEG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG/.vscode/extensions.json b/cpp/4_CUDA_Libraries/nvJPEG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG/CMakeLists.txt b/cpp/4_CUDA_Libraries/nvJPEG/CMakeLists.txt deleted file mode 100644 index 215cc96e..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG/CMakeLists.txt +++ /dev/null @@ -1,50 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(nvJPEG LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for nvJPEG -add_executable(nvJPEG nvJPEG.cpp) - -target_compile_options(nvJPEG PRIVATE $<$:--extended-lambda>) - -target_compile_features(nvJPEG PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(nvJPEG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(nvJPEG PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(nvJPEG PRIVATE - CUDA::cudart - CUDA::nvjpeg -) - -# Copy data to the output directory -add_custom_command(TARGET nvJPEG POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CMAKE_CURRENT_SOURCE_DIR}/images - ${CMAKE_CURRENT_BINARY_DIR}/images -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/nvJPEG/README.md b/cpp/4_CUDA_Libraries/nvJPEG/README.md deleted file mode 100644 index 973834d5..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# nvJPEG - NVJPEG simple - -## Description - -A CUDA Sample that demonstrates single and batched decoding of jpeg images using NVJPEG Library. - -## Key Concepts - -Image Decoding, NVJPEG Library - -## Supported SM Architectures - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaHostAlloc, cudaStreamCreateWithFlags, cudaStreamDestroy, cudaFree, cudaEventSynchronize, cudaEventRecord, cudaFreeHost, cudaStreamSynchronize, cudaMalloc, cudaEventElapsedTime, cudaGetDeviceProperties, cudaEventCreate - -## Dependencies needed to build/run -[NVJPEG](../../../README.md#nvjpeg) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img1.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img1.jpg deleted file mode 100644 index 7413faf0..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img1.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img2.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img2.jpg deleted file mode 100644 index d5b53b26..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img2.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img3.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img3.jpg deleted file mode 100644 index 41b1d9b4..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img3.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img4.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img4.jpg deleted file mode 100644 index 17fa69ef..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img4.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img5.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img5.jpg deleted file mode 100644 index 148ccb41..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img5.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img6.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img6.jpg deleted file mode 100644 index a58ef7c5..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img6.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img7.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img7.jpg deleted file mode 100644 index f49d9122..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img7.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/images/img8.jpg b/cpp/4_CUDA_Libraries/nvJPEG/images/img8.jpg deleted file mode 100644 index ab51a789..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG/images/img8.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG/nvJPEG.cpp b/cpp/4_CUDA_Libraries/nvJPEG/nvJPEG.cpp deleted file mode 100644 index ab3644bd..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG/nvJPEG.cpp +++ /dev/null @@ -1,640 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// This sample needs at least CUDA 10.0. It demonstrates usages of the nvJPEG -// library nvJPEG supports single and multiple image(batched) decode. Multiple -// images can be decoded using the API for batch mode - -#include - -#include "helper_nvJPEG.hxx" - -int dev_malloc(void **p, size_t s) { return (int)cudaMalloc(p, s); } - -int dev_free(void *p) { return (int)cudaFree(p); } - -int host_malloc(void **p, size_t s, unsigned int f) { return (int)cudaHostAlloc(p, s, f); } - -int host_free(void *p) { return (int)cudaFreeHost(p); } - -typedef std::vector FileNames; -typedef std::vector> FileData; - -struct decode_params_t -{ - std::string input_dir; - int batch_size; - int total_images; - int dev; - int warmup; - - nvjpegJpegState_t nvjpeg_state; - nvjpegHandle_t nvjpeg_handle; - cudaStream_t stream; - - // used with decoupled API - nvjpegJpegState_t nvjpeg_decoupled_state; - nvjpegBufferPinned_t pinned_buffers[2]; // 2 buffers for pipelining - nvjpegBufferDevice_t device_buffer; - nvjpegJpegStream_t jpeg_streams[2]; // 2 streams for pipelining - nvjpegDecodeParams_t nvjpeg_decode_params; - nvjpegJpegDecoder_t nvjpeg_decoder; - - nvjpegOutputFormat_t fmt; - bool write_decoded; - std::string output_dir; - - bool pipelined; - bool batched; -}; - -int read_next_batch(FileNames &image_names, - int batch_size, - FileNames::iterator &cur_iter, - FileData &raw_data, - std::vector &raw_len, - FileNames ¤t_names) -{ - int counter = 0; - - while (counter < batch_size) { - if (cur_iter == image_names.end()) { - std::cerr << "Image list is too short to fill the batch, adding files " - "from the beginning of the image list" - << std::endl; - cur_iter = image_names.begin(); - } - - if (image_names.size() == 0) { - std::cerr << "No valid images left in the input list, exit" << std::endl; - return EXIT_FAILURE; - } - - // Read an image from disk. - std::ifstream input(cur_iter->c_str(), std::ios::in | std::ios::binary | std::ios::ate); - if (!(input.is_open())) { - std::cerr << "Cannot open image: " << *cur_iter << ", removing it from image list" << std::endl; - image_names.erase(cur_iter); - continue; - } - - // Get the size - std::streamsize file_size = input.tellg(); - input.seekg(0, std::ios::beg); - // resize if buffer is too small - if (raw_data[counter].size() < file_size) { - raw_data[counter].resize(file_size); - } - if (!input.read(raw_data[counter].data(), file_size)) { - std::cerr << "Cannot read from file: " << *cur_iter << ", removing it from image list" << std::endl; - image_names.erase(cur_iter); - continue; - } - raw_len[counter] = file_size; - - current_names[counter] = *cur_iter; - - counter++; - cur_iter++; - } - return EXIT_SUCCESS; -} - -// prepare buffers for RGBi output format -int prepare_buffers(FileData &file_data, - std::vector &file_len, - std::vector &img_width, - std::vector &img_height, - std::vector &ibuf, - std::vector &isz, - FileNames ¤t_names, - decode_params_t ¶ms) -{ - int widths[NVJPEG_MAX_COMPONENT]; - int heights[NVJPEG_MAX_COMPONENT]; - int channels; - nvjpegChromaSubsampling_t subsampling; - - for (int i = 0; i < file_data.size(); i++) { - checkCudaErrors(nvjpegGetImageInfo(params.nvjpeg_handle, - (unsigned char *)file_data[i].data(), - file_len[i], - &channels, - &subsampling, - widths, - heights)); - - img_width[i] = widths[0]; - img_height[i] = heights[0]; - - std::cout << "Processing: " << current_names[i] << std::endl; - std::cout << "Image is " << channels << " channels." << std::endl; - for (int c = 0; c < channels; c++) { - std::cout << "Channel #" << c << " size: " << widths[c] << " x " << heights[c] << std::endl; - } - - switch (subsampling) { - case NVJPEG_CSS_444: - std::cout << "YUV 4:4:4 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_440: - std::cout << "YUV 4:4:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_422: - std::cout << "YUV 4:2:2 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_420: - std::cout << "YUV 4:2:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_411: - std::cout << "YUV 4:1:1 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_410: - std::cout << "YUV 4:1:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_GRAY: - std::cout << "Grayscale JPEG " << std::endl; - break; - case NVJPEG_CSS_UNKNOWN: - std::cout << "Unknown chroma subsampling" << std::endl; - return EXIT_FAILURE; - } - - int mul = 1; - // in the case of interleaved RGB output, write only to single channel, but - // 3 samples at once - if (params.fmt == NVJPEG_OUTPUT_RGBI || params.fmt == NVJPEG_OUTPUT_BGRI) { - channels = 1; - mul = 3; - } - // in the case of rgb create 3 buffers with sizes of original image - else if (params.fmt == NVJPEG_OUTPUT_RGB || params.fmt == NVJPEG_OUTPUT_BGR) { - channels = 3; - widths[1] = widths[2] = widths[0]; - heights[1] = heights[2] = heights[0]; - } - - // realloc output buffer if required - for (int c = 0; c < channels; c++) { - int aw = mul * widths[c]; - int ah = heights[c]; - int sz = aw * ah; - ibuf[i].pitch[c] = aw; - if (sz > isz[i].pitch[c]) { - if (ibuf[i].channel[c]) { - checkCudaErrors(cudaFree(ibuf[i].channel[c])); - } - checkCudaErrors(cudaMalloc(&ibuf[i].channel[c], sz)); - isz[i].pitch[c] = sz; - } - } - } - return EXIT_SUCCESS; -} - -void create_decoupled_api_handles(decode_params_t ¶ms) -{ - - checkCudaErrors(nvjpegDecoderCreate(params.nvjpeg_handle, NVJPEG_BACKEND_DEFAULT, ¶ms.nvjpeg_decoder)); - checkCudaErrors( - nvjpegDecoderStateCreate(params.nvjpeg_handle, params.nvjpeg_decoder, ¶ms.nvjpeg_decoupled_state)); - - checkCudaErrors(nvjpegBufferPinnedCreate(params.nvjpeg_handle, NULL, ¶ms.pinned_buffers[0])); - checkCudaErrors(nvjpegBufferPinnedCreate(params.nvjpeg_handle, NULL, ¶ms.pinned_buffers[1])); - checkCudaErrors(nvjpegBufferDeviceCreate(params.nvjpeg_handle, NULL, ¶ms.device_buffer)); - - checkCudaErrors(nvjpegJpegStreamCreate(params.nvjpeg_handle, ¶ms.jpeg_streams[0])); - checkCudaErrors(nvjpegJpegStreamCreate(params.nvjpeg_handle, ¶ms.jpeg_streams[1])); - - checkCudaErrors(nvjpegDecodeParamsCreate(params.nvjpeg_handle, ¶ms.nvjpeg_decode_params)); -} - -void destroy_decoupled_api_handles(decode_params_t ¶ms) -{ - - checkCudaErrors(nvjpegDecodeParamsDestroy(params.nvjpeg_decode_params)); - checkCudaErrors(nvjpegJpegStreamDestroy(params.jpeg_streams[0])); - checkCudaErrors(nvjpegJpegStreamDestroy(params.jpeg_streams[1])); - checkCudaErrors(nvjpegBufferPinnedDestroy(params.pinned_buffers[0])); - checkCudaErrors(nvjpegBufferPinnedDestroy(params.pinned_buffers[1])); - checkCudaErrors(nvjpegBufferDeviceDestroy(params.device_buffer)); - checkCudaErrors(nvjpegJpegStateDestroy(params.nvjpeg_decoupled_state)); - checkCudaErrors(nvjpegDecoderDestroy(params.nvjpeg_decoder)); -} - -void release_buffers(std::vector &ibuf) -{ - for (int i = 0; i < ibuf.size(); i++) { - for (int c = 0; c < NVJPEG_MAX_COMPONENT; c++) - if (ibuf[i].channel[c]) - checkCudaErrors(cudaFree(ibuf[i].channel[c])); - } -} - -int decode_images(const FileData &img_data, - const std::vector &img_len, - std::vector &out, - decode_params_t ¶ms, - double &time) -{ - checkCudaErrors(cudaStreamSynchronize(params.stream)); - cudaEvent_t startEvent = NULL, stopEvent = NULL; - float loopTime = 0; - - checkCudaErrors(cudaEventCreate(&startEvent, cudaEventBlockingSync)); - checkCudaErrors(cudaEventCreate(&stopEvent, cudaEventBlockingSync)); - - if (!params.batched) { - if (!params.pipelined) // decode one image at a time - { - checkCudaErrors(cudaEventRecord(startEvent, params.stream)); - for (int i = 0; i < params.batch_size; i++) { - checkCudaErrors(nvjpegDecode(params.nvjpeg_handle, - params.nvjpeg_state, - (const unsigned char *)img_data[i].data(), - img_len[i], - params.fmt, - &out[i], - params.stream)); - } - checkCudaErrors(cudaEventRecord(stopEvent, params.stream)); - } - else { - // use de-coupled API in pipelined mode - checkCudaErrors(cudaEventRecord(startEvent, params.stream)); - checkCudaErrors(nvjpegStateAttachDeviceBuffer(params.nvjpeg_decoupled_state, params.device_buffer)); - int buffer_index = 0; - checkCudaErrors(nvjpegDecodeParamsSetOutputFormat(params.nvjpeg_decode_params, params.fmt)); - for (int i = 0; i < params.batch_size; i++) { - checkCudaErrors(nvjpegJpegStreamParse(params.nvjpeg_handle, - (const unsigned char *)img_data[i].data(), - img_len[i], - 0, - 0, - params.jpeg_streams[buffer_index])); - - checkCudaErrors( - nvjpegStateAttachPinnedBuffer(params.nvjpeg_decoupled_state, params.pinned_buffers[buffer_index])); - - checkCudaErrors(nvjpegDecodeJpegHost(params.nvjpeg_handle, - params.nvjpeg_decoder, - params.nvjpeg_decoupled_state, - params.nvjpeg_decode_params, - params.jpeg_streams[buffer_index])); - - checkCudaErrors(cudaStreamSynchronize(params.stream)); - - checkCudaErrors(nvjpegDecodeJpegTransferToDevice(params.nvjpeg_handle, - params.nvjpeg_decoder, - params.nvjpeg_decoupled_state, - params.jpeg_streams[buffer_index], - params.stream)); - - buffer_index = 1 - buffer_index; // switch pinned buffer in pipeline mode to avoid an extra sync - - checkCudaErrors(nvjpegDecodeJpegDevice(params.nvjpeg_handle, - params.nvjpeg_decoder, - params.nvjpeg_decoupled_state, - &out[i], - params.stream)); - } - checkCudaErrors(cudaEventRecord(stopEvent, params.stream)); - } - } - else { - std::vector raw_inputs; - for (int i = 0; i < params.batch_size; i++) { - raw_inputs.push_back((const unsigned char *)img_data[i].data()); - } - - checkCudaErrors(cudaEventRecord(startEvent, params.stream)); - checkCudaErrors(nvjpegDecodeBatched( - params.nvjpeg_handle, params.nvjpeg_state, raw_inputs.data(), img_len.data(), out.data(), params.stream)); - checkCudaErrors(cudaEventRecord(stopEvent, params.stream)); - } - checkCudaErrors(cudaEventSynchronize(stopEvent)); - checkCudaErrors(cudaEventElapsedTime(&loopTime, startEvent, stopEvent)); - time = static_cast(loopTime); - - return EXIT_SUCCESS; -} - -void write_images(std::vector &iout, - std::vector &widths, - std::vector &heights, - decode_params_t ¶ms, - FileNames &filenames) -{ - for (int i = 0; i < params.batch_size; i++) { - // Get the file name, without extension. - // This will be used to rename the output file. - size_t position = filenames[i].rfind("/"); - std::string sFileName = - (std::string::npos == position) ? filenames[i] : filenames[i].substr(position + 1, filenames[i].size()); - position = sFileName.rfind("."); - sFileName = (std::string::npos == position) ? sFileName : sFileName.substr(0, position); - std::string fname(params.output_dir + "/" + sFileName + ".bmp"); - - int err; - if (params.fmt == NVJPEG_OUTPUT_RGB || params.fmt == NVJPEG_OUTPUT_BGR) { - err = writeBMP(fname.c_str(), - iout[i].channel[0], - iout[i].pitch[0], - iout[i].channel[1], - iout[i].pitch[1], - iout[i].channel[2], - iout[i].pitch[2], - widths[i], - heights[i]); - } - else if (params.fmt == NVJPEG_OUTPUT_RGBI || params.fmt == NVJPEG_OUTPUT_BGRI) { - // Write BMP from interleaved data - err = writeBMPi(fname.c_str(), iout[i].channel[0], iout[i].pitch[0], widths[i], heights[i]); - } - if (err) { - std::cout << "Cannot write output file: " << fname << std::endl; - return; - } - std::cout << "Done writing decoded image to file: " << fname << std::endl; - } -} - -double process_images(FileNames &image_names, decode_params_t ¶ms, double &total) -{ - // vector for storing raw files and file lengths - FileData file_data(params.batch_size); - std::vector file_len(params.batch_size); - FileNames current_names(params.batch_size); - std::vector widths(params.batch_size); - std::vector heights(params.batch_size); - // we wrap over image files to process total_images of files - FileNames::iterator file_iter = image_names.begin(); - - // stream for decoding - checkCudaErrors(cudaStreamCreateWithFlags(¶ms.stream, cudaStreamNonBlocking)); - - int total_processed = 0; - - // output buffers - std::vector iout(params.batch_size); - // output buffer sizes, for convenience - std::vector isz(params.batch_size); - - for (int i = 0; i < iout.size(); i++) { - for (int c = 0; c < NVJPEG_MAX_COMPONENT; c++) { - iout[i].channel[c] = NULL; - iout[i].pitch[c] = 0; - isz[i].pitch[c] = 0; - } - } - - double test_time = 0; - int warmup = 0; - while (total_processed < params.total_images) { - if (read_next_batch(image_names, params.batch_size, file_iter, file_data, file_len, current_names)) - return EXIT_FAILURE; - - if (prepare_buffers(file_data, file_len, widths, heights, iout, isz, current_names, params)) - return EXIT_FAILURE; - - double time; - if (decode_images(file_data, file_len, iout, params, time)) - return EXIT_FAILURE; - if (warmup < params.warmup) { - warmup++; - } - else { - total_processed += params.batch_size; - test_time += time; - } - - if (params.write_decoded) - write_images(iout, widths, heights, params, current_names); - } - total = test_time; - - release_buffers(iout); - - checkCudaErrors(cudaStreamDestroy(params.stream)); - - return EXIT_SUCCESS; -} - -// parse parameters -int findParamIndex(const char **argv, int argc, const char *parm) -{ - int count = 0; - int index = -1; - - for (int i = 0; i < argc; i++) { - if (strncmp(argv[i], parm, 100) == 0) { - index = i; - count++; - } - } - - if (count == 0 || count == 1) { - return index; - } - else { - std::cout << "Error, parameter " << parm << " has been specified more than once, exiting\n" << std::endl; - return -1; - } - - return -1; -} - -int main(int argc, const char *argv[]) -{ - int pidx; - - if ((pidx = findParamIndex(argv, argc, "-h")) != -1 || (pidx = findParamIndex(argv, argc, "--help")) != -1) { - std::cout << "Usage: " << argv[0] - << " -i images_dir [-b batch_size] [-t total_images] [-device= " - "device_id] [-w warmup_iterations] [-o output_dir] " - "[-pipelined] [-batched] [-fmt output_format]\n"; - std::cout << "Parameters: " << std::endl; - std::cout << "\timages_dir\t:\tPath to single image or directory of images" << std::endl; - std::cout << "\tbatch_size\t:\tDecode images from input by batches of " - "specified size" - << std::endl; - std::cout << "\ttotal_images\t:\tDecode this much images, if there are " - "less images \n" - << "\t\t\t\t\tin the input than total images, decoder will loop " - "over the input" - << std::endl; - std::cout << "\tdevice_id\t:\tWhich device to use for decoding" << std::endl; - std::cout << "\twarmup_iterations\t:\tRun this amount of batches first " - "without measuring performance" - << std::endl; - std::cout << "\toutput_dir\t:\tWrite decoded images as BMPs to this directory" << std::endl; - std::cout << "\tpipelined\t:\tUse decoding in phases" << std::endl; - std::cout << "\tbatched\t\t:\tUse batched interface" << std::endl; - std::cout << "\toutput_format\t:\tnvJPEG output format for decoding. One " - "of [rgb, rgbi, bgr, bgri, yuv, y, unchanged]" - << std::endl; - return EXIT_SUCCESS; - } - - decode_params_t params; - - params.input_dir = "./"; - if ((pidx = findParamIndex(argv, argc, "-i")) != -1) { - params.input_dir = argv[pidx + 1]; - } - else { - // Search in default paths for input images. - int found = getInputDir(params.input_dir, argv[0]); - if (!found) { - std::cout << "Please specify input directory with encoded images" << std::endl; - return EXIT_WAIVED; - } - } - - params.batch_size = 1; - if ((pidx = findParamIndex(argv, argc, "-b")) != -1) { - params.batch_size = std::atoi(argv[pidx + 1]); - } - - params.total_images = -1; - if ((pidx = findParamIndex(argv, argc, "-t")) != -1) { - params.total_images = std::atoi(argv[pidx + 1]); - } - - params.dev = 0; - params.dev = findCudaDevice(argc, argv); - - params.warmup = 0; - if ((pidx = findParamIndex(argv, argc, "-w")) != -1) { - params.warmup = std::atoi(argv[pidx + 1]); - } - - params.batched = false; - if ((pidx = findParamIndex(argv, argc, "-batched")) != -1) { - params.batched = true; - } - - params.pipelined = false; - if ((pidx = findParamIndex(argv, argc, "-pipelined")) != -1) { - params.pipelined = true; - } - - params.fmt = NVJPEG_OUTPUT_RGB; - if ((pidx = findParamIndex(argv, argc, "-fmt")) != -1) { - std::string sfmt = argv[pidx + 1]; - if (sfmt == "rgb") - params.fmt = NVJPEG_OUTPUT_RGB; - else if (sfmt == "bgr") - params.fmt = NVJPEG_OUTPUT_BGR; - else if (sfmt == "rgbi") - params.fmt = NVJPEG_OUTPUT_RGBI; - else if (sfmt == "bgri") - params.fmt = NVJPEG_OUTPUT_BGRI; - else if (sfmt == "yuv") - params.fmt = NVJPEG_OUTPUT_YUV; - else if (sfmt == "y") - params.fmt = NVJPEG_OUTPUT_Y; - else if (sfmt == "unchanged") - params.fmt = NVJPEG_OUTPUT_UNCHANGED; - else { - std::cout << "Unknown format: " << sfmt << std::endl; - return EXIT_FAILURE; - } - } - - params.write_decoded = false; - if ((pidx = findParamIndex(argv, argc, "-o")) != -1) { - params.output_dir = argv[pidx + 1]; - if (params.fmt != NVJPEG_OUTPUT_RGB && params.fmt != NVJPEG_OUTPUT_BGR && params.fmt != NVJPEG_OUTPUT_RGBI - && params.fmt != NVJPEG_OUTPUT_BGRI) { - std::cout << "We can write ony BMPs, which require output format be " - "either RGB/BGR or RGBi/BGRi" - << std::endl; - return EXIT_FAILURE; - } - params.write_decoded = true; - } - - cudaDeviceProp props; - checkCudaErrors(cudaGetDeviceProperties(&props, params.dev)); - - printf("Using GPU %d (%s, %d SMs, %d th/SM max, CC %d.%d, ECC %s)\n", - params.dev, - props.name, - props.multiProcessorCount, - props.maxThreadsPerMultiProcessor, - props.major, - props.minor, - props.ECCEnabled ? "on" : "off"); - - nvjpegDevAllocator_t dev_allocator = {&dev_malloc, &dev_free}; - nvjpegPinnedAllocator_t pinned_allocator = {&host_malloc, &host_free}; - int flags = 0; - checkCudaErrors( - nvjpegCreateEx(NVJPEG_BACKEND_DEFAULT, &dev_allocator, &pinned_allocator, flags, ¶ms.nvjpeg_handle)); - - checkCudaErrors(nvjpegJpegStateCreate(params.nvjpeg_handle, ¶ms.nvjpeg_state)); - checkCudaErrors( - nvjpegDecodeBatchedInitialize(params.nvjpeg_handle, params.nvjpeg_state, params.batch_size, 1, params.fmt)); - - if (params.pipelined) { - create_decoupled_api_handles(params); - } - // read source images - FileNames image_names; - readInput(params.input_dir, image_names); - - if (params.total_images == -1) { - params.total_images = image_names.size(); - } - else if (params.total_images % params.batch_size) { - params.total_images = ((params.total_images) / params.batch_size) * params.batch_size; - std::cout << "Changing total_images number to " << params.total_images << " to be multiple of batch_size - " - << params.batch_size << std::endl; - } - - std::cout << "Decoding images in directory: " << params.input_dir << ", total " << params.total_images - << ", batchsize " << params.batch_size << std::endl; - - double total; - if (process_images(image_names, params, total)) - return EXIT_FAILURE; - std::cout << "Total decoding time: " << total << std::endl; - std::cout << "Avg decoding time per image: " << total / params.total_images << std::endl; - std::cout << "Avg images per sec: " << params.total_images / total << std::endl; - std::cout << "Avg decoding time per batch: " - << total / ((params.total_images + params.batch_size - 1) / params.batch_size) << std::endl; - - if (params.pipelined) { - destroy_decoupled_api_handles(params); - } - - checkCudaErrors(nvjpegJpegStateDestroy(params.nvjpeg_state)); - checkCudaErrors(nvjpegDestroy(params.nvjpeg_handle)); - - return EXIT_SUCCESS; -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/extensions.json b/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG_encoder/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/CMakeLists.txt b/cpp/4_CUDA_Libraries/nvJPEG_encoder/CMakeLists.txt deleted file mode 100644 index 47ebb087..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG_encoder/CMakeLists.txt +++ /dev/null @@ -1,56 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(nvJPEG_encoder LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for nvJPEG_encoder -add_executable(nvJPEG_encoder nvJPEG_encoder.cpp) - -target_compile_options(nvJPEG_encoder PRIVATE $<$:--extended-lambda>) - -target_compile_features(nvJPEG_encoder PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(nvJPEG_encoder PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(nvJPEG_encoder PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(nvJPEG_encoder PRIVATE - CUDA::cudart - CUDA::nvjpeg -) - -# Copy data to the output directory -add_custom_command(TARGET nvJPEG_encoder POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CMAKE_CURRENT_SOURCE_DIR}/images - ${CMAKE_CURRENT_BINARY_DIR}/images -) - -add_custom_command(TARGET nvJPEG_encoder POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_directory - ${CMAKE_CURRENT_SOURCE_DIR}/encode_output - ${CMAKE_CURRENT_BINARY_DIR}/encode_output -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/README.md b/cpp/4_CUDA_Libraries/nvJPEG_encoder/README.md deleted file mode 100644 index 15f109de..00000000 --- a/cpp/4_CUDA_Libraries/nvJPEG_encoder/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# nvJPEG_encoder - NVJPEG Encoder - -## Description - -A CUDA Sample that demonstrates single encoding of jpeg images using NVJPEG Library. - -## Key Concepts - -Image Encoding, NVJPEG Library - -## Supported SM Architectures - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaFree, cudaGetErrorString, cudaEventSynchronize, cudaDeviceSynchronize, cudaEventRecord, cudaMalloc, cudaEventElapsedTime, cudaGetDeviceProperties, cudaEventCreate - -## Dependencies needed to build/run -[NVJPEG](../../../README.md#nvjpeg) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img1.jpg b/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img1.jpg deleted file mode 100644 index d89c7bac..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img1.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img2.jpg b/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img2.jpg deleted file mode 100644 index d346f621..00000000 Binary files a/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img2.jpg and /dev/null differ diff --git a/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img3.jpg b/cpp/4_CUDA_Libraries/nvJPEG_encoder/encode_output/img3.jpg deleted 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All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// This sample needs at least CUDA 10.1. It demonstrates usages of the nvJPEG -// library nvJPEG encoder supports single and multiple image encode. - -#include - -#include "helper_nvJPEG.hxx" - - -int dev_malloc(void **p, size_t s) { return (int)cudaMalloc(p, s); } -int dev_free(void *p) { return (int)cudaFree(p); } - -bool is_interleaved(nvjpegOutputFormat_t format) -{ - if (format == NVJPEG_OUTPUT_RGBI || format == NVJPEG_OUTPUT_BGRI) - return true; - else - return false; -} - -struct encode_params_t -{ - std::string input_dir; - std::string output_dir; - std::string format; - std::string subsampling; - int quality; - int huf; - int dev; -}; - -nvjpegEncoderParams_t encode_params; -nvjpegHandle_t nvjpeg_handle; -nvjpegJpegState_t jpeg_state; -nvjpegEncoderState_t encoder_state; - -int decodeEncodeOneImage(std::string sImagePath, - std::string sOutputPath, - double &time, - nvjpegOutputFormat_t output_format, - nvjpegInputFormat_t input_format) -{ - time = 0.; - cudaEvent_t startEvent = NULL, stopEvent = NULL; - float loopTime = 0; - checkCudaErrors(cudaEventCreate(&startEvent, cudaEventBlockingSync)); - checkCudaErrors(cudaEventCreate(&stopEvent, cudaEventBlockingSync)); - - // Get the file name, without extension. - // This will be used to rename the output file. - size_t position = sImagePath.rfind("/"); - std::string sFileName = - (std::string::npos == position) ? sImagePath : sImagePath.substr(position + 1, sImagePath.size()); - position = sFileName.rfind("."); - sFileName = (std::string::npos == position) ? sFileName : sFileName.substr(0, position); - position = sFileName.rfind("/"); - sFileName = (std::string::npos == position) ? sFileName : sFileName.substr(position + 1, sFileName.length()); - position = sFileName.rfind("\\"); - sFileName = (std::string::npos == position) ? sFileName : sFileName.substr(position + 1, sFileName.length()); - - // Read an image from disk. - std::ifstream oInputStream(sImagePath.c_str(), std::ios::in | std::ios::binary | std::ios::ate); - if (!(oInputStream.is_open())) { - std::cerr << "Cannot open image: " << sImagePath << std::endl; - return 1; - } - - // Get the size. - std::streamsize nSize = oInputStream.tellg(); - oInputStream.seekg(0, std::ios::beg); - - // Image buffers. - unsigned char *pBuffer = NULL; - double encoder_time = 0.; - - std::vector vBuffer(nSize); - - if (oInputStream.read(vBuffer.data(), nSize)) { - unsigned char *dpImage = (unsigned char *)vBuffer.data(); - - // Retrieve the componenet and size info. - int nComponent = 0; - nvjpegChromaSubsampling_t subsampling; - int widths[NVJPEG_MAX_COMPONENT]; - int heights[NVJPEG_MAX_COMPONENT]; - if (NVJPEG_STATUS_SUCCESS - != nvjpegGetImageInfo(nvjpeg_handle, dpImage, nSize, &nComponent, &subsampling, widths, heights)) { - std::cerr << "Error decoding JPEG header: " << sImagePath << std::endl; - return 1; - } - - // image information - std::cout << "Image is " << nComponent << " channels." << std::endl; - for (int i = 0; i < nComponent; i++) { - std::cout << "Channel #" << i << " size: " << widths[i] << " x " << heights[i] << std::endl; - } - - switch (subsampling) { - case NVJPEG_CSS_444: - std::cout << "YUV 4:4:4 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_440: - std::cout << "YUV 4:4:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_422: - std::cout << "YUV 4:2:2 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_420: - std::cout << "YUV 4:2:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_411: - std::cout << "YUV 4:1:1 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_410: - std::cout << "YUV 4:1:0 chroma subsampling" << std::endl; - break; - case NVJPEG_CSS_GRAY: - std::cout << "Grayscale JPEG " << std::endl; - break; - case NVJPEG_CSS_UNKNOWN: - std::cout << "Unknown chroma subsampling" << std::endl; - return 1; - } - - { - - cudaError_t eCopy = cudaMalloc(&pBuffer, widths[0] * heights[0] * NVJPEG_MAX_COMPONENT); - if (cudaSuccess != eCopy) { - std::cerr << "cudaMalloc failed for component Y: " << cudaGetErrorString(eCopy) << std::endl; - return 1; - } - - nvjpegImage_t imgdesc = {{pBuffer, - pBuffer + widths[0] * heights[0], - pBuffer + widths[0] * heights[0] * 2, - pBuffer + widths[0] * heights[0] * 3}, - {(unsigned int)(is_interleaved(output_format) ? widths[0] * 3 : widths[0]), - (unsigned int)widths[0], - (unsigned int)widths[0], - (unsigned int)widths[0]}}; - - int nReturnCode = 0; - - cudaDeviceSynchronize(); - - nReturnCode = nvjpegDecode(nvjpeg_handle, jpeg_state, dpImage, nSize, output_format, &imgdesc, NULL); - - // alternatively decode by stages - /*int nReturnCode = nvjpegDecodeCPU(nvjpeg_handle, dpImage, nSize, output_format, &imgdesc, NULL); - nReturnCode = nvjpegDecodeMixed(nvjpeg_handle, NULL); - nReturnCode = nvjpegDecodeGPU(nvjpeg_handle, NULL);*/ - cudaDeviceSynchronize(); - - if (nReturnCode != 0) { - std::cerr << "Error in nvjpegDecode." << std::endl; - return 1; - } - - checkCudaErrors(cudaEventRecord(startEvent, NULL)); - /////////////////////// encode //////////////////// - if (NVJPEG_OUTPUT_YUV == output_format) { - checkCudaErrors(nvjpegEncodeYUV( - nvjpeg_handle, encoder_state, encode_params, &imgdesc, subsampling, widths[0], heights[0], NULL)); - } - else { - checkCudaErrors(nvjpegEncodeImage( - nvjpeg_handle, encoder_state, encode_params, &imgdesc, input_format, widths[0], heights[0], NULL)); - } - - std::vector obuffer; - size_t length; - checkCudaErrors(nvjpegEncodeRetrieveBitstream(nvjpeg_handle, encoder_state, NULL, &length, NULL)); - obuffer.resize(length); - checkCudaErrors(nvjpegEncodeRetrieveBitstream(nvjpeg_handle, encoder_state, obuffer.data(), &length, NULL)); - - checkCudaErrors(cudaEventRecord(stopEvent, NULL)); - checkCudaErrors(cudaEventSynchronize(stopEvent)); - checkCudaErrors(cudaEventElapsedTime(&loopTime, startEvent, stopEvent)); - encoder_time = static_cast(loopTime); - - std::string output_filename = sOutputPath + "/" + sFileName + ".jpg"; - char directory[120]; - char mkdir_cmd[256]; - std::string folder = sOutputPath; - output_filename = folder + "/" + sFileName + ".jpg"; -#if !defined(_WIN32) - sprintf(directory, "%s", folder.c_str()); - sprintf(mkdir_cmd, "mkdir -p %s 2> /dev/null", directory); -#else - sprintf(directory, "%s", folder.c_str()); - sprintf(mkdir_cmd, "mkdir %s 2> nul", directory); -#endif - - int ret = system(mkdir_cmd); - - std::cout << "Writing JPEG file: " << output_filename << std::endl; - std::ofstream outputFile(output_filename.c_str(), std::ios::out | std::ios::binary); - outputFile.write(reinterpret_cast(obuffer.data()), static_cast(length)); - - // Free memory - checkCudaErrors(cudaFree(pBuffer)); - } - } - - time = encoder_time; - - return 0; -} - -int processArgs(encode_params_t param) -{ - std::string sInputPath(param.input_dir); - std::string sOutputPath(param.output_dir); - std::string sFormat(param.format); - std::string sSubsampling(param.subsampling); - nvjpegOutputFormat_t oformat = NVJPEG_OUTPUT_RGB; - nvjpegInputFormat_t iformat = NVJPEG_INPUT_RGB; - - int error_code = 1; - - if (sFormat == "yuv") { - oformat = NVJPEG_OUTPUT_YUV; - } - else if (sFormat == "rgb") { - oformat = NVJPEG_OUTPUT_RGB; - iformat = NVJPEG_INPUT_RGB; - } - else if (sFormat == "bgr") { - oformat = NVJPEG_OUTPUT_BGR; - iformat = NVJPEG_INPUT_BGR; - } - else if (sFormat == "rgbi") { - oformat = NVJPEG_OUTPUT_RGBI; - iformat = NVJPEG_INPUT_RGBI; - } - else if (sFormat == "bgri") { - oformat = NVJPEG_OUTPUT_BGRI; - iformat = NVJPEG_INPUT_BGRI; - } - else { - std::cerr << "Unknown or unsupported output format: " << sFormat << std::endl; - return error_code; - } - - if (sSubsampling == "444") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_444, NULL)); - } - else if (sSubsampling == "422") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_422, NULL)); - } - else if (sSubsampling == "420") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_420, NULL)); - } - else if (sSubsampling == "440") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_440, NULL)); - } - else if (sSubsampling == "411") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_411, NULL)); - } - else if (sSubsampling == "410") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_410, NULL)); - } - else if (sSubsampling == "400") { - checkCudaErrors(nvjpegEncoderParamsSetSamplingFactors(encode_params, NVJPEG_CSS_GRAY, NULL)); - } - else { - std::cerr << "Unknown or unsupported subsampling: " << sSubsampling << std::endl; - return error_code; - } - /*if( stat(sOutputPath.c_str(), &s) == 0 ) - { - if( !(s.st_mode & S_IFDIR) ) - { - std::cout << "Output path already exist as non-directory: " << sOutputPath << std::endl; - return error_code; - } - } - else - { - if (mkdir(sOutputPath.c_str(), 0775)) - { - std::cout << "Cannot create output directory: " << sOutputPath << std::endl; - return error_code; - } - }*/ - - std::vector inputFiles; - if (readInput(sInputPath, inputFiles)) { - return error_code; - } - - double total_time = 0., encoder_time = 0.; - int total_images = 0; - - for (unsigned int i = 0; i < inputFiles.size(); i++) { - std::string &sFileName = inputFiles[i]; - std::cout << "Processing file: " << sFileName << std::endl; - int image_error_code = decodeEncodeOneImage(sFileName, sOutputPath, encoder_time, oformat, iformat); - if (image_error_code) { - std::cerr << "Error processing file: " << sFileName << std::endl; - // return image_error_code; - } - else { - total_images++; - total_time += encoder_time; - } - } - std::cout << "Total images processed: " << total_images << std::endl; - std::cout << "Total time spent on encoding: " << total_time << std::endl; - std::cout << "Avg time/image: " << total_time / total_images << std::endl; - - return 0; -} - -// parse parameters -int findParamIndex(const char **argv, int argc, const char *parm) -{ - int count = 0; - int index = -1; - - for (int i = 0; i < argc; i++) { - if (strncmp(argv[i], parm, 100) == 0) { - index = i; - count++; - } - } - - if (count == 0 || count == 1) { - return index; - } - else { - std::cout << "Error, parameter " << parm << " has been specified more than once, exiting\n" << std::endl; - return -1; - } - - return -1; -} - - -int main(int argc, const char *argv[]) -{ - int pidx; - - if ((pidx = findParamIndex(argv, argc, "-h")) != -1 || (pidx = findParamIndex(argv, argc, "--help")) != -1) { - std::cout << "Usage: " << argv[0] - << " -i images_dir [-o output_dir] [-device=device_id]" - "[-q quality][-s 420/444] [-fmt output_format] [-huf 0]\n"; - std::cout << "Parameters: " << std::endl; - std::cout << "\timages_dir\t:\tPath to single image or directory of images" << std::endl; - std::cout << "\toutput_dir\t:\tWrite encoded images as jpeg to this directory" << std::endl; - std::cout << "\tdevice_id\t:\tWhich device to use for encoding" << std::endl; - std::cout << "\tQuality\t:\tUse image quality [default 70]" << std::endl; - std::cout << "\tsubsampling\t:\tUse Subsampling [420, 444]" << std::endl; - std::cout << "\toutput_format\t:\tnvJPEG output format for encoding. One " - "of [rgb, rgbi, bgr, bgri, yuv, y, unchanged]" - << std::endl; - std::cout << "\tHuffman Optimization\t:\tUse Huffman optimization [default 0]" << std::endl; - return EXIT_SUCCESS; - } - - encode_params_t params; - - params.input_dir = "./"; - if ((pidx = findParamIndex(argv, argc, "-i")) != -1) { - params.input_dir = argv[pidx + 1]; - } - else { - // Search in default paths for input images. - int found = getInputDir(params.input_dir, argv[0]); - if (!found) { - std::cout << "Please specify input directory with encoded images" << std::endl; - return EXIT_WAIVED; - } - } - if ((pidx = findParamIndex(argv, argc, "-o")) != -1) { - params.output_dir = argv[pidx + 1]; - } - else { - // by-default write the folder named "output" in cwd - params.output_dir = "encode_output"; - } - params.dev = 0; - params.dev = findCudaDevice(argc, argv); - - params.quality = 70; - if ((pidx = findParamIndex(argv, argc, "-q")) != -1) { - params.quality = std::atoi(argv[pidx + 1]); - } - - if ((pidx = findParamIndex(argv, argc, "-s")) != -1) { - params.subsampling = argv[pidx + 1]; - } - else { - // by-default use subsampling as 420 - params.subsampling = "420"; - } - if ((pidx = findParamIndex(argv, argc, "-fmt")) != -1) { - params.format = argv[pidx + 1]; - } - else { - // by-default use output format yuv - params.format = "yuv"; - } - - params.huf = 0; - if ((pidx = findParamIndex(argv, argc, "-huf")) != -1) { - params.huf = std::atoi(argv[pidx + 1]); - } - - cudaDeviceProp props; - checkCudaErrors(cudaGetDeviceProperties(&props, params.dev)); - - printf("Using GPU %d (%s, %d SMs, %d th/SM max, CC %d.%d, ECC %s)\n", - params.dev, - props.name, - props.multiProcessorCount, - props.maxThreadsPerMultiProcessor, - props.major, - props.minor, - props.ECCEnabled ? "on" : "off"); - - nvjpegDevAllocator_t dev_allocator = {&dev_malloc, &dev_free}; - checkCudaErrors(nvjpegCreate(NVJPEG_BACKEND_DEFAULT, &dev_allocator, &nvjpeg_handle)); - checkCudaErrors(nvjpegJpegStateCreate(nvjpeg_handle, &jpeg_state)); - checkCudaErrors(nvjpegEncoderStateCreate(nvjpeg_handle, &encoder_state, NULL)); - checkCudaErrors(nvjpegEncoderParamsCreate(nvjpeg_handle, &encode_params, NULL)); - - // sample input parameters - checkCudaErrors(nvjpegEncoderParamsSetQuality(encode_params, params.quality, NULL)); - checkCudaErrors(nvjpegEncoderParamsSetOptimizedHuffman(encode_params, params.huf, NULL)); - - pidx = processArgs(params); - - checkCudaErrors(nvjpegEncoderParamsDestroy(encode_params)); - checkCudaErrors(nvjpegEncoderStateDestroy(encoder_state)); - checkCudaErrors(nvjpegJpegStateDestroy(jpeg_state)); - checkCudaErrors(nvjpegDestroy(nvjpeg_handle)); - - return pidx; -} diff --git a/cpp/4_CUDA_Libraries/oceanFFT/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/oceanFFT/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/oceanFFT/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/oceanFFT/.vscode/extensions.json b/cpp/4_CUDA_Libraries/oceanFFT/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/oceanFFT/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/oceanFFT/CMakeLists.txt b/cpp/4_CUDA_Libraries/oceanFFT/CMakeLists.txt index 99aa0e07..f2e1418b 100644 --- a/cpp/4_CUDA_Libraries/oceanFFT/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/oceanFFT/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(oceanFFT LANGUAGES CUDA CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -30,54 +26,41 @@ if(WIN32) endif() endif() - find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for oceanFFT add_executable(oceanFFT oceanFFT.cpp oceanFFT_kernel.cu) - -target_compile_options(oceanFFT PRIVATE $<$:--extended-lambda>) - -target_compile_features(oceanFFT PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(oceanFFT PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(oceanFFT PRIVATE $<$:--extended-lambda>) + target_compile_features(oceanFFT PRIVATE cxx_std_17 cuda_std_17) target_include_directories(oceanFFT PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(oceanFFT ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} CUDA::cufft ) - # Copy data files to the output directory add_custom_command(TARGET oceanFFT POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(oceanFFT ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET oceanFFT POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET oceanFFT POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -85,15 +68,11 @@ target_compile_features(oceanFFT PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'oceanFFT'") endif() else() message(STATUS "OpenGL not found - will not build sample 'oceanFFT'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/randomFog/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/randomFog/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/randomFog/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/randomFog/.vscode/extensions.json b/cpp/4_CUDA_Libraries/randomFog/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/randomFog/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/randomFog/CMakeLists.txt b/cpp/4_CUDA_Libraries/randomFog/CMakeLists.txt index 635a2960..fc2d67d1 100644 --- a/cpp/4_CUDA_Libraries/randomFog/CMakeLists.txt +++ b/cpp/4_CUDA_Libraries/randomFog/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(randomFog LANGUAGES CXX) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) # Check for Debian13 or later @@ -43,24 +39,16 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for randomFog add_executable(randomFog randomFog.cpp rng.cpp) - target_compile_options(randomFog PRIVATE $<$:--extended-lambda>) - target_compile_features(randomFog PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(randomFog PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(randomFog PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(randomFog ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} @@ -76,27 +64,23 @@ if(${OpenGL_FOUND}) Xext ) endif() - # Copy data files to the output directory add_custom_command(TARGET randomFog POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(randomFog ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET randomFog POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET randomFog POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -104,14 +88,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'randomFog'") endif() else() message(STATUS "OpenGL not found - will not build sample 'randomFog'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUBLAS/CMakeLists.txt deleted file mode 100644 index cc7f7149..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUBLAS LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUBLAS -add_executable(simpleCUBLAS simpleCUBLAS.cpp) - -target_compile_options(simpleCUBLAS PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUBLAS PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUBLAS PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(simpleCUBLAS PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(simpleCUBLAS PRIVATE - CUDA::cudart - CUDA::cublas -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS/README.md b/cpp/4_CUDA_Libraries/simpleCUBLAS/README.md deleted file mode 100644 index b381aac3..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUBLAS - Simple CUBLAS - -## Description - -Example of using CUBLAS API interface to perform GEMM operations. - -## Key Concepts - -Image Processing, CUBLAS Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaFree - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS/simpleCUBLAS.cpp b/cpp/4_CUDA_Libraries/simpleCUBLAS/simpleCUBLAS.cpp deleted file mode 100644 index 6197a4ee..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS/simpleCUBLAS.cpp +++ /dev/null @@ -1,253 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* This example demonstrates how to use the CUBLAS library - * by scaling an array of floating-point values on the device - * and comparing the result to the same operation performed - * on the host. - */ - -/* Includes, system */ -#include -#include -#include - -/* Includes, cuda */ -#include -#include -#include - -/* Matrix size */ -#define N (275) - -/* Host implementation of a simple version of sgemm */ -static void simple_sgemm(int n, float alpha, const float *A, const float *B, float beta, float *C) -{ - int i; - int j; - int k; - - for (i = 0; i < n; ++i) { - for (j = 0; j < n; ++j) { - float prod = 0; - - for (k = 0; k < n; ++k) { - prod += A[k * n + i] * B[j * n + k]; - } - - C[j * n + i] = alpha * prod + beta * C[j * n + i]; - } - } -} - -/* Main */ -int main(int argc, char **argv) -{ - cublasStatus_t status; - float *h_A; - float *h_B; - float *h_C; - float *h_C_ref; - float *d_A = 0; - float *d_B = 0; - float *d_C = 0; - float alpha = 1.0f; - float beta = 0.0f; - int n2 = N * N; - int i; - float error_norm; - float ref_norm; - float diff; - cublasHandle_t handle; - - int dev = findCudaDevice(argc, (const char **)argv); - - if (dev == -1) { - return EXIT_FAILURE; - } - - /* Initialize CUBLAS */ - printf("simpleCUBLAS test running..\n"); - - status = cublasCreate(&handle); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! CUBLAS initialization error\n"); - return EXIT_FAILURE; - } - - /* Allocate host memory for the matrices */ - h_A = reinterpret_cast(malloc(n2 * sizeof(h_A[0]))); - - if (h_A == 0) { - fprintf(stderr, "!!!! host memory allocation error (A)\n"); - return EXIT_FAILURE; - } - - h_B = reinterpret_cast(malloc(n2 * sizeof(h_B[0]))); - - if (h_B == 0) { - fprintf(stderr, "!!!! host memory allocation error (B)\n"); - return EXIT_FAILURE; - } - - h_C = reinterpret_cast(malloc(n2 * sizeof(h_C[0]))); - - if (h_C == 0) { - fprintf(stderr, "!!!! host memory allocation error (C)\n"); - return EXIT_FAILURE; - } - - /* Fill the matrices with test data */ - for (i = 0; i < n2; i++) { - h_A[i] = rand() / static_cast(RAND_MAX); - h_B[i] = rand() / static_cast(RAND_MAX); - h_C[i] = rand() / static_cast(RAND_MAX); - } - - /* Allocate device memory for the matrices */ - if (cudaMalloc(reinterpret_cast(&d_A), n2 * sizeof(d_A[0])) != cudaSuccess) { - fprintf(stderr, "!!!! device memory allocation error (allocate A)\n"); - return EXIT_FAILURE; - } - - if (cudaMalloc(reinterpret_cast(&d_B), n2 * sizeof(d_B[0])) != cudaSuccess) { - fprintf(stderr, "!!!! device memory allocation error (allocate B)\n"); - return EXIT_FAILURE; - } - - if (cudaMalloc(reinterpret_cast(&d_C), n2 * sizeof(d_C[0])) != cudaSuccess) { - fprintf(stderr, "!!!! device memory allocation error (allocate C)\n"); - return EXIT_FAILURE; - } - - /* Initialize the device matrices with the host matrices */ - status = cublasSetVector(n2, sizeof(h_A[0]), h_A, 1, d_A, 1); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! device access error (write A)\n"); - return EXIT_FAILURE; - } - - status = cublasSetVector(n2, sizeof(h_B[0]), h_B, 1, d_B, 1); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! device access error (write B)\n"); - return EXIT_FAILURE; - } - - status = cublasSetVector(n2, sizeof(h_C[0]), h_C, 1, d_C, 1); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! device access error (write C)\n"); - return EXIT_FAILURE; - } - - /* Performs operation using plain C code */ - simple_sgemm(N, alpha, h_A, h_B, beta, h_C); - h_C_ref = h_C; - - /* Performs operation using cublas */ - status = cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, N, N, N, &alpha, d_A, N, d_B, N, &beta, d_C, N); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! kernel execution error.\n"); - return EXIT_FAILURE; - } - - /* Allocate host memory for reading back the result from device memory */ - h_C = reinterpret_cast(malloc(n2 * sizeof(h_C[0]))); - - if (h_C == 0) { - fprintf(stderr, "!!!! host memory allocation error (C)\n"); - return EXIT_FAILURE; - } - - /* Read the result back */ - status = cublasGetVector(n2, sizeof(h_C[0]), d_C, 1, h_C, 1); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! device access error (read C)\n"); - return EXIT_FAILURE; - } - - /* Check result against reference */ - error_norm = 0; - ref_norm = 0; - - for (i = 0; i < n2; ++i) { - diff = h_C_ref[i] - h_C[i]; - error_norm += diff * diff; - ref_norm += h_C_ref[i] * h_C_ref[i]; - } - - error_norm = static_cast(sqrt(static_cast(error_norm))); - ref_norm = static_cast(sqrt(static_cast(ref_norm))); - - if (fabs(ref_norm) < 1e-7) { - fprintf(stderr, "!!!! reference norm is 0\n"); - return EXIT_FAILURE; - } - - /* Memory clean up */ - free(h_A); - free(h_B); - free(h_C); - free(h_C_ref); - - if (cudaFree(d_A) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (A)\n"); - return EXIT_FAILURE; - } - - if (cudaFree(d_B) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (B)\n"); - return EXIT_FAILURE; - } - - if (cudaFree(d_C) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (C)\n"); - return EXIT_FAILURE; - } - - /* Shutdown */ - status = cublasDestroy(handle); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! shutdown error (A)\n"); - return EXIT_FAILURE; - } - - if (error_norm / ref_norm < 1e-6f) { - printf("simpleCUBLAS test passed.\n"); - exit(EXIT_SUCCESS); - } - else { - printf("simpleCUBLAS test failed.\n"); - exit(EXIT_FAILURE); - } -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLASXT/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLASXT/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUBLASXT/CMakeLists.txt deleted file mode 100644 index 72d33680..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLASXT/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUBLASXT LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUBLASXT -add_executable(simpleCUBLASXT simpleCUBLASXT.cpp) - -target_compile_options(simpleCUBLASXT PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUBLASXT PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUBLASXT PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(simpleCUBLASXT PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(simpleCUBLASXT PRIVATE - CUDA::cudart - CUDA::cublas -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUBLASXT/README.md b/cpp/4_CUDA_Libraries/simpleCUBLASXT/README.md deleted file mode 100644 index 64a884b2..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLASXT/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUBLASXT - Simple CUBLAS XT - -## Description - -Example of using CUBLAS-XT library which performs GEMM operations over Multiple GPUs. - -## Key Concepts - -CUBLAS-XT Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaGetDeviceProperties, cudaGetDeviceCount, cudaFree - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUBLASXT/simpleCUBLASXT.cpp b/cpp/4_CUDA_Libraries/simpleCUBLASXT/simpleCUBLASXT.cpp deleted file mode 100644 index b100f1ae..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLASXT/simpleCUBLASXT.cpp +++ /dev/null @@ -1,308 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* This example demonstrates how to use the CUBLAS library - * by scaling an array of floating-point values on the device - * and comparing the result to the same operation performed - * on the host. - */ - -/* Includes, system */ -#include -#include -#include - -/* Includes, cuda */ -#include -#include -#include - -/* Matrix size */ -// #define N (275) -#define N (1024) -// Restricting the max used GPUs as input matrix is not so large -#define MAX_NUM_OF_GPUS 2 - -/* Host implementation of a simple version of sgemm */ -static void simple_sgemm(int n, float alpha, const float *A, const float *B, float beta, float *C) -{ - int i; - int j; - int k; - - for (i = 0; i < n; ++i) { - for (j = 0; j < n; ++j) { - float prod = 0; - - for (k = 0; k < n; ++k) { - prod += A[k * n + i] * B[j * n + k]; - } - - C[j * n + i] = alpha * prod + beta * C[j * n + i]; - } - } -} - -void findMultipleBestGPUs(int &num_of_devices, int *device_ids) -{ - // Find the best CUDA capable GPU device - int current_device = 0; - - int device_count; - checkCudaErrors(cudaGetDeviceCount(&device_count)); - typedef struct gpu_perf_t - { - uint64_t compute_perf; - int device_id; - } gpu_perf; - - gpu_perf *gpu_stats = (gpu_perf *)malloc(sizeof(gpu_perf) * device_count); - - cudaDeviceProp deviceProp; - int devices_prohibited = 0; - int computeMode; - int clockRate; - - while (current_device < device_count) { - cudaGetDeviceProperties(&deviceProp, current_device); - checkCudaErrors(cudaDeviceGetAttribute(&computeMode, cudaDevAttrComputeMode, current_device)); - checkCudaErrors(cudaDeviceGetAttribute(&clockRate, cudaDevAttrClockRate, current_device)); - // If this GPU is not running on Compute Mode prohibited, - // then we can add it to the list - int sm_per_multiproc; - if (computeMode != cudaComputeModeProhibited) { - if (deviceProp.major == 9999 && deviceProp.minor == 9999) { - sm_per_multiproc = 1; - } - else { - sm_per_multiproc = _ConvertSMVer2Cores(deviceProp.major, deviceProp.minor); - } - - gpu_stats[current_device].compute_perf = - (uint64_t)deviceProp.multiProcessorCount * sm_per_multiproc * clockRate; - gpu_stats[current_device].device_id = current_device; - } - else { - devices_prohibited++; - } - - ++current_device; - } - if (devices_prohibited == device_count) { - fprintf(stderr, - "gpuGetMaxGflopsDeviceId() CUDA error:" - " all devices have compute mode prohibited.\n"); - exit(EXIT_FAILURE); - } - else { - gpu_perf temp_elem; - // Sort the GPUs by highest compute perf. - for (int i = 0; i < current_device - 1; i++) { - for (int j = 0; j < current_device - i - 1; j++) { - if (gpu_stats[j].compute_perf < gpu_stats[j + 1].compute_perf) { - temp_elem = gpu_stats[j]; - gpu_stats[j] = gpu_stats[j + 1]; - gpu_stats[j + 1] = temp_elem; - } - } - } - - for (int i = 0; i < num_of_devices; i++) { - device_ids[i] = gpu_stats[i].device_id; - } - } - free(gpu_stats); -} - -/* Main */ -int main(int argc, char **argv) -{ - cublasStatus_t status; - float *h_A; - float *h_B; - float *h_C; - float *h_C_ref; - float *d_A = 0; - float *d_B = 0; - float *d_C = 0; - float alpha = 1.0f; - float beta = 0.0f; - int n2 = N * N; - int i; - float error_norm; - float ref_norm; - float diff; - cublasXtHandle_t handle; - int *devices = NULL; - - int num_of_devices = 0; - - checkCudaErrors(cudaGetDeviceCount(&num_of_devices)); - - if (num_of_devices > MAX_NUM_OF_GPUS) { - num_of_devices = MAX_NUM_OF_GPUS; - } - devices = (int *)malloc(sizeof(int) * num_of_devices); - - findMultipleBestGPUs(num_of_devices, devices); - cudaDeviceProp deviceProp; - printf("Using %d GPUs\n", num_of_devices); - for (i = 0; i < num_of_devices; i++) { - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devices[i])); - printf("GPU ID = %d, Name = %s \n", devices[i], deviceProp.name); - } - - /* Initialize CUBLAS */ - printf("simpleCUBLASXT test running..\n"); - - status = cublasXtCreate(&handle); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! CUBLASXT initialization error\n"); - return EXIT_FAILURE; - } - - /* Select devices for use in CUBLASXT math functions */ - status = cublasXtDeviceSelect(handle, num_of_devices, devices); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! CUBLASXT device selection error\n"); - return EXIT_FAILURE; - } - - /* Optional: Set a block size for CUBLASXT math functions */ - status = cublasXtSetBlockDim(handle, 64); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! CUBLASXT set block dimension error\n"); - return EXIT_FAILURE; - } - - /* Allocate host memory for the matrices */ - h_A = (float *)malloc(n2 * sizeof(h_A[0])); - - if (h_A == 0) { - fprintf(stderr, "!!!! host memory allocation error (A)\n"); - return EXIT_FAILURE; - } - - h_B = (float *)malloc(n2 * sizeof(h_B[0])); - - if (h_B == 0) { - fprintf(stderr, "!!!! host memory allocation error (B)\n"); - return EXIT_FAILURE; - } - - h_C_ref = (float *)malloc(n2 * sizeof(h_C[0])); - - if (h_C_ref == 0) { - fprintf(stderr, "!!!! host memory allocation error (C_ref)\n"); - return EXIT_FAILURE; - } - - h_C = (float *)malloc(n2 * sizeof(h_C[0])); - - if (h_C == 0) { - fprintf(stderr, "!!!! host memory allocation error (C)\n"); - return EXIT_FAILURE; - } - - /* Fill the matrices with test data */ - for (i = 0; i < n2; i++) { - h_A[i] = rand() / (float)RAND_MAX; - h_B[i] = rand() / (float)RAND_MAX; - h_C[i] = rand() / (float)RAND_MAX; - h_C_ref[i] = h_C[i]; - } - - /* Performs operation using plain C code */ - simple_sgemm(N, alpha, h_A, h_B, beta, h_C_ref); - - /* Performs operation using cublas */ - status = cublasXtSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, N, N, N, &alpha, h_A, N, h_B, N, &beta, h_C, N); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! kernel execution error.\n"); - return EXIT_FAILURE; - } - - /* Check result against reference */ - error_norm = 0; - ref_norm = 0; - - for (i = 0; i < n2; ++i) { - diff = h_C_ref[i] - h_C[i]; - error_norm += diff * diff; - ref_norm += h_C_ref[i] * h_C_ref[i]; - } - - error_norm = (float)sqrt((double)error_norm); - ref_norm = (float)sqrt((double)ref_norm); - - if (fabs(ref_norm) < 1e-7) { - fprintf(stderr, "!!!! reference norm is 0\n"); - return EXIT_FAILURE; - } - - /* Memory clean up */ - free(h_A); - free(h_B); - free(h_C); - free(h_C_ref); - - if (cudaFree(d_A) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (A)\n"); - return EXIT_FAILURE; - } - - if (cudaFree(d_B) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (B)\n"); - return EXIT_FAILURE; - } - - if (cudaFree(d_C) != cudaSuccess) { - fprintf(stderr, "!!!! memory free error (C)\n"); - return EXIT_FAILURE; - } - - /* Shutdown */ - status = cublasXtDestroy(handle); - - if (status != CUBLAS_STATUS_SUCCESS) { - fprintf(stderr, "!!!! shutdown error (A)\n"); - return EXIT_FAILURE; - } - - if (error_norm / ref_norm < 1e-6f) { - printf("simpleCUBLASXT test passed.\n"); - exit(EXIT_SUCCESS); - } - else { - printf("simpleCUBLASXT test failed.\n"); - exit(EXIT_FAILURE); - } -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/CMakeLists.txt deleted file mode 100644 index 01f8c8a0..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUBLAS_LU LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUBLAS_LU -add_executable(simpleCUBLAS_LU simpleCUBLAS_LU.cpp) - -target_compile_options(simpleCUBLAS_LU PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUBLAS_LU PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUBLAS_LU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(simpleCUBLAS_LU PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) - -target_link_libraries(simpleCUBLAS_LU PRIVATE - CUDA::cudart - CUDA::cublas -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/README.md b/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/README.md deleted file mode 100644 index 1b6e2bbc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUBLAS_LU - Simple CUBLAS LU - -## Description - -CUDA sample demonstrating cuBLAS API cublasDgetrfBatched() for lower-upper (LU) decomposition of a matrix. - -## Key Concepts - -CUBLAS Library, LU decomposition - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaGetErrorEnum, cudaMalloc, cudaMemcpy, cudaFree - -## Dependencies needed to build/run -[CUBLAS](../../../README.md#cublas) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/simpleCUBLAS_LU.cpp b/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/simpleCUBLAS_LU.cpp deleted file mode 100644 index 201301c5..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUBLAS_LU/simpleCUBLAS_LU.cpp +++ /dev/null @@ -1,418 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This example demonstrates how to use the cuBLAS library API - * for lower-upper (LU) decomposition of a matrix. LU decomposition - * factors a matrix as the product of upper triangular matrix and - * lower trianglular matrix. - * - * https://en.wikipedia.org/wiki/LU_decomposition - * - * This sample uses 10000 matrices of size 4x4 and performs - * LU decomposition of them using batched decomposition API - * of cuBLAS library. To test the correctness of upper and lower - * matrices generated, they are multiplied and compared with the - * original input matrix. - * - */ - -#include -#include - -// cuda libraries and helpers -#include -#include -#include - -// configurable parameters -// dimension of matrix -#define N 4 -#define BATCH_SIZE 10000 - -// use double precision data type -#define DOUBLE_PRECISION /* comment this to use single precision */ -#ifdef DOUBLE_PRECISION -#define DATA_TYPE double -#define MAX_ERROR 1e-15 -#else -#define DATA_TYPE float -#define MAX_ERROR 1e-6 -#endif /* DOUBLE_PRCISION */ - -// use pivot vector while decomposing -#define PIVOT /* comment this to disable pivot use */ - -// helper functions - -// wrapper around cublasgetrfBatched() -cublasStatus_t -cublasXgetrfBatched(cublasHandle_t handle, int n, DATA_TYPE *const A[], int lda, int *P, int *info, int batchSize) -{ -#ifdef DOUBLE_PRECISION - return cublasDgetrfBatched(handle, n, A, lda, P, info, batchSize); -#else - return cublasSgetrfBatched(handle, n, A, lda, P, info, batchSize); -#endif -} - -// wrapper around malloc -// clears the allocated memory to 0 -// terminates the program if malloc fails -void *xmalloc(size_t size) -{ - void *ptr = malloc(size); - if (ptr == NULL) { - printf("> ERROR: malloc for size %zu failed..\n", size); - exit(EXIT_FAILURE); - } - memset(ptr, 0, size); - return ptr; -} - -// initalize identity matrix -void initIdentityMatrix(DATA_TYPE *mat) -{ - // clear the matrix - memset(mat, 0, N * N * sizeof(DATA_TYPE)); - - // set all diagonals to 1 - for (int i = 0; i < N; i++) { - mat[(i * N) + i] = 1.0; - } -} - -// initialize matrix with all elements as 0 -void initZeroMatrix(DATA_TYPE *mat) { memset(mat, 0, N * N * sizeof(DATA_TYPE)); } - -// fill random value in column-major matrix -void initRandomMatrix(DATA_TYPE *mat) -{ - for (int i = 0; i < N; i++) { - for (int j = 0; j < N; j++) { - mat[(j * N) + i] = (DATA_TYPE)1.0 + ((DATA_TYPE)rand() / (DATA_TYPE)RAND_MAX); - } - } - - // diagonal dominant matrix to insure it is invertible matrix - for (int i = 0; i < N; i++) { - mat[(i * N) + i] += (DATA_TYPE)N; - } -} - -// print column-major matrix -void printMatrix(DATA_TYPE *mat) -{ - for (int i = 0; i < N; i++) { - for (int j = 0; j < N; j++) { - printf("%20.16f ", mat[(j * N) + i]); - } - printf("\n"); - } - printf("\n"); -} - -// matrix mulitplication -void matrixMultiply(DATA_TYPE *res, DATA_TYPE *mat1, DATA_TYPE *mat2) -{ - initZeroMatrix(res); - - for (int i = 0; i < N; i++) { - for (int j = 0; j < N; j++) { - for (int k = 0; k < N; k++) { - res[(j * N) + i] += mat1[(k * N) + i] * mat2[(j * N) + k]; - } - } - } -} - -// check matrix equality -bool checkRelativeError(DATA_TYPE *mat1, DATA_TYPE *mat2, DATA_TYPE maxError) -{ - DATA_TYPE err = (DATA_TYPE)0.0; - DATA_TYPE refNorm = (DATA_TYPE)0.0; - DATA_TYPE relError = (DATA_TYPE)0.0; - DATA_TYPE relMaxError = (DATA_TYPE)0.0; - - for (int i = 0; i < N * N; i++) { - refNorm = abs(mat1[i]); - err = abs(mat1[i] - mat2[i]); - - if (refNorm != 0.0 && err > 0.0) { - relError = err / refNorm; - relMaxError = MAX(relMaxError, relError); - } - - if (relMaxError > maxError) - return false; - } - return true; -} - -// decode lower and upper matrix from single matrix -// returned by getrfBatched() -void getLUdecoded(DATA_TYPE *mat, DATA_TYPE *L, DATA_TYPE *U) -{ - // init L as identity matrix - initIdentityMatrix(L); - - // copy lower triangular values from mat to L (skip diagonal) - for (int i = 0; i < N; i++) { - for (int j = 0; j < i; j++) { - L[(j * N) + i] = mat[(j * N) + i]; - } - } - - // init U as all zero - initZeroMatrix(U); - - // copy upper triangular values from mat to U - for (int i = 0; i < N; i++) { - for (int j = i; j < N; j++) { - U[(j * N) + i] = mat[(j * N) + i]; - } - } -} - -// generate permutation matrix from pivot vector -void getPmatFromPivot(DATA_TYPE *Pmat, int *P) -{ - int pivot[N]; - - // pivot vector in base-1 - // convert it to base-0 - for (int i = 0; i < N; i++) { - P[i]--; - } - - // generate permutation vector from pivot - // initialize pivot with identity sequence - for (int k = 0; k < N; k++) { - pivot[k] = k; - } - - // swap the indices according to pivot vector - for (int k = 0; k < N; k++) { - int q = P[k]; - - // swap pivot(k) and pivot(q) - int s = pivot[k]; - int t = pivot[q]; - pivot[k] = t; - pivot[q] = s; - } - - // generate permutation matrix from pivot vector - initZeroMatrix(Pmat); - for (int i = 0; i < N; i++) { - int j = pivot[i]; - Pmat[(j * N) + i] = (DATA_TYPE)1.0; - } -} - -int main(int argc, char **argv) -{ - // cuBLAS variables - cublasStatus_t status; - cublasHandle_t handle; - - // host variables - size_t matSize = N * N * sizeof(DATA_TYPE); - - DATA_TYPE *h_AarrayInput; - DATA_TYPE *h_AarrayOutput; - DATA_TYPE *h_ptr_array[BATCH_SIZE]; - - int *h_pivotArray; - int *h_infoArray; - - // device variables - DATA_TYPE *d_Aarray; - DATA_TYPE **d_ptr_array; - - int *d_pivotArray; - int *d_infoArray; - - int err_count = 0; - - // seed the rand() function with time - srand(12345); - - // find cuda device - printf("> initializing..\n"); - int dev = findCudaDevice(argc, (const char **)argv); - if (dev == -1) { - return (EXIT_FAILURE); - } - - // initialize cuBLAS - status = cublasCreate(&handle); - if (status != CUBLAS_STATUS_SUCCESS) { - printf("> ERROR: cuBLAS initialization failed..\n"); - return (EXIT_FAILURE); - } - -#ifdef DOUBLE_PRECISION - printf("> using DOUBLE precision..\n"); -#else - printf("> using SINGLE precision..\n"); -#endif - -#ifdef PIVOT - printf("> pivot ENABLED..\n"); -#else - printf("> pivot DISABLED..\n"); -#endif - - // allocate memory for host variables - h_AarrayInput = (DATA_TYPE *)xmalloc(BATCH_SIZE * matSize); - h_AarrayOutput = (DATA_TYPE *)xmalloc(BATCH_SIZE * matSize); - - h_pivotArray = (int *)xmalloc(N * BATCH_SIZE * sizeof(int)); - h_infoArray = (int *)xmalloc(BATCH_SIZE * sizeof(int)); - - // allocate memory for device variables - checkCudaErrors(cudaMalloc((void **)&d_Aarray, BATCH_SIZE * matSize)); - checkCudaErrors(cudaMalloc((void **)&d_pivotArray, N * BATCH_SIZE * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_infoArray, BATCH_SIZE * sizeof(int))); - checkCudaErrors(cudaMalloc((void **)&d_ptr_array, BATCH_SIZE * sizeof(DATA_TYPE *))); - - // fill matrix with random data - printf("> generating random matrices..\n"); - for (int i = 0; i < BATCH_SIZE; i++) { - initRandomMatrix(h_AarrayInput + (i * N * N)); - } - - // copy data to device from host - printf("> copying data from host memory to GPU memory..\n"); - checkCudaErrors(cudaMemcpy(d_Aarray, h_AarrayInput, BATCH_SIZE * matSize, cudaMemcpyHostToDevice)); - - // create pointer array for matrices - for (int i = 0; i < BATCH_SIZE; i++) - h_ptr_array[i] = d_Aarray + (i * N * N); - - // copy pointer array to device memory - checkCudaErrors(cudaMemcpy(d_ptr_array, h_ptr_array, BATCH_SIZE * sizeof(DATA_TYPE *), cudaMemcpyHostToDevice)); - - // perform LU decomposition - printf("> performing LU decomposition..\n"); -#ifdef PIVOT - status = cublasXgetrfBatched(handle, N, d_ptr_array, N, d_pivotArray, d_infoArray, BATCH_SIZE); -#else - status = cublasXgetrfBatched(handle, N, d_ptr_array, N, NULL, d_infoArray, BATCH_SIZE); -#endif /* PIVOT */ - if (status != CUBLAS_STATUS_SUCCESS) { - printf("> ERROR: cublasDgetrfBatched() failed with error %s..\n", _cudaGetErrorEnum(status)); - return (EXIT_FAILURE); - } - - // copy data to host from device - printf("> copying data from GPU memory to host memory..\n"); - checkCudaErrors(cudaMemcpy(h_AarrayOutput, d_Aarray, BATCH_SIZE * matSize, cudaMemcpyDeviceToHost)); - checkCudaErrors(cudaMemcpy(h_infoArray, d_infoArray, BATCH_SIZE * sizeof(int), cudaMemcpyDeviceToHost)); -#ifdef PIVOT - checkCudaErrors(cudaMemcpy(h_pivotArray, d_pivotArray, N * BATCH_SIZE * sizeof(int), cudaMemcpyDeviceToHost)); -#endif /* PIVOT */ - - // verify the result - printf("> verifying the result..\n"); - for (int i = 0; i < BATCH_SIZE; i++) { - if (h_infoArray[i] == 0) { - DATA_TYPE *A = h_AarrayInput + (i * N * N); - DATA_TYPE *LU = h_AarrayOutput + (i * N * N); - DATA_TYPE L[N * N]; - DATA_TYPE U[N * N]; - getLUdecoded(LU, L, U); - - // test P * A = L * U - int *P = h_pivotArray + (i * N); - DATA_TYPE Pmat[N * N]; -#ifdef PIVOT - getPmatFromPivot(Pmat, P); -#else - initIdentityMatrix(Pmat); -#endif /* PIVOT */ - - // perform matrix multiplication - DATA_TYPE PxA[N * N]; - DATA_TYPE LxU[N * N]; - matrixMultiply(PxA, Pmat, A); - matrixMultiply(LxU, L, U); - - // check for equality of matrices - if (!checkRelativeError(PxA, LxU, (DATA_TYPE)MAX_ERROR)) { - printf("> ERROR: accuracy check failed for matrix number %05d..\n", i + 1); - err_count++; - } - } - else if (h_infoArray[i] > 0) { - printf("> execution for matrix %05d is successful, but U is singular and " - "U(%d,%d) = 0..\n", - i + 1, - h_infoArray[i] - 1, - h_infoArray[i] - 1); - } - else // (h_infoArray[i] < 0) - { - printf("> ERROR: matrix %05d have an illegal value at index %d = %lf..\n", - i + 1, - -h_infoArray[i], - *(h_AarrayInput + (i * N * N) + (-h_infoArray[i]))); - } - } - - // free device variables - checkCudaErrors(cudaFree(d_ptr_array)); - checkCudaErrors(cudaFree(d_infoArray)); - checkCudaErrors(cudaFree(d_pivotArray)); - checkCudaErrors(cudaFree(d_Aarray)); - - // free host variables - if (h_infoArray) - free(h_infoArray); - if (h_pivotArray) - free(h_pivotArray); - if (h_AarrayOutput) - free(h_AarrayOutput); - if (h_AarrayInput) - free(h_AarrayInput); - - // destroy cuBLAS handle - status = cublasDestroy(handle); - if (status != CUBLAS_STATUS_SUCCESS) { - printf("> ERROR: cuBLAS uninitialization failed..\n"); - return (EXIT_FAILURE); - } - - if (err_count > 0) { - printf("> TEST FAILED for %d matrices, with precision: %g\n", err_count, MAX_ERROR); - return (EXIT_FAILURE); - } - - printf("> TEST SUCCESSFUL, with precision: %g\n", MAX_ERROR); - return (EXIT_SUCCESS); -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUFFT/CMakeLists.txt deleted file mode 100644 index a3db2d0a..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUFFT LANGUAGES CUDA) - -# Disable response file for libraries on QNX as qcc does not support lib paths with double quotes -if(CMAKE_SYSTEM_NAME STREQUAL "QNX") - set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) -endif() - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUFFT -add_executable(simpleCUFFT simpleCUFFT.cu) - -target_compile_options(simpleCUFFT PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUFFT PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUFFT PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(simpleCUFFT PRIVATE - CUDA::cufft -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT/README.md b/cpp/4_CUDA_Libraries/simpleCUFFT/README.md deleted file mode 100644 index ac63e813..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUFFT - Simple CUFFT - -## Description - -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain. cuFFT plans are created using simple and advanced API functions. - -## Key Concepts - -Image Processing, CUFFT Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMalloc, cudaMemcpy, cudaFree - -## Dependencies needed to build/run -[CUFFT](../../../README.md#cufft) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT/simpleCUFFT.cu b/cpp/4_CUDA_Libraries/simpleCUFFT/simpleCUFFT.cu deleted file mode 100644 index 6180b990..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT/simpleCUFFT.cu +++ /dev/null @@ -1,281 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* Example showing the use of CUFFT for fast 1D-convolution using FFT. */ - -// includes, system -#include -#include -#include -#include - -// includes, project -#include -#include -#include -#include -#include - -// Complex data type -typedef float2 Complex; -static __device__ __host__ inline Complex ComplexAdd(Complex, Complex); -static __device__ __host__ inline Complex ComplexScale(Complex, float); -static __device__ __host__ inline Complex ComplexMul(Complex, Complex); -static __global__ void ComplexPointwiseMulAndScale(Complex *, const Complex *, int, float); - -// Filtering functions -void Convolve(const Complex *, int, const Complex *, int, Complex *); - -// Padding functions -int PadData(const Complex *, Complex **, int, const Complex *, Complex **, int); - -//////////////////////////////////////////////////////////////////////////////// -// declaration, forward -void runTest(int argc, char **argv); - -// The filter size is assumed to be a number smaller than the signal size -#define SIGNAL_SIZE 50 -#define FILTER_KERNEL_SIZE 11 - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) { runTest(argc, argv); } - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUDA -//////////////////////////////////////////////////////////////////////////////// -void runTest(int argc, char **argv) -{ - printf("[simpleCUFFT] is starting...\n"); - - findCudaDevice(argc, (const char **)argv); - - // Allocate host memory for the signal - Complex *h_signal = reinterpret_cast(malloc(sizeof(Complex) * SIGNAL_SIZE)); - - // Initialize the memory for the signal - for (unsigned int i = 0; i < SIGNAL_SIZE; ++i) { - h_signal[i].x = rand() / static_cast(RAND_MAX); - h_signal[i].y = 0; - } - - // Allocate host memory for the filter - Complex *h_filter_kernel = reinterpret_cast(malloc(sizeof(Complex) * FILTER_KERNEL_SIZE)); - - // Initialize the memory for the filter - for (unsigned int i = 0; i < FILTER_KERNEL_SIZE; ++i) { - h_filter_kernel[i].x = rand() / static_cast(RAND_MAX); - h_filter_kernel[i].y = 0; - } - - // Pad signal and filter kernel - Complex *h_padded_signal; - Complex *h_padded_filter_kernel; - int new_size = - PadData(h_signal, &h_padded_signal, SIGNAL_SIZE, h_filter_kernel, &h_padded_filter_kernel, FILTER_KERNEL_SIZE); - int mem_size = sizeof(Complex) * new_size; - - // Allocate device memory for signal - Complex *d_signal; - checkCudaErrors(cudaMalloc(reinterpret_cast(&d_signal), mem_size)); - // Copy host memory to device - checkCudaErrors(cudaMemcpy(d_signal, h_padded_signal, mem_size, cudaMemcpyHostToDevice)); - - // Allocate device memory for filter kernel - Complex *d_filter_kernel; - checkCudaErrors(cudaMalloc(reinterpret_cast(&d_filter_kernel), mem_size)); - - // Copy host memory to device - checkCudaErrors(cudaMemcpy(d_filter_kernel, h_padded_filter_kernel, mem_size, cudaMemcpyHostToDevice)); - - // CUFFT plan simple API - cufftHandle plan; - checkCudaErrors(cufftPlan1d(&plan, new_size, CUFFT_C2C, 1)); - - // CUFFT plan advanced API - cufftHandle plan_adv; - size_t workSize; - long long int new_size_long = new_size; - - checkCudaErrors(cufftCreate(&plan_adv)); - checkCudaErrors(cufftXtMakePlanMany( - plan_adv, 1, &new_size_long, NULL, 1, 1, CUDA_C_32F, NULL, 1, 1, CUDA_C_32F, 1, &workSize, CUDA_C_32F)); - printf("Temporary buffer size %li bytes\n", workSize); - - // Transform signal and kernel - printf("Transforming signal cufftExecC2C\n"); - checkCudaErrors(cufftExecC2C( - plan, reinterpret_cast(d_signal), reinterpret_cast(d_signal), CUFFT_FORWARD)); - checkCudaErrors(cufftExecC2C(plan_adv, - reinterpret_cast(d_filter_kernel), - reinterpret_cast(d_filter_kernel), - CUFFT_FORWARD)); - - // Multiply the coefficients together and normalize the result - printf("Launching ComplexPointwiseMulAndScale<<< >>>\n"); - ComplexPointwiseMulAndScale<<<32, 256>>>(d_signal, d_filter_kernel, new_size, 1.0f / new_size); - - // Check if kernel execution generated and error - getLastCudaError("Kernel execution failed [ ComplexPointwiseMulAndScale ]"); - - // Transform signal back - printf("Transforming signal back cufftExecC2C\n"); - checkCudaErrors(cufftExecC2C( - plan, reinterpret_cast(d_signal), reinterpret_cast(d_signal), CUFFT_INVERSE)); - - // Copy device memory to host - Complex *h_convolved_signal = h_padded_signal; - checkCudaErrors(cudaMemcpy(h_convolved_signal, d_signal, mem_size, cudaMemcpyDeviceToHost)); - - // Allocate host memory for the convolution result - Complex *h_convolved_signal_ref = reinterpret_cast(malloc(sizeof(Complex) * SIGNAL_SIZE)); - - // Convolve on the host - Convolve(h_signal, SIGNAL_SIZE, h_filter_kernel, FILTER_KERNEL_SIZE, h_convolved_signal_ref); - - // check result - bool bTestResult = sdkCompareL2fe(reinterpret_cast(h_convolved_signal_ref), - reinterpret_cast(h_convolved_signal), - 2 * SIGNAL_SIZE, - 1e-5f); - - // Destroy CUFFT context - checkCudaErrors(cufftDestroy(plan)); - checkCudaErrors(cufftDestroy(plan_adv)); - - // cleanup memory - free(h_signal); - free(h_filter_kernel); - free(h_padded_signal); - free(h_padded_filter_kernel); - free(h_convolved_signal_ref); - checkCudaErrors(cudaFree(d_signal)); - checkCudaErrors(cudaFree(d_filter_kernel)); - - exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -// Pad data -int PadData(const Complex *signal, - Complex **padded_signal, - int signal_size, - const Complex *filter_kernel, - Complex **padded_filter_kernel, - int filter_kernel_size) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - int new_size = signal_size + maxRadius; - - // Pad signal - Complex *new_data = reinterpret_cast(malloc(sizeof(Complex) * new_size)); - memcpy(new_data + 0, signal, signal_size * sizeof(Complex)); - memset(new_data + signal_size, 0, (new_size - signal_size) * sizeof(Complex)); - *padded_signal = new_data; - - // Pad filter - new_data = reinterpret_cast(malloc(sizeof(Complex) * new_size)); - memcpy(new_data + 0, filter_kernel + minRadius, maxRadius * sizeof(Complex)); - memset(new_data + maxRadius, 0, (new_size - filter_kernel_size) * sizeof(Complex)); - memcpy(new_data + new_size - minRadius, filter_kernel, minRadius * sizeof(Complex)); - *padded_filter_kernel = new_data; - - return new_size; -} - -//////////////////////////////////////////////////////////////////////////////// -// Filtering operations -//////////////////////////////////////////////////////////////////////////////// - -// Computes convolution on the host -void Convolve(const Complex *signal, - int signal_size, - const Complex *filter_kernel, - int filter_kernel_size, - Complex *filtered_signal) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - - // Loop over output element indices - for (int i = 0; i < signal_size; ++i) { - filtered_signal[i].x = filtered_signal[i].y = 0; - - // Loop over convolution indices - for (int j = -maxRadius + 1; j <= minRadius; ++j) { - int k = i + j; - - if (k >= 0 && k < signal_size) { - filtered_signal[i] = - ComplexAdd(filtered_signal[i], ComplexMul(signal[k], filter_kernel[minRadius - j])); - } - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Complex operations -//////////////////////////////////////////////////////////////////////////////// - -// Complex addition -static __device__ __host__ inline Complex ComplexAdd(Complex a, Complex b) -{ - Complex c; - c.x = a.x + b.x; - c.y = a.y + b.y; - return c; -} - -// Complex scale -static __device__ __host__ inline Complex ComplexScale(Complex a, float s) -{ - Complex c; - c.x = s * a.x; - c.y = s * a.y; - return c; -} - -// Complex multiplication -static __device__ __host__ inline Complex ComplexMul(Complex a, Complex b) -{ - Complex c; - c.x = a.x * b.x - a.y * b.y; - c.y = a.x * b.y + a.y * b.x; - return c; -} - -// Complex pointwise multiplication -static __global__ void ComplexPointwiseMulAndScale(Complex *a, const Complex *b, int size, float scale) -{ - const int numThreads = blockDim.x * gridDim.x; - const int threadID = blockIdx.x * blockDim.x + threadIdx.x; - - for (int i = threadID; i < size; i += numThreads) { - a[i] = ComplexScale(ComplexMul(a[i], b[i]), scale); - } -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/CMakeLists.txt deleted file mode 100644 index 28d435f6..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/CMakeLists.txt +++ /dev/null @@ -1,47 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUFFT_2d_MGPU LANGUAGES CUDA) - -# Disable response file for libraries on QNX as qcc does not support lib paths with double quotes -if(CMAKE_SYSTEM_NAME STREQUAL "QNX") - set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) -endif() - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUFFT_2d_MGPU -add_executable(simpleCUFFT_2d_MGPU simpleCUFFT_2d_MGPU.cu) - -if(MSVC) - add_compile_definitions(_USE_MATH_DEFINES) -endif() - -target_compile_options(simpleCUFFT_2d_MGPU PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUFFT_2d_MGPU PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUFFT_2d_MGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(simpleCUFFT_2d_MGPU PRIVATE - CUDA::cufft -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/README.md b/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/README.md deleted file mode 100644 index 8d98df78..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUFFT_2d_MGPU - SimpleCUFFT_2d_MGPU - -## Description - -Example of using CUFFT. In this example, CUFFT is used to compute the 2D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU. - -## Key Concepts - -Image Processing, CUFFT Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaXtFree, cudaMemcpy, cudaFree, cudaSetDevice, cudaGetDeviceCount, cudaDeviceSynchronize, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUFFT](../../../README.md#cufft) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/simpleCUFFT_2d_MGPU.cu b/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/simpleCUFFT_2d_MGPU.cu deleted file mode 100644 index 11fe5fdd..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_2d_MGPU/simpleCUFFT_2d_MGPU.cu +++ /dev/null @@ -1,375 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -//////////////////////////////////////////////////////////////////////////////// -// -// simpleCUFFT_2d_MGPU.cu -// -// This sample code demonstrate the use of CUFFT library for 2D data on multiple GPU. -// Example showing the use of CUFFT for solving 2D-POISSON equation using FFT on multiple GPU. -// For reference we have used the equation given in http://www.bu.edu/pasi/files/2011/07/ -// Lecture83.pdf -// -//////////////////////////////////////////////////////////////////////////////// - - -// System includes -#include -#include -#include -#include - -// CUDA runtime -#include - -// CUFFT Header file -#include - -// helper functions and utilities to work with CUDA -#include -#include - -// Complex data type -typedef float2 Complex; - -// Data configuration -const int GPU_COUNT = 2; -const int BSZ_Y = 4; -const int BSZ_X = 4; - -// Forward Declaration -void solvePoissonEquation(cudaLibXtDesc *, cudaLibXtDesc *, float **, int, int); - -__global__ void solvePoisson(cufftComplex *, cufftComplex *, float *, int, int, int n_gpu); - -/////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - printf("\nPoisson equation using CUFFT library on Multiple GPUs is " - "starting...\n\n"); - - int GPU_N; - checkCudaErrors(cudaGetDeviceCount(&GPU_N)); - - if (GPU_N < GPU_COUNT) { - printf("No. of GPU on node %d\n", GPU_N); - printf("Two GPUs are required to run simpleCUFFT_2d_MGPU sample code\n"); - exit(EXIT_WAIVED); - } - - int *major_minor = (int *)malloc(sizeof(int) * GPU_N * 2); - int found2IdenticalGPUs = 0; - int nGPUs = 2; - int *whichGPUs; - whichGPUs = (int *)malloc(sizeof(int) * nGPUs); - - for (int i = 0; i < GPU_N; i++) { - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, i)); - major_minor[i * 2] = deviceProp.major; - major_minor[i * 2 + 1] = deviceProp.minor; - printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", - i, - deviceProp.name, - deviceProp.major, - deviceProp.minor); - } - - for (int i = 0; i < GPU_N; i++) { - for (int j = i + 1; j < GPU_N; j++) { - if ((major_minor[i * 2] == major_minor[j * 2]) && (major_minor[i * 2 + 1] == major_minor[j * 2 + 1])) { - whichGPUs[0] = i; - whichGPUs[1] = j; - found2IdenticalGPUs = 1; - break; - } - } - if (found2IdenticalGPUs) { - break; - } - } - - free(major_minor); - if (!found2IdenticalGPUs) { - printf("No Two GPUs with same architecture found\nWaiving simpleCUFFT_2d_MGPU " - "sample\n"); - exit(EXIT_WAIVED); - } - - int N = 64; - float xMAX = 1.0f, xMIN = 0.0f, yMIN = 0.0f, h = (xMAX - xMIN) / ((float)N), s = 0.1f, s2 = s * s; - float *x, *y, *f, *u_a, r2; - - x = (float *)malloc(sizeof(float) * N * N); - y = (float *)malloc(sizeof(float) * N * N); - f = (float *)malloc(sizeof(float) * N * N); - u_a = (float *)malloc(sizeof(float) * N * N); - - for (int j = 0; j < N; j++) - for (int i = 0; i < N; i++) { - x[N * j + i] = xMIN + i * h; - y[N * j + i] = yMIN + j * h; - r2 = (x[N * j + i] - 0.5f) * (x[N * j + i] - 0.5f) + (y[N * j + i] - 0.5f) * (y[N * j + i] - 0.5f); - f[N * j + i] = (r2 - 2 * s2) / (s2 * s2) * exp(-r2 / (2 * s2)); - u_a[N * j + i] = exp(-r2 / (2 * s2)); // analytical solution - } - - float *k, *d_k[GPU_COUNT]; - k = (float *)malloc(sizeof(float) * N); - for (int i = 0; i <= N / 2; i++) { - k[i] = i * 2 * (float)M_PI; - } - for (int i = N / 2 + 1; i < N; i++) { - k[i] = (i - N) * 2 * (float)M_PI; - } - - // Create a complex variable on host - Complex *h_f = (Complex *)malloc(sizeof(Complex) * N * N); - - // Initialize the memory for the signal - for (int i = 0; i < (N * N); i++) { - h_f[i].x = f[i]; - h_f[i].y = 0.0f; - } - - // cufftCreate() - Create an empty plan - cufftResult result; - cufftHandle planComplex; - result = cufftCreate(&planComplex); - if (result != CUFFT_SUCCESS) { - printf("cufftCreate failed\n"); - exit(EXIT_FAILURE); - } - - // cufftXtSetGPUs() - Define which GPUs to use - result = cufftXtSetGPUs(planComplex, nGPUs, whichGPUs); - - if (result == CUFFT_INVALID_DEVICE) { - printf("This sample requires two GPUs on the same board.\n"); - printf("No such board was found. Waiving sample.\n"); - exit(EXIT_WAIVED); - } - else if (result != CUFFT_SUCCESS) { - printf("cufftXtSetGPUs failed\n"); - exit(EXIT_FAILURE); - } - - // Print the device information to run the code - printf("\nRunning on GPUs\n"); - for (int i = 0; i < 2; i++) { - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, whichGPUs[i])); - printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", - whichGPUs[i], - deviceProp.name, - deviceProp.major, - deviceProp.minor); - } - - size_t *worksize; - worksize = (size_t *)malloc(sizeof(size_t) * nGPUs); - - // cufftMakePlan2d() - Create the plan - result = cufftMakePlan2d(planComplex, N, N, CUFFT_C2C, worksize); - if (result != CUFFT_SUCCESS) { - printf("*MakePlan* failed\n"); - exit(EXIT_FAILURE); - } - - for (int i = 0; i < nGPUs; i++) { - cudaSetDevice(whichGPUs[i]); - cudaMalloc((void **)&d_k[i], sizeof(float) * N); - cudaMemcpy(d_k[i], k, sizeof(float) * N, cudaMemcpyHostToDevice); - } - - // Create a variable on device - // d_f - variable on device to store the input data - // d_d_f - variable that store the natural order of d_f data - // d_out - device output - cudaLibXtDesc *d_f, *d_d_f, *d_out; - - // cufftXtMalloc() - Malloc data on multiple GPUs - - result = cufftXtMalloc(planComplex, (cudaLibXtDesc **)&d_f, CUFFT_XT_FORMAT_INPLACE); - if (result != CUFFT_SUCCESS) { - printf("*XtMalloc failed\n"); - exit(EXIT_FAILURE); - } - - result = cufftXtMalloc(planComplex, (cudaLibXtDesc **)&d_d_f, CUFFT_XT_FORMAT_INPLACE); - if (result != CUFFT_SUCCESS) { - printf("*XtMalloc failed\n"); - exit(EXIT_FAILURE); - } - - result = cufftXtMalloc(planComplex, (cudaLibXtDesc **)&d_out, CUFFT_XT_FORMAT_INPLACE); - if (result != CUFFT_SUCCESS) { - printf("*XtMalloc failed\n"); - exit(EXIT_FAILURE); - } - - // cufftXtMemcpy() - Copy the data from host to device - result = cufftXtMemcpy(planComplex, d_f, h_f, CUFFT_COPY_HOST_TO_DEVICE); - if (result != CUFFT_SUCCESS) { - printf("*XtMemcpy failed\n"); - exit(EXIT_FAILURE); - } - - // cufftXtExecDescriptorC2C() - Execute FFT on data on multiple GPUs - printf("Forward 2d FFT on multiple GPUs\n"); - result = cufftXtExecDescriptorC2C(planComplex, d_f, d_f, CUFFT_FORWARD); - if (result != CUFFT_SUCCESS) { - printf("*XtExecC2C failed\n"); - exit(EXIT_FAILURE); - } - - // cufftXtMemcpy() - Copy the data to natural order on GPUs - result = cufftXtMemcpy(planComplex, d_d_f, d_f, CUFFT_COPY_DEVICE_TO_DEVICE); - if (result != CUFFT_SUCCESS) { - printf("*XtMemcpy failed\n"); - exit(EXIT_FAILURE); - } - - printf("Solve Poisson Equation\n"); - solvePoissonEquation(d_d_f, d_out, d_k, N, nGPUs); - - printf("Inverse 2d FFT on multiple GPUs\n"); - // cufftXtExecDescriptorC2C() - Execute inverse FFT on data on multiple GPUs - result = cufftXtExecDescriptorC2C(planComplex, d_out, d_out, CUFFT_INVERSE); - if (result != CUFFT_SUCCESS) { - printf("*XtExecC2C failed\n"); - exit(EXIT_FAILURE); - } - - // Create a variable on host to copy the data from device - // h_d_out - variable store the output of device - Complex *h_d_out = (Complex *)malloc(sizeof(Complex) * N * N); - - // cufftXtMemcpy() - Copy data from multiple GPUs to host - result = cufftXtMemcpy(planComplex, h_d_out, d_out, CUFFT_COPY_DEVICE_TO_HOST); - if (result != CUFFT_SUCCESS) { - printf("*XtMemcpy failed\n"); - exit(EXIT_FAILURE); - } - - float *out = (float *)malloc(sizeof(float) * N * N); - float constant = h_d_out[0].x / N * N; - for (int i = 0; i < N * N; i++) { - // subtract u[0] to force the arbitrary constant to be 0 - out[i] = (h_d_out[i].x / (N * N)) - constant; - } - - // cleanup memory - - free(h_f); - free(k); - free(out); - free(h_d_out); - free(x); - free(whichGPUs); - free(y); - free(f); - free(u_a); - free(worksize); - - // cudaXtFree() - Free GPU memory - for (int i = 0; i < GPU_COUNT; i++) { - cudaFree(d_k[i]); - } - result = cufftXtFree(d_out); - if (result != CUFFT_SUCCESS) { - printf("*XtFree failed\n"); - exit(EXIT_FAILURE); - } - result = cufftXtFree(d_f); - if (result != CUFFT_SUCCESS) { - printf("*XtFree failed\n"); - exit(EXIT_FAILURE); - } - result = cufftXtFree(d_d_f); - if (result != CUFFT_SUCCESS) { - printf("*XtFree failed\n"); - exit(EXIT_FAILURE); - } - - // cufftDestroy() - Destroy FFT plan - result = cufftDestroy(planComplex); - if (result != CUFFT_SUCCESS) { - printf("cufftDestroy failed: code %d\n", (int)result); - exit(EXIT_FAILURE); - } - - exit(EXIT_SUCCESS); -} - -//////////////////////////////////////////////////////////////////////////////////// -// Launch kernel on multiple GPU -/////////////////////////////////////////////////////////////////////////////////// -void solvePoissonEquation(cudaLibXtDesc *d_ft, cudaLibXtDesc *d_ft_k, float **k, int N, int nGPUs) -{ - int device; - dim3 dimGrid(int(N / BSZ_X), int((N / 2) / BSZ_Y)); - dim3 dimBlock(BSZ_X, BSZ_Y); - - for (int i = 0; i < nGPUs; i++) { - device = d_ft_k->descriptor->GPUs[i]; - cudaSetDevice(device); - solvePoisson<<>>( - (cufftComplex *)d_ft->descriptor->data[i], (cufftComplex *)d_ft_k->descriptor->data[i], k[i], N, i, nGPUs); - } - - // Wait for device to finish all operation - for (int i = 0; i < nGPUs; i++) { - device = d_ft_k->descriptor->GPUs[i]; - cudaSetDevice(device); - cudaDeviceSynchronize(); - - // Check if kernel execution generated and error - getLastCudaError("Kernel execution failed [ solvePoisson ]"); - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Kernel for Solving Poisson equation on GPU -//////////////////////////////////////////////////////////////////////////////// -__global__ void solvePoisson(cufftComplex *ft, cufftComplex *ft_k, float *k, int N, int gpu_id, int n_gpu) -{ - int i = threadIdx.x + blockIdx.x * blockDim.x; - int j = threadIdx.y + blockIdx.y * blockDim.y; - int index = j * N + i; - if (i < N && j < N / n_gpu) { - float k2 = k[i] * k[i] + k[j + gpu_id * N / n_gpu] * k[j + gpu_id * N / n_gpu]; - if (i == 0 && j == 0 && gpu_id == 0) { - k2 = 1.0f; - } - - ft_k[index].x = -ft[index].x * 1 / k2; - ft_k[index].y = -ft[index].y * 1 / k2; - } -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/CMakeLists.txt deleted file mode 100644 index 4f0f1aaa..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUFFT_MGPU LANGUAGES CUDA) - -# Disable response file for libraries on QNX as qcc does not support lib paths with double quotes -if(CMAKE_SYSTEM_NAME STREQUAL "QNX") - set(CMAKE_CUDA_USE_RESPONSE_FILE_FOR_LIBRARIES OFF) -endif() - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for simpleCUFFT_MGPU -add_executable(simpleCUFFT_MGPU simpleCUFFT_MGPU.cu) - -target_compile_options(simpleCUFFT_MGPU PRIVATE $<$:--extended-lambda>) - -target_compile_features(simpleCUFFT_MGPU PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(simpleCUFFT_MGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(simpleCUFFT_MGPU PRIVATE - CUDA::cufft -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/README.md b/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/README.md deleted file mode 100644 index ac4a57c4..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUFFT_MGPU - Simple CUFFT_MGPU - -## Description - -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain on Multiple GPU. - -## Key Concepts - -Image Processing, CUFFT Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaXtFree, cudaSetDevice, cudaGetDeviceCount, cudaDeviceSynchronize, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUFFT](../../../README.md#cufft) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/simpleCUFFT_MGPU.cu b/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/simpleCUFFT_MGPU.cu deleted file mode 100644 index ede799b0..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_MGPU/simpleCUFFT_MGPU.cu +++ /dev/null @@ -1,387 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* Example showing the use of CUFFT for fast 1D-convolution using FFT. */ - -// System includes -#include -#include -#include -#include - -// CUDA runtime -#include - -// CUFFT Header file -#include - -// helper functions and utilities to work with CUDA -#include -#include - -// Complex data type -typedef float2 Complex; - -static __device__ __host__ inline Complex ComplexAdd(Complex, Complex); -static __device__ __host__ inline Complex ComplexScale(Complex, float); -static __device__ __host__ inline Complex ComplexMul(Complex, Complex); -static __global__ void ComplexPointwiseMulAndScale(cufftComplex *, cufftComplex *, int, float); - -// Kernel for GPU -void multiplyCoefficient(cudaLibXtDesc *, cudaLibXtDesc *, int, float, int); - -// Filtering functions -void Convolve(const Complex *, int, const Complex *, int, Complex *); - -// Padding functions -int PadData(const Complex *, Complex **, int, const Complex *, Complex **, int); - -//////////////////////////////////////////////////////////////////////////////// -// Data configuration -// The filter size is assumed to be a number smaller than the signal size -/////////////////////////////////////////////////////////////////////////////// -const int SIGNAL_SIZE = 1018; -const int FILTER_KERNEL_SIZE = 11; -const int GPU_COUNT = 2; - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - printf("\n[simpleCUFFT_MGPU] is starting...\n\n"); - - int GPU_N; - checkCudaErrors(cudaGetDeviceCount(&GPU_N)); - - if (GPU_N < GPU_COUNT) { - printf("No. of GPU on node %d\n", GPU_N); - printf("Two GPUs are required to run simpleCUFFT_MGPU sample code\n"); - exit(EXIT_WAIVED); - } - - int *major_minor = (int *)malloc(sizeof(int) * GPU_N * 2); - int found2IdenticalGPUs = 0; - int nGPUs = 2; - int *whichGPUs; - whichGPUs = (int *)malloc(sizeof(int) * nGPUs); - - for (int i = 0; i < GPU_N; i++) { - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, i)); - major_minor[i * 2] = deviceProp.major; - major_minor[i * 2 + 1] = deviceProp.minor; - printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", - i, - deviceProp.name, - deviceProp.major, - deviceProp.minor); - } - - for (int i = 0; i < GPU_N; i++) { - for (int j = i + 1; j < GPU_N; j++) { - if ((major_minor[i * 2] == major_minor[j * 2]) && (major_minor[i * 2 + 1] == major_minor[j * 2 + 1])) { - whichGPUs[0] = i; - whichGPUs[1] = j; - found2IdenticalGPUs = 1; - break; - } - } - if (found2IdenticalGPUs) { - break; - } - } - - free(major_minor); - if (!found2IdenticalGPUs) { - printf("No Two GPUs with same architecture found\nWaiving simpleCUFFT_2d_MGPU " - "sample\n"); - exit(EXIT_WAIVED); - } - - // Allocate host memory for the signal - Complex *h_signal = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE); - - // Initialize the memory for the signal - for (int i = 0; i < SIGNAL_SIZE; ++i) { - h_signal[i].x = rand() / (float)RAND_MAX; - h_signal[i].y = 0; - } - - // Allocate host memory for the filter - Complex *h_filter_kernel = (Complex *)malloc(sizeof(Complex) * FILTER_KERNEL_SIZE); - - // Initialize the memory for the filter - for (int i = 0; i < FILTER_KERNEL_SIZE; ++i) { - h_filter_kernel[i].x = rand() / (float)RAND_MAX; - h_filter_kernel[i].y = 0; - } - - // Pad signal and filter kernel - Complex *h_padded_signal; - Complex *h_padded_filter_kernel; - int new_size = - PadData(h_signal, &h_padded_signal, SIGNAL_SIZE, h_filter_kernel, &h_padded_filter_kernel, FILTER_KERNEL_SIZE); - - // cufftCreate() - Create an empty plan - cufftResult result; - cufftHandle plan_input; - checkCudaErrors(cufftCreate(&plan_input)); - - // cufftXtSetGPUs() - Define which GPUs to use - result = cufftXtSetGPUs(plan_input, nGPUs, whichGPUs); - - if (result == CUFFT_INVALID_DEVICE) { - printf("This sample requires two GPUs on the same board.\n"); - printf("No such board was found. Waiving sample.\n"); - exit(EXIT_WAIVED); - } - else if (result != CUFFT_SUCCESS) { - printf("cufftXtSetGPUs failed\n"); - exit(EXIT_FAILURE); - } - - // Print the device information to run the code - printf("\nRunning on GPUs\n"); - for (int i = 0; i < nGPUs; i++) { - cudaDeviceProp deviceProp; - checkCudaErrors(cudaGetDeviceProperties(&deviceProp, whichGPUs[i])); - printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", - whichGPUs[i], - deviceProp.name, - deviceProp.major, - deviceProp.minor); - } - - size_t *worksize; - worksize = (size_t *)malloc(sizeof(size_t) * nGPUs); - - // cufftMakePlan1d() - Create the plan - checkCudaErrors(cufftMakePlan1d(plan_input, new_size, CUFFT_C2C, 1, worksize)); - - // cufftXtMalloc() - Malloc data on multiple GPUs - cudaLibXtDesc *d_signal; - checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_signal, CUFFT_XT_FORMAT_INPLACE)); - cudaLibXtDesc *d_out_signal; - checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_out_signal, CUFFT_XT_FORMAT_INPLACE)); - cudaLibXtDesc *d_filter_kernel; - checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_filter_kernel, CUFFT_XT_FORMAT_INPLACE)); - cudaLibXtDesc *d_out_filter_kernel; - checkCudaErrors(cufftXtMalloc(plan_input, (cudaLibXtDesc **)&d_out_filter_kernel, CUFFT_XT_FORMAT_INPLACE)); - - // cufftXtMemcpy() - Copy data from host to multiple GPUs - checkCudaErrors(cufftXtMemcpy(plan_input, d_signal, h_padded_signal, CUFFT_COPY_HOST_TO_DEVICE)); - checkCudaErrors(cufftXtMemcpy(plan_input, d_filter_kernel, h_padded_filter_kernel, CUFFT_COPY_HOST_TO_DEVICE)); - - // cufftXtExecDescriptorC2C() - Execute FFT on data on multiple GPUs - checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_signal, d_signal, CUFFT_FORWARD)); - checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_filter_kernel, d_filter_kernel, CUFFT_FORWARD)); - - // cufftXtMemcpy() - Copy the data to natural order on GPUs - checkCudaErrors(cufftXtMemcpy(plan_input, d_out_signal, d_signal, CUFFT_COPY_DEVICE_TO_DEVICE)); - checkCudaErrors(cufftXtMemcpy(plan_input, d_out_filter_kernel, d_filter_kernel, CUFFT_COPY_DEVICE_TO_DEVICE)); - - printf("\n\nValue of Library Descriptor\n"); - printf("Number of GPUs %d\n", d_out_signal->descriptor->nGPUs); - printf("Device id %d %d\n", d_out_signal->descriptor->GPUs[0], d_out_signal->descriptor->GPUs[1]); - printf("Data size on GPU %ld %ld\n", - (long)(d_out_signal->descriptor->size[0] / sizeof(cufftComplex)), - (long)(d_out_signal->descriptor->size[1] / sizeof(cufftComplex))); - - // Multiply the coefficients together and normalize the result - printf("Launching ComplexPointwiseMulAndScale<<< >>>\n"); - multiplyCoefficient(d_out_signal, d_out_filter_kernel, new_size, 1.0f / new_size, nGPUs); - - // cufftXtExecDescriptorC2C() - Execute inverse FFT on data on multiple GPUs - printf("Transforming signal back cufftExecC2C\n"); - checkCudaErrors(cufftXtExecDescriptorC2C(plan_input, d_out_signal, d_out_signal, CUFFT_INVERSE)); - - // Create host pointer pointing to padded signal - Complex *h_convolved_signal = h_padded_signal; - - // Allocate host memory for the convolution result - Complex *h_convolved_signal_ref = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE); - - // cufftXtMemcpy() - Copy data from multiple GPUs to host - checkCudaErrors(cufftXtMemcpy(plan_input, h_convolved_signal, d_out_signal, CUFFT_COPY_DEVICE_TO_HOST)); - - // Convolve on the host - Convolve(h_signal, SIGNAL_SIZE, h_filter_kernel, FILTER_KERNEL_SIZE, h_convolved_signal_ref); - - // Compare CPU and GPU result - bool bTestResult = - sdkCompareL2fe((float *)h_convolved_signal_ref, (float *)h_convolved_signal, 2 * SIGNAL_SIZE, 1e-5f); - printf("\nvalue of TestResult %d\n", bTestResult); - - // Cleanup memory - free(whichGPUs); - free(worksize); - free(h_signal); - free(h_filter_kernel); - free(h_padded_signal); - free(h_padded_filter_kernel); - free(h_convolved_signal_ref); - - // cudaXtFree() - Free GPU memory - checkCudaErrors(cufftXtFree(d_signal)); - checkCudaErrors(cufftXtFree(d_filter_kernel)); - checkCudaErrors(cufftXtFree(d_out_signal)); - checkCudaErrors(cufftXtFree(d_out_filter_kernel)); - - // cufftDestroy() - Destroy FFT plan - checkCudaErrors(cufftDestroy(plan_input)); - - exit(bTestResult ? EXIT_SUCCESS : EXIT_FAILURE); -} - -/////////////////////////////////////////////////////////////////////////////////// -// Function for padding original data -////////////////////////////////////////////////////////////////////////////////// -int PadData(const Complex *signal, - Complex **padded_signal, - int signal_size, - const Complex *filter_kernel, - Complex **padded_filter_kernel, - int filter_kernel_size) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - int new_size = signal_size + maxRadius; - - // Pad signal - Complex *new_data = (Complex *)malloc(sizeof(Complex) * new_size); - memcpy(new_data + 0, signal, signal_size * sizeof(Complex)); - memset(new_data + signal_size, 0, (new_size - signal_size) * sizeof(Complex)); - *padded_signal = new_data; - - // Pad filter - new_data = (Complex *)malloc(sizeof(Complex) * new_size); - memcpy(new_data + 0, filter_kernel + minRadius, maxRadius * sizeof(Complex)); - memset(new_data + maxRadius, 0, (new_size - filter_kernel_size) * sizeof(Complex)); - memcpy(new_data + new_size - minRadius, filter_kernel, minRadius * sizeof(Complex)); - *padded_filter_kernel = new_data; - - return new_size; -} - -//////////////////////////////////////////////////////////////////////////////// -// Filtering operations - Computing Convolution on the host -//////////////////////////////////////////////////////////////////////////////// -void Convolve(const Complex *signal, - int signal_size, - const Complex *filter_kernel, - int filter_kernel_size, - Complex *filtered_signal) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - - // Loop over output element indices - for (int i = 0; i < signal_size; ++i) { - filtered_signal[i].x = filtered_signal[i].y = 0; - - // Loop over convolution indices - for (int j = -maxRadius + 1; j <= minRadius; ++j) { - int k = i + j; - - if (k >= 0 && k < signal_size) { - filtered_signal[i] = - ComplexAdd(filtered_signal[i], ComplexMul(signal[k], filter_kernel[minRadius - j])); - } - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Launch Kernel on multiple GPU -//////////////////////////////////////////////////////////////////////////////// -void multiplyCoefficient(cudaLibXtDesc *d_signal, cudaLibXtDesc *d_filter_kernel, int new_size, float val, int nGPUs) -{ - int device; - // Launch the ComplexPointwiseMulAndScale<<< >>> kernel on multiple GPU - for (int i = 0; i < nGPUs; i++) { - device = d_signal->descriptor->GPUs[i]; - - // Set device - checkCudaErrors(cudaSetDevice(device)); - - // Perform GPU computations - ComplexPointwiseMulAndScale<<<32, 256>>>((cufftComplex *)d_signal->descriptor->data[i], - (cufftComplex *)d_filter_kernel->descriptor->data[i], - int(d_signal->descriptor->size[i] / sizeof(cufftComplex)), - val); - } - - // Wait for device to finish all operation - for (int i = 0; i < nGPUs; i++) { - device = d_signal->descriptor->GPUs[i]; - checkCudaErrors(cudaSetDevice(device)); - cudaDeviceSynchronize(); - // Check if kernel execution generated and error - getLastCudaError("Kernel execution failed [ ComplexPointwiseMulAndScale ]"); - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Complex operations -//////////////////////////////////////////////////////////////////////////////// - -// Complex addition -static __device__ __host__ inline Complex ComplexAdd(Complex a, Complex b) -{ - Complex c; - c.x = a.x + b.x; - c.y = a.y + b.y; - return c; -} - -// Complex scale -static __device__ __host__ inline Complex ComplexScale(Complex a, float s) -{ - Complex c; - c.x = s * a.x; - c.y = s * a.y; - return c; -} - -// Complex multiplication -static __device__ __host__ inline Complex ComplexMul(Complex a, Complex b) -{ - Complex c; - c.x = a.x * b.x - a.y * b.y; - c.y = a.x * b.y + a.y * b.x; - return c; -} -// Complex pointwise multiplication -static __global__ void ComplexPointwiseMulAndScale(cufftComplex *a, cufftComplex *b, int size, float scale) -{ - const int numThreads = blockDim.x * gridDim.x; - const int threadID = blockIdx.x * blockDim.x + threadIdx.x; - for (int i = threadID; i < size; i += numThreads) { - a[i] = ComplexScale(ComplexMul(a[i], b[i]), scale); - } -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/extensions.json b/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/CMakeLists.txt b/cpp/4_CUDA_Libraries/simpleCUFFT_callback/CMakeLists.txt deleted file mode 100644 index 341796e9..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/CMakeLists.txt +++ /dev/null @@ -1,43 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(simpleCUFFT_callback LANGUAGES CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file - # Add target for simpleCUFFT_callback - add_executable(simpleCUFFT_callback simpleCUFFT_callback.cu) - - target_compile_options(simpleCUFFT_callback PRIVATE $<$:--extended-lambda>) - - target_compile_features(simpleCUFFT_callback PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleCUFFT_callback PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - - target_link_libraries(simpleCUFFT_callback PRIVATE - CUDA::cufft_static - culibos - ) -else() - message(STATUS "Will not build sample simpleCUFFT_callback - requires Linux OS") -endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/README.md b/cpp/4_CUDA_Libraries/simpleCUFFT_callback/README.md deleted file mode 100644 index 0ac3eafc..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# simpleCUFFT_callback - Simple CUFFT Callbacks - -## Description - -Example of using CUFFT. In this example, CUFFT is used to compute the 1D-convolution of some signal with some filter by transforming both into frequency domain, multiplying them together, and transforming the signal back to time domain. The difference between this example and the Simple CUFFT example is that the multiplication step is done by the CUFFT kernel with a user-supplied CUFFT callback routine, rather than by a separate kernel call. - -## Key Concepts - -Image Processing, CUFFT Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaMemcpy, cudaFree, cudaMemcpyFromSymbol, cudaGetDevice, cudaMalloc, cudaGetDeviceProperties - -## Dependencies needed to build/run -[CUFFT Callback Routines](../../../README.md#cufft-callback-routines), [CUFFT](../../../README.md#cufft) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/simpleCUFFT_callback.cu b/cpp/4_CUDA_Libraries/simpleCUFFT_callback/simpleCUFFT_callback.cu deleted file mode 100644 index 5f636e1d..00000000 --- a/cpp/4_CUDA_Libraries/simpleCUFFT_callback/simpleCUFFT_callback.cu +++ /dev/null @@ -1,317 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -/* - * Example showing the use of CUFFT for fast 1D-convolution using FFT. - * This sample is the same as simpleCUFFT, except that it uses a callback - * function to perform the pointwise multiply and scale, on input to the - * inverse transform. - * - */ - -// includes, system -#include -#include -#include -#include - -// includes, project -#include -#include -#include -#include -#include - -// Complex data type -typedef float2 Complex; -static __device__ __host__ inline Complex ComplexAdd(Complex, Complex); -static __device__ __host__ inline Complex ComplexScale(Complex, float); -static __device__ __host__ inline Complex ComplexMul(Complex, Complex); - -// This is the callback routine prototype -static __device__ cufftComplex ComplexPointwiseMulAndScale(void *a, size_t index, void *cb_info, void *sharedmem); - -typedef struct _cb_params -{ - Complex *filter; - float scale; -} cb_params; - -// This is the callback routine. It does complex pointwise multiplication with -// scaling. -static __device__ cufftComplex ComplexPointwiseMulAndScale(void *a, size_t index, void *cb_info, void *sharedmem) -{ - cb_params *my_params = (cb_params *)cb_info; - return (cufftComplex)ComplexScale(ComplexMul(((Complex *)a)[index], (my_params->filter)[index]), my_params->scale); -} - -// Define the device pointer to the callback routine. The host code will fetch -// this and pass it to CUFFT -__device__ cufftCallbackLoadC myOwnCallbackPtr = ComplexPointwiseMulAndScale; -// Filtering functions -void Convolve(const Complex *, int, const Complex *, int, Complex *); - -// Padding functions -int PadData(const Complex *, Complex **, int, const Complex *, Complex **, int); - -//////////////////////////////////////////////////////////////////////////////// -// declaration, forward -int runTest(int argc, char **argv); - -// The filter size is assumed to be a number smaller than the signal size -#define SIGNAL_SIZE 50 -#define FILTER_KERNEL_SIZE 11 - -//////////////////////////////////////////////////////////////////////////////// -// Program main -//////////////////////////////////////////////////////////////////////////////// -int main(int argc, char **argv) -{ - struct cudaDeviceProp properties; - int device; - checkCudaErrors(cudaGetDevice(&device)); - checkCudaErrors(cudaGetDeviceProperties(&properties, device)); - if (!(properties.major >= 2)) { - printf("simpleCUFFT_callback requires CUDA architecture SM2.0 or higher\n"); - return EXIT_WAIVED; - } - - return runTest(argc, argv); -} - -//////////////////////////////////////////////////////////////////////////////// -//! Run a simple test for CUFFT callbacks -//////////////////////////////////////////////////////////////////////////////// -int runTest(int argc, char **argv) -{ - printf("[simpleCUFFT_callback] is starting...\n"); - - findCudaDevice(argc, (const char **)argv); - - // Allocate host memory for the signal - Complex *h_signal = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE); - - // Initialize the memory for the signal - for (unsigned int i = 0; i < SIGNAL_SIZE; ++i) { - h_signal[i].x = rand() / (float)RAND_MAX; - h_signal[i].y = 0; - } - - // Allocate host memory for the filter - Complex *h_filter_kernel = (Complex *)malloc(sizeof(Complex) * FILTER_KERNEL_SIZE); - - // Initialize the memory for the filter - for (unsigned int i = 0; i < FILTER_KERNEL_SIZE; ++i) { - h_filter_kernel[i].x = rand() / (float)RAND_MAX; - h_filter_kernel[i].y = 0; - } - - // Pad signal and filter kernel - Complex *h_padded_signal; - Complex *h_padded_filter_kernel; - int new_size = - PadData(h_signal, &h_padded_signal, SIGNAL_SIZE, h_filter_kernel, &h_padded_filter_kernel, FILTER_KERNEL_SIZE); - int mem_size = sizeof(Complex) * new_size; - - // Allocate device memory for signal - Complex *d_signal; - checkCudaErrors(cudaMalloc((void **)&d_signal, mem_size)); - // Copy host memory to device - checkCudaErrors(cudaMemcpy(d_signal, h_padded_signal, mem_size, cudaMemcpyHostToDevice)); - - // Allocate device memory for filter kernel - Complex *d_filter_kernel; - checkCudaErrors(cudaMalloc((void **)&d_filter_kernel, mem_size)); - - // Copy host memory to device - checkCudaErrors(cudaMemcpy(d_filter_kernel, h_padded_filter_kernel, mem_size, cudaMemcpyHostToDevice)); - - // Create one CUFFT plan for the forward transforms, and one for the reverse - // transform with load callback. - cufftHandle plan, cb_plan; - size_t work_size; - - checkCudaErrors(cufftCreate(&plan)); - checkCudaErrors(cufftCreate(&cb_plan)); - - checkCudaErrors(cufftMakePlan1d(plan, new_size, CUFFT_C2C, 1, &work_size)); - checkCudaErrors(cufftMakePlan1d(cb_plan, new_size, CUFFT_C2C, 1, &work_size)); - - // Define a structure used to pass in the device address of the filter kernel, - // and the scale factor - cb_params h_params; - - h_params.filter = d_filter_kernel; - h_params.scale = 1.0f / new_size; - - // Allocate device memory for parameters - cb_params *d_params; - checkCudaErrors(cudaMalloc((void **)&d_params, sizeof(cb_params))); - - // Copy host memory to device - checkCudaErrors(cudaMemcpy(d_params, &h_params, sizeof(cb_params), cudaMemcpyHostToDevice)); - - // The host needs to get a copy of the device pointer to the callback - cufftCallbackLoadC hostCopyOfCallbackPtr; - - checkCudaErrors(cudaMemcpyFromSymbol(&hostCopyOfCallbackPtr, myOwnCallbackPtr, sizeof(hostCopyOfCallbackPtr))); - - // Now associate the load callback with the plan. - checkCudaErrors( - cufftXtSetCallback(cb_plan, (void **)&hostCopyOfCallbackPtr, CUFFT_CB_LD_COMPLEX, (void **)&d_params)); - - // Transform signal and kernel - printf("Transforming signal cufftExecC2C\n"); - checkCudaErrors(cufftExecC2C(plan, (cufftComplex *)d_signal, (cufftComplex *)d_signal, CUFFT_FORWARD)); - checkCudaErrors( - cufftExecC2C(plan, (cufftComplex *)d_filter_kernel, (cufftComplex *)d_filter_kernel, CUFFT_FORWARD)); - - // Transform signal back, using the callback to do the pointwise multiply on - // the way in. - printf("Transforming signal back cufftExecC2C\n"); - checkCudaErrors(cufftExecC2C(cb_plan, (cufftComplex *)d_signal, (cufftComplex *)d_signal, CUFFT_INVERSE)); - - // Copy device memory to host - Complex *h_convolved_signal = h_padded_signal; - checkCudaErrors(cudaMemcpy(h_convolved_signal, d_signal, mem_size, cudaMemcpyDeviceToHost)); - - // Allocate host memory for the convolution result - Complex *h_convolved_signal_ref = (Complex *)malloc(sizeof(Complex) * SIGNAL_SIZE); - - // Convolve on the host - Convolve(h_signal, SIGNAL_SIZE, h_filter_kernel, FILTER_KERNEL_SIZE, h_convolved_signal_ref); - - // check result - bool bTestResult = - sdkCompareL2fe((float *)h_convolved_signal_ref, (float *)h_convolved_signal, 2 * SIGNAL_SIZE, 1e-5f); - - // Destroy CUFFT context - checkCudaErrors(cufftDestroy(plan)); - checkCudaErrors(cufftDestroy(cb_plan)); - - // cleanup memory - free(h_signal); - free(h_filter_kernel); - free(h_padded_signal); - free(h_padded_filter_kernel); - free(h_convolved_signal_ref); - checkCudaErrors(cudaFree(d_signal)); - checkCudaErrors(cudaFree(d_filter_kernel)); - checkCudaErrors(cudaFree(d_params)); - - return bTestResult ? EXIT_SUCCESS : EXIT_FAILURE; -} - -// Pad data -int PadData(const Complex *signal, - Complex **padded_signal, - int signal_size, - const Complex *filter_kernel, - Complex **padded_filter_kernel, - int filter_kernel_size) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - int new_size = signal_size + maxRadius; - - // Pad signal - Complex *new_data = (Complex *)malloc(sizeof(Complex) * new_size); - memcpy(new_data + 0, signal, signal_size * sizeof(Complex)); - memset(new_data + signal_size, 0, (new_size - signal_size) * sizeof(Complex)); - *padded_signal = new_data; - - // Pad filter - new_data = (Complex *)malloc(sizeof(Complex) * new_size); - memcpy(new_data + 0, filter_kernel + minRadius, maxRadius * sizeof(Complex)); - memset(new_data + maxRadius, 0, (new_size - filter_kernel_size) * sizeof(Complex)); - memcpy(new_data + new_size - minRadius, filter_kernel, minRadius * sizeof(Complex)); - *padded_filter_kernel = new_data; - - return new_size; -} - -//////////////////////////////////////////////////////////////////////////////// -// Filtering operations -//////////////////////////////////////////////////////////////////////////////// - -// Computes convolution on the host -void Convolve(const Complex *signal, - int signal_size, - const Complex *filter_kernel, - int filter_kernel_size, - Complex *filtered_signal) -{ - int minRadius = filter_kernel_size / 2; - int maxRadius = filter_kernel_size - minRadius; - - // Loop over output element indices - for (int i = 0; i < signal_size; ++i) { - filtered_signal[i].x = filtered_signal[i].y = 0; - - // Loop over convolution indices - for (int j = -maxRadius + 1; j <= minRadius; ++j) { - int k = i + j; - - if (k >= 0 && k < signal_size) { - filtered_signal[i] = - ComplexAdd(filtered_signal[i], ComplexMul(signal[k], filter_kernel[minRadius - j])); - } - } - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Complex operations -//////////////////////////////////////////////////////////////////////////////// - -// Complex addition -static __device__ __host__ inline Complex ComplexAdd(Complex a, Complex b) -{ - Complex c; - c.x = a.x + b.x; - c.y = a.y + b.y; - return c; -} - -// Complex scale -static __device__ __host__ inline Complex ComplexScale(Complex a, float s) -{ - Complex c; - c.x = s * a.x; - c.y = s * a.y; - return c; -} - -// Complex multiplication -static __device__ __host__ inline Complex ComplexMul(Complex a, Complex b) -{ - Complex c; - c.x = a.x * b.x - a.y * b.y; - c.y = a.x * b.y + a.y * b.x; - return c; -} diff --git a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/c_cpp_properties.json b/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/extensions.json b/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/CMakeLists.txt b/cpp/4_CUDA_Libraries/watershedSegmentationNPP/CMakeLists.txt deleted file mode 100644 index 744a4920..00000000 --- a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/CMakeLists.txt +++ /dev/null @@ -1,71 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(watershedSegmentationNPP LANGUAGES CXX) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# This sample is not supported on QNX -if(CMAKE_SYSTEM_NAME STREQUAL "QNX") - message(STATUS "Will not build sample ${PROJECT_NAME} - not supported on QNX") - return() -endif() - -# Source file -# Add target for watershedSegmentationNPP -add_executable(watershedSegmentationNPP watershedSegmentationNPP.cpp) - -target_compile_options(watershedSegmentationNPP PRIVATE $<$:--extended-lambda>) - -target_compile_features(watershedSegmentationNPP PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(watershedSegmentationNPP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_include_directories(watershedSegmentationNPP PRIVATE - ${CUDAToolkit_INCLUDE_DIRS} -) -target_link_libraries(watershedSegmentationNPP PRIVATE - CUDA::nppc - CUDA::nppisu - CUDA::nppif - CUDA::cudart -) - -# Copy data files to output directory -add_custom_command(TARGET watershedSegmentationNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/data/teapot_512x512_8u_Gray.raw - $/ -) - -# Copy data files to output directory -add_custom_command(TARGET watershedSegmentationNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/data/CT_skull_512x512_8u_Gray.raw - $/ -) - -# Copy data files to output directory -add_custom_command(TARGET watershedSegmentationNPP POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/data/Rocks_512x512_8u_Gray.raw - $/ -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/README.md b/cpp/4_CUDA_Libraries/watershedSegmentationNPP/README.md deleted file mode 100644 index 6db12b81..00000000 --- a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# watershedSegmentationNPP - Watershed Segmentation NPP - -## Description - -An NPP CUDA Sample that demonstrates how to use the NPP watershed segmentation function. - -## Key Concepts - -Performance Strategies, Image Processing, NPP Library - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaRuntimeGetVersion, cudaFree, cudaDeviceGetAttribute, cudaDriverGetVersion, cudaGetDevice, cudaStreamGetFlags, cudaStreamSynchronize, cudaGetDeviceProperties - -## Dependencies needed to build/run -[NPP](../../../README.md#npp) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/watershedSegmentationNPP.cpp b/cpp/4_CUDA_Libraries/watershedSegmentationNPP/watershedSegmentationNPP.cpp deleted file mode 100644 index bcd8c572..00000000 --- a/cpp/4_CUDA_Libraries/watershedSegmentationNPP/watershedSegmentationNPP.cpp +++ /dev/null @@ -1,596 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - - -#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64) -#define WINDOWS_LEAN_AND_MEAN -#define NOMINMAX -#include -#pragma warning(disable : 4819) -#endif - -#include -#include -#include -#include -#include - -// Note: If you want to view these images we HIGHLY recommend using imagej which is free on the internet and works on -// most platforms -// because it is one of the few image viewing apps that can display 32 bit integer image data. While it -// normalizes the data to floating point values for viewing it still provides a good representation of the -// relative brightness of each label value. -// -// The files read and written by this sample app use RAW image format, that is, only the image data itself exists -// in the files with no image format information. When viewing RAW files with imagej just enter the image size -// and bit depth values that are part of the file name when requested by imagej. -// - -#define NUMBER_OF_IMAGES 3 - -Npp8u *pInputImageDev[NUMBER_OF_IMAGES]; -Npp8u *pInputImageHost[NUMBER_OF_IMAGES]; -Npp8u *pSegmentationScratchBufferDev[NUMBER_OF_IMAGES]; -Npp8u *pSegmentsDev[NUMBER_OF_IMAGES]; -Npp8u *pSegmentsHost[NUMBER_OF_IMAGES]; -Npp32u *pSegmentLabelsOutputBufferDev[NUMBER_OF_IMAGES]; -Npp32u *pSegmentLabelsOutputBufferHost[NUMBER_OF_IMAGES]; - -void tearDown() // Clean up and tear down -{ - for (int j = 0; j < NUMBER_OF_IMAGES; j++) { - if (pSegmentLabelsOutputBufferDev[j] != 0) - cudaFree(pSegmentLabelsOutputBufferDev[j]); - if (pSegmentationScratchBufferDev[j] != 0) - cudaFree(pSegmentationScratchBufferDev[j]); - if (pSegmentsDev[j] != 0) - cudaFree(pSegmentsDev[j]); - if (pInputImageDev[j] != 0) - cudaFree(pInputImageDev[j]); - if (pSegmentLabelsOutputBufferHost[j] != 0) - free(pSegmentLabelsOutputBufferHost[j]); - if (pSegmentsHost[j] != 0) - free(pSegmentsHost[j]); - if (pInputImageHost[j] != 0) - free(pInputImageHost[j]); - } -} - -const std::string &SegmentsOutputFile0 = "teapot_Segments_8Way_512x512_8u.raw"; -const std::string &SegmentsOutputFile1 = "CT_skull_Segments_8Way_512x512_8u.raw"; -const std::string &SegmentsOutputFile2 = "Rocks_Segments_8Way_512x512_8u.raw"; - -const std::string &SegmentBoundariesOutputFile0 = "teapot_SegmentBoundaries_8Way_512x512_8u.raw"; -const std::string &SegmentBoundariesOutputFile1 = "CT_skull_SegmentBoundaries_8Way_512x512_8u.raw"; -const std::string &SegmentBoundariesOutputFile2 = "Rocks_SegmentBoundaries_8Way_512x512_8u.raw"; - -const std::string &SegmentsWithContrastingBoundariesOutputFile0 = - "teapot_SegmentsWithContrastingBoundaries_8Way_512x512_8u.raw"; -const std::string &SegmentsWithContrastingBoundariesOutputFile1 = - "CT_skull_SegmentsWithContrastingBoundaries_8Way_512x512_8u.raw"; -const std::string &SegmentsWithContrastingBoundariesOutputFile2 = - "Rocks_SegmentsWithContrastingBoundaries_8Way_512x512_8u.raw"; - -const std::string &CompressedSegmentLabelsOutputFile0 = "teapot_CompressedSegmentLabels_8Way_512x512_32u.raw"; -const std::string &CompressedSegmentLabelsOutputFile1 = "CT_skull_CompressedSegmentLabels_8Way_512x512_32u.raw"; -const std::string &CompressedSegmentLabelsOutputFile2 = "Rocks_CompressedSegmentLabels_8Way_512x512_32u.raw"; - -int loadRaw8BitImage(Npp8u *pImage, int nWidth, int nHeight, int nImage) -{ - FILE *bmpFile; - size_t nSize; - - if (nImage == 0) { - if (nWidth != 512 || nHeight != 512) - return -1; - const char *fileName = "teapot_512x512_8u_Gray.raw"; - const char *InputFile = sdkFindFilePath(fileName, "."); - if (InputFile == NULL) { - printf("%s file not found.. exiting\n", fileName); - exit(EXIT_WAIVED); - } - - bmpFile = fopen(InputFile, "rb"); - } - else if (nImage == 1) { - if (nWidth != 512 || nHeight != 512) - return -1; - const char *fileName = "CT_skull_512x512_8u_Gray.raw"; - const char *InputFile = sdkFindFilePath(fileName, "."); - if (InputFile == NULL) { - printf("%s file not found.. exiting\n", fileName); - exit(EXIT_WAIVED); - } - - bmpFile = fopen(InputFile, "rb"); - } - else if (nImage == 2) { - if (nWidth != 512 || nHeight != 512) - return -1; - const char *fileName = "Rocks_512x512_8u_Gray.raw"; - const char *InputFile = sdkFindFilePath(fileName, "."); - if (InputFile == NULL) { - printf("%s file not found.. exiting\n", fileName); - exit(EXIT_WAIVED); - } - - bmpFile = fopen(InputFile, "rb"); - } - else { - printf("Input file load failed.\n"); - return -1; - } - - if (bmpFile == NULL) { - printf("Input file load failed.\n"); - return -1; - } - nSize = fread(pImage, 1, nWidth * nHeight, bmpFile); - if (nSize < nWidth * nHeight) { - printf("Input file load failed.\n"); - fclose(bmpFile); - return -1; - } - fclose(bmpFile); - - printf("Input file load succeeded.\n"); - - return 0; -} - -int main(int argc, char **argv) -{ - - size_t aSegmentationScratchBufferSize[NUMBER_OF_IMAGES]; - int aSegmentLabelsOutputBufferSize[NUMBER_OF_IMAGES]; - - cudaError_t cudaError; - NppStatus nppStatus; - NppStreamContext nppStreamCtx; - FILE *bmpFile; - NppiNorm eNorm = nppiNormInf; // default to 8 way neighbor search - - for (int j = 0; j < NUMBER_OF_IMAGES; j++) { - pInputImageDev[j] = 0; - pInputImageHost[j] = 0; - pSegmentationScratchBufferDev[j] = 0; - pSegmentLabelsOutputBufferDev[j] = 0; - pSegmentLabelsOutputBufferHost[j] = 0; - pSegmentsDev[j] = 0; - pSegmentsHost[j] = 0; - } - - nppStreamCtx.hStream = - 0; // The NULL stream by default, set this to whatever your stream ID is if not the NULL stream. - - cudaError = cudaGetDevice(&nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) { - printf("CUDA error: no devices supporting CUDA.\n"); - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - } - - const NppLibraryVersion *libVer = nppGetLibVersion(); - - printf("NPP Library Version %d.%d.%d\n", libVer->major, libVer->minor, libVer->build); - - int driverVersion, runtimeVersion; - cudaDriverGetVersion(&driverVersion); - cudaRuntimeGetVersion(&runtimeVersion); - - printf("CUDA Driver Version: %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10); - printf("CUDA Runtime Version: %d.%d\n\n", runtimeVersion / 1000, (runtimeVersion % 100) / 10); - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMajor, - cudaDevAttrComputeCapabilityMajor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaDeviceGetAttribute(&nppStreamCtx.nCudaDevAttrComputeCapabilityMinor, - cudaDevAttrComputeCapabilityMinor, - nppStreamCtx.nCudaDeviceId); - if (cudaError != cudaSuccess) - return NPP_NOT_SUFFICIENT_COMPUTE_CAPABILITY; - - cudaError = cudaStreamGetFlags(nppStreamCtx.hStream, &nppStreamCtx.nStreamFlags); - - cudaDeviceProp oDeviceProperties; - - cudaError = cudaGetDeviceProperties(&oDeviceProperties, nppStreamCtx.nCudaDeviceId); - - nppStreamCtx.nMultiProcessorCount = oDeviceProperties.multiProcessorCount; - nppStreamCtx.nMaxThreadsPerMultiProcessor = oDeviceProperties.maxThreadsPerMultiProcessor; - nppStreamCtx.nMaxThreadsPerBlock = oDeviceProperties.maxThreadsPerBlock; - nppStreamCtx.nSharedMemPerBlock = oDeviceProperties.sharedMemPerBlock; - - NppiSize oSizeROI[NUMBER_OF_IMAGES]; - - for (int nImage = 0; nImage < NUMBER_OF_IMAGES; nImage++) { - if (nImage == 0) { - oSizeROI[nImage].width = 512; - oSizeROI[nImage].height = 512; - } - else if (nImage == 1) { - oSizeROI[nImage].width = 512; - oSizeROI[nImage].height = 512; - } - else if (nImage == 2) { - oSizeROI[nImage].width = 512; - oSizeROI[nImage].height = 512; - } - - // cudaMallocPitch OR cudaMalloc can be used here, in this sample case width == pitch. - - cudaError = cudaMalloc((void **)&pInputImageDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u) * oSizeROI[nImage].height); - if (cudaError != cudaSuccess) - return NPP_MEMORY_ALLOCATION_ERR; - - cudaError = cudaMalloc((void **)&pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height); - if (cudaError != cudaSuccess) - return NPP_MEMORY_ALLOCATION_ERR; - - pInputImageHost[nImage] = - reinterpret_cast(malloc(oSizeROI[nImage].width * sizeof(Npp8u) * oSizeROI[nImage].height)); - pSegmentsHost[nImage] = - reinterpret_cast(malloc(oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height)); - - nppStatus = nppiSegmentWatershedGetBufferSize_8u_C1R(oSizeROI[nImage], &aSegmentationScratchBufferSize[nImage]); - - cudaError = cudaMalloc((void **)&pSegmentationScratchBufferDev[nImage], aSegmentationScratchBufferSize[nImage]); - if (cudaError != cudaSuccess) - return NPP_MEMORY_ALLOCATION_ERR; - - // Output label marker buffers are only needed if you want to same the generated segmentation labels, they ARE - // compatible with NPP UF generated labels. Requesting segmentation output may slightly decrease segmentation - // function performance. Regardless of the pitch of the segmentation image the segment labels output buffer - // will have a pitch of oSizeROI[nImage].width * sizeof(Npp32u). - - aSegmentLabelsOutputBufferSize[nImage] = oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height; - - cudaError = cudaMalloc((void **)&pSegmentLabelsOutputBufferDev[nImage], aSegmentLabelsOutputBufferSize[nImage]); - if (cudaError != cudaSuccess) - return NPP_MEMORY_ALLOCATION_ERR; - - pSegmentLabelsOutputBufferHost[nImage] = - reinterpret_cast(malloc(oSizeROI[nImage].width * sizeof(Npp32u) * oSizeROI[nImage].height)); - - if (loadRaw8BitImage( - pInputImageHost[nImage], oSizeROI[nImage].width * sizeof(Npp8u), oSizeROI[nImage].height, nImage) - == 0) { - cudaError = cudaMemcpy2DAsync(pInputImageDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pInputImageHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyHostToDevice, - nppStreamCtx.hStream); - - // Make a second copy of the unaltered input image since this function works in place and we want to reuse - // the input image multiple times. - cudaError = cudaMemcpy2DAsync(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pInputImageHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyHostToDevice, - nppStreamCtx.hStream); - - nppStatus = nppiSegmentWatershed_8u_C1IR_Ctx(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pSegmentLabelsOutputBufferDev[nImage], - oSizeROI[nImage].width * sizeof(Npp32u), - eNorm, - NPP_WATERSHED_SEGMENT_BOUNDARIES_NONE, - oSizeROI[nImage], - pSegmentationScratchBufferDev[nImage], - nppStreamCtx); - - if (nppStatus != NPP_SUCCESS) { - if (nImage == 0) - printf("Lena segments 8Way 512x512 8u failed.\n"); - else if (nImage == 1) - printf("CT skull segments 8Way 512x512 8u failed.\n"); - else if (nImage == 2) - printf("Rocks segments 8Way 512x512 8u failed.\n"); - tearDown(); - return -1; - } - - // Now compress the label markers output to make them easier to view. - int nCompressedLabelsScratchBufferSize; - Npp8u *pCompressedLabelsScratchBufferDev; - - nppStatus = nppiCompressMarkerLabelsGetBufferSize_32u_C1R(oSizeROI[nImage].width * oSizeROI[nImage].height, - &nCompressedLabelsScratchBufferSize); - if (nppStatus != NPP_NO_ERROR) - return nppStatus; - - cudaError = cudaMalloc((void **)&pCompressedLabelsScratchBufferDev, nCompressedLabelsScratchBufferSize); - if (cudaError != cudaSuccess) - return NPP_MEMORY_ALLOCATION_ERR; - - int nCompressedLabelCount = 0; - - nppStatus = nppiCompressMarkerLabelsUF_32u_C1IR_Ctx(pSegmentLabelsOutputBufferDev[nImage], - oSizeROI[nImage].width * sizeof(Npp32u), - oSizeROI[nImage], - oSizeROI[nImage].width * oSizeROI[nImage].height, - &nCompressedLabelCount, - pCompressedLabelsScratchBufferDev, - nppStreamCtx); - - if (nppStatus != NPP_SUCCESS) { - if (nImage == 0) - printf("teapot_CompressedLabelMarkersUF_8Way_512x512_32u failed.\n"); - else if (nImage == 1) - printf("CT_Skull_CompressedLabelMarkersUF_8Way_512x512_32u failed.\n"); - else if (nImage == 2) - printf("Rocks_CompressedLabelMarkersUF_8Way_512x512_32u failed.\n"); - tearDown(); - return -1; - } - - // Copy segmented image to host - cudaError = cudaMemcpy2DAsync(pSegmentsHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyDeviceToHost, - nppStreamCtx.hStream); - - // Copy segment labels image to host - cudaError = cudaMemcpy2DAsync(pSegmentLabelsOutputBufferHost[nImage], - oSizeROI[nImage].width * sizeof(Npp32u), - pSegmentLabelsOutputBufferDev[nImage], - oSizeROI[nImage].width * sizeof(Npp32u), - oSizeROI[nImage].width * sizeof(Npp32u), - oSizeROI[nImage].height, - cudaMemcpyDeviceToHost, - nppStreamCtx.hStream); - - // Wait host image read backs to complete, not necessary if no need to synchronize - if ((cudaError = cudaStreamSynchronize(nppStreamCtx.hStream)) != cudaSuccess) { - printf("Post segmentation cudaStreamSynchronize failed\n"); - tearDown(); - return -1; - } - - // Free single image scratch buffer - cudaFree(pCompressedLabelsScratchBufferDev); - - // Save default segments file. - if (nImage == 0) - bmpFile = fopen(SegmentsOutputFile0.c_str(), "wb"); - else if (nImage == 1) - bmpFile = fopen(SegmentsOutputFile1.c_str(), "wb"); - else if (nImage == 2) - bmpFile = fopen(SegmentsOutputFile2.c_str(), "wb"); - - if (bmpFile == NULL) - return -1; - size_t nSize = 0; - for (int j = 0; j < oSizeROI[nImage].height; j++) { - nSize += fwrite( - &pSegmentsHost[nImage][j * oSizeROI[nImage].width], sizeof(Npp8u), oSizeROI[nImage].width, bmpFile); - } - fclose(bmpFile); - - if (nImage == 0) - printf("teapot_Segments_8Way_512x512_8u succeeded.\n"); - else if (nImage == 1) - printf("CT_Skull_Segments_8Way_512x512_8u succeeded.\n"); - else if (nImage == 2) - printf("Rocks_Segments_8Way_512x512_8u succeeded.\n"); - - // Save segment labels file. - if (nImage == 0) - bmpFile = fopen(CompressedSegmentLabelsOutputFile0.c_str(), "wb"); - else if (nImage == 1) - bmpFile = fopen(CompressedSegmentLabelsOutputFile1.c_str(), "wb"); - else if (nImage == 2) - bmpFile = fopen(CompressedSegmentLabelsOutputFile2.c_str(), "wb"); - - if (bmpFile == NULL) - return -1; - nSize = 0; - for (int j = 0; j < oSizeROI[nImage].height; j++) { - nSize += fwrite(&pSegmentLabelsOutputBufferHost[nImage][j * oSizeROI[nImage].width], - sizeof(Npp32u), - oSizeROI[nImage].width, - bmpFile); - } - fclose(bmpFile); - - if (nImage == 0) - printf("teapot_CompressedSegmentLabels_8Way_512x512_32u succeeded.\n"); - else if (nImage == 1) - printf("CT_Skull_CompressedSegmentLabels_8Way_512x512_32u succeeded.\n"); - else if (nImage == 2) - printf("Rocks_CompressedSegmentLabels_8Way_512x512_32u succeeded.\n"); - - // Now generate a segment boundaries only output image - - // Make a second copy of the unaltered input image since this function works in place and we want to reuse - // the input image multiple times. - cudaError = cudaMemcpy2DAsync(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pInputImageHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyHostToDevice, - nppStreamCtx.hStream); - - // We already generated segment labels images to skip that this time - nppStatus = nppiSegmentWatershed_8u_C1IR_Ctx(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - 0, - 0, - eNorm, - NPP_WATERSHED_SEGMENT_BOUNDARIES_ONLY, - oSizeROI[nImage], - pSegmentationScratchBufferDev[nImage], - nppStreamCtx); - - if (nppStatus != NPP_SUCCESS) { - if (nImage == 0) - printf("Lena segment boundaries 8Way 512x512 8u failed.\n"); - else if (nImage == 1) - printf("CT skull segment boundaries 8Way 512x512 8u failed.\n"); - else if (nImage == 2) - printf("Rocks segment boundaries 8Way 512x512 8u failed.\n"); - tearDown(); - return -1; - } - - // Copy segment boundaries image to host - cudaError = cudaMemcpy2DAsync(pSegmentsHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyDeviceToHost, - nppStreamCtx.hStream); - - // Wait host image read backs to complete, not necessary if no need to synchronize - if ((cudaError = cudaStreamSynchronize(nppStreamCtx.hStream)) != cudaSuccess) { - printf("Post segmentation cudaStreamSynchronize failed\n"); - tearDown(); - return -1; - } - - if (nImage == 0) - bmpFile = fopen(SegmentBoundariesOutputFile0.c_str(), "wb"); - else if (nImage == 1) - bmpFile = fopen(SegmentBoundariesOutputFile1.c_str(), "wb"); - else if (nImage == 2) - bmpFile = fopen(SegmentBoundariesOutputFile2.c_str(), "wb"); - - if (bmpFile == NULL) - return -1; - nSize = 0; - for (int j = 0; j < oSizeROI[nImage].height; j++) { - nSize += fwrite( - &pSegmentsHost[nImage][j * oSizeROI[nImage].width], sizeof(Npp8u), oSizeROI[nImage].width, bmpFile); - } - fclose(bmpFile); - - if (nImage == 0) - printf("teapot_SegmentBoundaries_8Way_512x512_8u succeeded.\n"); - else if (nImage == 1) - printf("CT_Skull_SegmentBoundaries_8Way_512x512_8u succeeded.\n"); - else if (nImage == 2) - printf("Rocks_SegmentBoundaries_8Way_512x512_8u succeeded.\n"); - - // Now generate a segmented with contrasting boundaries output image - - // Make a second copy of the unaltered input image since this function works in place and we want to reuse - // the input image multiple times. - cudaError = cudaMemcpy2DAsync(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pInputImageHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyHostToDevice, - nppStreamCtx.hStream); - - // We already generated segment labels images to skip that this time - nppStatus = nppiSegmentWatershed_8u_C1IR_Ctx(pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - 0, - 0, - eNorm, - NPP_WATERSHED_SEGMENT_BOUNDARIES_CONTRAST, - oSizeROI[nImage], - pSegmentationScratchBufferDev[nImage], - nppStreamCtx); - - if (nppStatus != NPP_SUCCESS) { - if (nImage == 0) - printf("Lena segments with contrasting boundaries 8Way 512x512 8u failed.\n"); - else if (nImage == 1) - printf("CT skull segments with contrasting boundaries 8Way 512x512 8u failed.\n"); - else if (nImage == 2) - printf("Rocks segments with contrasting boundaries 8Way 512x512 8u failed.\n"); - tearDown(); - return -1; - } - - // Copy segment boundaries image to host - cudaError = cudaMemcpy2DAsync(pSegmentsHost[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - pSegmentsDev[nImage], - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].width * sizeof(Npp8u), - oSizeROI[nImage].height, - cudaMemcpyDeviceToHost, - nppStreamCtx.hStream); - - // Wait host image read backs to complete, not necessary if no need to synchronize - if ((cudaError = cudaStreamSynchronize(nppStreamCtx.hStream)) != cudaSuccess) { - printf("Post segmentation cudaStreamSynchronize failed\n"); - tearDown(); - return -1; - } - - if (nImage == 0) - bmpFile = fopen(SegmentsWithContrastingBoundariesOutputFile0.c_str(), "wb"); - else if (nImage == 1) - bmpFile = fopen(SegmentsWithContrastingBoundariesOutputFile1.c_str(), "wb"); - else if (nImage == 2) - bmpFile = fopen(SegmentsWithContrastingBoundariesOutputFile2.c_str(), "wb"); - - if (bmpFile == NULL) - return -1; - nSize = 0; - for (int j = 0; j < oSizeROI[nImage].height; j++) { - nSize += fwrite( - &pSegmentsHost[nImage][j * oSizeROI[nImage].width], sizeof(Npp8u), oSizeROI[nImage].width, bmpFile); - } - fclose(bmpFile); - - if (nImage == 0) - printf("teapot_SegmentsWithContrastingBoundaries_8Way_512x512_8u succeeded.\n"); - else if (nImage == 1) - printf("CT_Skull_SegmentsWithContrastingBoundaries_8Way_512x512_8u succeeded.\n"); - else if (nImage == 2) - printf("Rocks_SegmentsWithContrastingBoundaries_8Way_512x512_8u succeeded.\n"); - } - } - - tearDown(); - - return 0; -} diff --git a/cpp/5_Domain_Specific/BlackScholes/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/BlackScholes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/BlackScholes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/BlackScholes/.vscode/extensions.json b/cpp/5_Domain_Specific/BlackScholes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/BlackScholes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/BlackScholes/CMakeLists.txt b/cpp/5_Domain_Specific/BlackScholes/CMakeLists.txt index 01329a83..acce64db 100644 --- a/cpp/5_Domain_Specific/BlackScholes/CMakeLists.txt +++ b/cpp/5_Domain_Specific/BlackScholes/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(BlackScholes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for BlackScholes add_executable(BlackScholes BlackScholes.cu BlackScholes_gold.cpp) target_compile_options(BlackScholes PRIVATE $<$:--extended-lambda>) target_compile_features(BlackScholes PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(BlackScholes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/extensions.json b/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes.cpp b/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes.cpp deleted file mode 100644 index bd538c87..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes.cpp +++ /dev/null @@ -1,281 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample evaluates fair call and put prices for a - * given set of European options by Black-Scholes formula. - * See supplied whitepaper for more explanations. - */ - -#include -#include // helper functions for string parsing -#include - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of optN options on CPU -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void BlackScholesCPU(float *h_CallResult, - float *h_PutResult, - float *h_StockPrice, - float *h_OptionStrike, - float *h_OptionYears, - float Riskfree, - float Volatility, - int optN); - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of OptN options on GPU -//////////////////////////////////////////////////////////////////////////////// -//////////////////////////////////////////////////////////////////////////////// - -// Helper function, returning uniformly distributed -// random float in [low, high] range -//////////////////////////////////////////////////////////////////////////////// - -float RandFloat(float low, float high) -{ - float t = (float)rand() / (float)RAND_MAX; - return (1.0f - t) * low + t * high; -} - -//////////////////////////////////////////////////////////////////////////////// -// Data configuration -//////////////////////////////////////////////////////////////////////////////// - -const int OPT_N = 4000000; -const int NUM_ITERATIONS = 512; -const int OPT_SZ = OPT_N * sizeof(float); -const float RISKFREE = 0.02f; -const float VOLATILITY = 0.30f; - -#define DIV_UP(a, b) (((a) + (b) - 1) / (b)) - -//////////////////////////////////////////////////////////////////////////////// -// Main program -//////////////////////////////////////////////////////////////////////////////// - -int main(int argc, char **argv) -{ - // Start logs - printf("[%s] - Starting...\n", argv[0]); - - //'h_' prefix - CPU (host) memory space - float - // Results calculated by CPU for reference - *h_CallResultCPU, - *h_PutResultCPU, - // CPU copy of GPU results - *h_CallResultGPU, *h_PutResultGPU, - // CPU instance of input data - *h_StockPrice, *h_OptionStrike, *h_OptionYears; - - //'d_' prefix - GPU (device) memory space - CUdeviceptr - // Results calculated by GPU - d_CallResult, - d_PutResult, - - // GPU instance of input data - d_StockPrice, d_OptionStrike, d_OptionYears; - - double delta, ref, sum_delta, sum_ref, max_delta, L1norm, gpuTime; - - StopWatchInterface *hTimer = NULL; - int i; - - sdkCreateTimer(&hTimer); - - printf("Initializing data...\n"); - printf("...allocating CPU memory for options.\n"); - - h_CallResultCPU = (float *)malloc(OPT_SZ); - h_PutResultCPU = (float *)malloc(OPT_SZ); - h_CallResultGPU = (float *)malloc(OPT_SZ); - h_PutResultGPU = (float *)malloc(OPT_SZ); - h_StockPrice = (float *)malloc(OPT_SZ); - h_OptionStrike = (float *)malloc(OPT_SZ); - h_OptionYears = (float *)malloc(OPT_SZ); - - char *cubin, *kernel_file; - size_t cubinSize; - kernel_file = sdkFindFilePath("BlackScholes_kernel.cuh", argv[0]); - - // Compile the kernel BlackScholes_kernel. - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - CUmodule module = loadCUBIN(cubin, argc, argv); - - CUfunction kernel_addr; - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "BlackScholesGPU")); - - printf("...allocating GPU memory for options.\n"); - checkCudaErrors(cuMemAlloc(&d_CallResult, OPT_SZ)); - checkCudaErrors(cuMemAlloc(&d_PutResult, OPT_SZ)); - checkCudaErrors(cuMemAlloc(&d_StockPrice, OPT_SZ)); - checkCudaErrors(cuMemAlloc(&d_OptionStrike, OPT_SZ)); - checkCudaErrors(cuMemAlloc(&d_OptionYears, OPT_SZ)); - - printf("...generating input data in CPU mem.\n"); - srand(5347); - - // Generate options set - for (i = 0; i < OPT_N; i++) { - h_CallResultCPU[i] = 0.0f; - h_PutResultCPU[i] = -1.0f; - h_StockPrice[i] = RandFloat(5.0f, 30.0f); - h_OptionStrike[i] = RandFloat(1.0f, 100.0f); - h_OptionYears[i] = RandFloat(0.25f, 10.0f); - } - - printf("...copying input data to GPU mem.\n"); - // Copy options data to GPU memory for further processing - checkCudaErrors(cuMemcpyHtoD(d_StockPrice, h_StockPrice, OPT_SZ)); - checkCudaErrors(cuMemcpyHtoD(d_OptionStrike, h_OptionStrike, OPT_SZ)); - checkCudaErrors(cuMemcpyHtoD(d_OptionYears, h_OptionYears, OPT_SZ)); - - printf("Data init done.\n\n"); - printf("Executing Black-Scholes GPU kernel (%i iterations)...\n", NUM_ITERATIONS); - - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - - dim3 cudaBlockSize(128, 1, 1); - dim3 cudaGridSize(DIV_UP(OPT_N / 2, 128), 1, 1); - - float risk = RISKFREE; - float volatility = VOLATILITY; - int optval = OPT_N; - - void *arr[] = {(void *)&d_CallResult, - (void *)&d_PutResult, - (void *)&d_StockPrice, - (void *)&d_OptionStrike, - (void *)&d_OptionYears, - (void *)&risk, - (void *)&volatility, - (void *)&optval}; - - for (i = 0; i < NUM_ITERATIONS; i++) { - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - cudaBlockSize.x, - cudaBlockSize.y, - cudaBlockSize.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &arr[0], /* arguments */ - 0)); - } - - checkCudaErrors(cuCtxSynchronize()); - - sdkStopTimer(&hTimer); - gpuTime = sdkGetTimerValue(&hTimer) / NUM_ITERATIONS; - - // Both call and put is calculated - printf("Options count : %i \n", 2 * OPT_N); - printf("BlackScholesGPU() time : %f msec\n", gpuTime); - printf("Effective memory bandwidth: %f GB/s\n", ((double)(5 * OPT_N * sizeof(float)) * 1E-9) / (gpuTime * 1E-3)); - printf("Gigaoptions per second : %f \n\n", ((double)(2 * OPT_N) * 1E-9) / (gpuTime * 1E-3)); - printf("BlackScholes, Throughput = %.4f GOptions/s, Time = %.5f s, Size = %u " - "options, NumDevsUsed = %u, Workgroup = %u\n", - (((double)(2.0 * OPT_N) * 1.0E-9) / (gpuTime * 1.0E-3)), - gpuTime * 1e-3, - (2 * OPT_N), - 1, - 128); - - printf("\nReading back GPU results...\n"); - - // Read back GPU results to compare them to CPU results - checkCudaErrors(cuMemcpyDtoH(h_CallResultGPU, d_CallResult, OPT_SZ)); - checkCudaErrors(cuMemcpyDtoH(h_PutResultGPU, d_PutResult, OPT_SZ)); - - printf("Checking the results...\n"); - printf("...running CPU calculations.\n\n"); - - // Calculate options values on CPU - BlackScholesCPU( - h_CallResultCPU, h_PutResultCPU, h_StockPrice, h_OptionStrike, h_OptionYears, RISKFREE, VOLATILITY, OPT_N); - - printf("Comparing the results...\n"); - // Calculate max absolute difference and L1 distance - // between CPU and GPU results - sum_delta = 0; - sum_ref = 0; - max_delta = 0; - - for (i = 0; i < OPT_N; i++) { - ref = h_CallResultCPU[i]; - delta = fabs(h_CallResultCPU[i] - h_CallResultGPU[i]); - - if (delta > max_delta) { - max_delta = delta; - } - - sum_delta += delta; - sum_ref += fabs(ref); - } - - L1norm = sum_delta / sum_ref; - printf("L1 norm: %E\n", L1norm); - printf("Max absolute error: %E\n\n", max_delta); - - printf("Shutting down...\n"); - printf("...releasing GPU memory.\n"); - - checkCudaErrors(cuMemFree(d_OptionYears)); - checkCudaErrors(cuMemFree(d_OptionStrike)); - checkCudaErrors(cuMemFree(d_StockPrice)); - checkCudaErrors(cuMemFree(d_PutResult)); - checkCudaErrors(cuMemFree(d_CallResult)); - - printf("...releasing CPU memory.\n"); - - free(h_OptionYears); - free(h_OptionStrike); - free(h_StockPrice); - free(h_PutResultGPU); - free(h_CallResultGPU); - free(h_PutResultCPU); - free(h_CallResultCPU); - - sdkDeleteTimer(&hTimer); - printf("Shutdown done.\n"); - - printf("\n[%s] - Test Summary\n", argv[0]); - - if (L1norm > 1e-6) { - printf("Test failed!\n"); - exit(EXIT_FAILURE); - } - - printf("Test passed\n"); - exit(EXIT_SUCCESS); -} diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_gold.cpp b/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_gold.cpp deleted file mode 100644 index ea76cabe..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_gold.cpp +++ /dev/null @@ -1,100 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include - -/////////////////////////////////////////////////////////////////////////////// -// Polynomial approximation of cumulative normal distribution function -/////////////////////////////////////////////////////////////////////////////// - -static double CND(double d) -{ - const double A1 = 0.31938153; - const double A2 = -0.356563782; - const double A3 = 1.781477937; - const double A4 = -1.821255978; - const double A5 = 1.330274429; - const double RSQRT2PI = 0.39894228040143267793994605993438; - - double K = 1.0 / (1.0 + 0.2316419 * fabs(d)); - - double cnd = RSQRT2PI * exp(-0.5 * d * d) * (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))); - - if (d > 0) - cnd = 1.0 - cnd; - - return cnd; -} - -/////////////////////////////////////////////////////////////////////////////// -// Black-Scholes formula for both call and put -/////////////////////////////////////////////////////////////////////////////// -static void BlackScholesBodyCPU(float &callResult, - float &putResult, - float Sf, // Stock price - float Xf, // Option strike - float Tf, // Option years - float Rf, // Riskless rate - float Vf // Volatility rate -) -{ - double S = Sf, X = Xf, T = Tf, R = Rf, V = Vf; - double sqrtT = sqrt(T); - double d1 = (log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT); - double d2 = d1 - V * sqrtT; - double CNDD1 = CND(d1); - double CNDD2 = CND(d2); - - // Calculate Call and Put simultaneously - double expRT = exp(-R * T); - - callResult = (float)(S * CNDD1 - X * expRT * CNDD2); - putResult = (float)(X * expRT * (1.0 - CNDD2) - S * (1.0 - CNDD1)); -} - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of optN options -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void BlackScholesCPU(float *h_CallResult, - float *h_PutResult, - float *h_StockPrice, - float *h_OptionStrike, - float *h_OptionYears, - float Riskfree, - float Volatility, - int optN) -{ - for (int opt = 0; opt < optN; opt++) - BlackScholesBodyCPU(h_CallResult[opt], - h_PutResult[opt], - h_StockPrice[opt], - h_OptionStrike[opt], - h_OptionYears[opt], - Riskfree, - Volatility); -} diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_kernel.cuh b/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_kernel.cuh deleted file mode 100644 index 8d67b7d2..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/BlackScholes_kernel.cuh +++ /dev/null @@ -1,116 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/////////////////////////////////////////////////////////////////////////////// -// Polynomial approximation of cumulative normal distribution function -/////////////////////////////////////////////////////////////////////////////// - -__device__ inline float cndGPU(float d) -{ - const float A1 = 0.31938153f; - const float A2 = -0.356563782f; - const float A3 = 1.781477937f; - const float A4 = -1.821255978f; - const float A5 = 1.330274429f; - const float RSQRT2PI = 0.39894228040143267793994605993438f; - - float K = __fdividef(1.0f, (1.0f + 0.2316419f * fabsf(d))); - - float cnd = RSQRT2PI * __expf(-0.5f * d * d) * (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))); - - if (d > 0) - cnd = 1.0f - cnd; - - return cnd; -} - -/////////////////////////////////////////////////////////////////////////////// -// Black-Scholes formula for both call and put -/////////////////////////////////////////////////////////////////////////////// -__device__ inline void BlackScholesBodyGPU(float &CallResult, - float &PutResult, - float S, // Stock price - float X, // Option strike - float T, // Option years - float R, // Riskless rate - float V // Volatility rate -) -{ - float sqrtT, expRT; - float d1, d2, CNDD1, CNDD2; - - sqrtT = __fdividef(1.0F, rsqrtf(T)); - d1 = __fdividef(__logf(S / X) + (R + 0.5f * V * V) * T, V * sqrtT); - d2 = d1 - V * sqrtT; - - CNDD1 = cndGPU(d1); - CNDD2 = cndGPU(d2); - - // Calculate Call and Put simultaneously - expRT = __expf(-R * T); - CallResult = S * CNDD1 - X * expRT * CNDD2; - PutResult = X * expRT * (1.0f - CNDD2) - S * (1.0f - CNDD1); -} - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of optN options on GPU -//////////////////////////////////////////////////////////////////////////////// -extern "C" __launch_bounds__(128) __global__ void BlackScholesGPU(float2 *__restrict d_CallResult, - float2 *__restrict d_PutResult, - float2 *__restrict d_StockPrice, - float2 *__restrict d_OptionStrike, - float2 *__restrict d_OptionYears, - float Riskfree, - float Volatility, - int optN) -{ - ////Thread index - const int opt = blockDim.x * blockIdx.x + threadIdx.x; - - // Calculating 2 options per thread to increase ILP (instruction level - // parallelism) - if (opt < (optN / 2)) { - float callResult1, callResult2; - float putResult1, putResult2; - BlackScholesBodyGPU(callResult1, - putResult1, - d_StockPrice[opt].x, - d_OptionStrike[opt].x, - d_OptionYears[opt].x, - Riskfree, - Volatility); - BlackScholesBodyGPU(callResult2, - putResult2, - d_StockPrice[opt].y, - d_OptionStrike[opt].y, - d_OptionYears[opt].y, - Riskfree, - Volatility); - d_CallResult[opt] = make_float2(callResult1, callResult2); - d_PutResult[opt] = make_float2(putResult1, putResult2); - } -} diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/CMakeLists.txt b/cpp/5_Domain_Specific/BlackScholes_nvrtc/CMakeLists.txt deleted file mode 100644 index eae3d403..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,45 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(BlackScholes_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for BlackScholes_nvrtc -add_executable(BlackScholes_nvrtc BlackScholes.cpp BlackScholes_gold.cpp) - -target_compile_options(BlackScholes_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(BlackScholes_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(BlackScholes_nvrtc PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(BlackScholes_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy kernel to the output directory -add_custom_command(TARGET BlackScholes_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/BlackScholes_kernel.cuh ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/BlackScholes_nvrtc/README.md b/cpp/5_Domain_Specific/BlackScholes_nvrtc/README.md deleted file mode 100644 index e719e0bc..00000000 --- a/cpp/5_Domain_Specific/BlackScholes_nvrtc/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# BlackScholes_nvrtc - Black-Scholes Option Pricing with libNVRTC - -## Description - -This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula, compiling the CUDA kernels involved at runtime using NVRTC. - -## Key Concepts - -Computational Finance, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuMemcpyHtoD, cuCtxSynchronize, cuMemAlloc, cuMemFree, cuModuleGetFunction - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaBlockSize, cudaGridSize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/5_Domain_Specific/CMakeLists.txt b/cpp/5_Domain_Specific/CMakeLists.txt index 9ded8e5a..730c6eaf 100644 --- a/cpp/5_Domain_Specific/CMakeLists.txt +++ b/cpp/5_Domain_Specific/CMakeLists.txt @@ -1,5 +1,4 @@ add_subdirectory(BlackScholes) -add_subdirectory(BlackScholes_nvrtc) add_subdirectory(FDTD3d) add_subdirectory(HSOpticalFlow) add_subdirectory(Mandelbrot) @@ -10,7 +9,6 @@ add_subdirectory(SobolQRNG) add_subdirectory(bicubicTexture) add_subdirectory(bilateralFilter) add_subdirectory(binomialOptions) -add_subdirectory(binomialOptions_nvrtc) add_subdirectory(convolutionFFT2D) add_subdirectory(dwtHaar1D) add_subdirectory(dxtc) @@ -18,10 +16,8 @@ add_subdirectory(fastWalshTransform) add_subdirectory(fluidsGL) add_subdirectory(marchingCubes) add_subdirectory(nbody) -add_subdirectory(p2pBandwidthLatencyTest) add_subdirectory(postProcessGL) add_subdirectory(quasirandomGenerator) -add_subdirectory(quasirandomGenerator_nvrtc) add_subdirectory(recursiveGaussian) add_subdirectory(simpleD3D11) add_subdirectory(simpleD3D11Texture) diff --git a/cpp/5_Domain_Specific/FDTD3d/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/FDTD3d/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/FDTD3d/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/FDTD3d/.vscode/extensions.json b/cpp/5_Domain_Specific/FDTD3d/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/FDTD3d/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/FDTD3d/CMakeLists.txt b/cpp/5_Domain_Specific/FDTD3d/CMakeLists.txt index 1a1d50b2..90357df0 100644 --- a/cpp/5_Domain_Specific/FDTD3d/CMakeLists.txt +++ b/cpp/5_Domain_Specific/FDTD3d/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(FDTD3d LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for FDTD3d add_executable(FDTD3d src/FDTD3d.cpp src/FDTD3dGPU.cu src/FDTD3dReference.cpp) target_compile_options(FDTD3d PRIVATE $<$:--extended-lambda>) target_compile_features(FDTD3d PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(FDTD3d PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(FDTD3d PRIVATE inc ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/extensions.json b/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/HSOpticalFlow/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/HSOpticalFlow/CMakeLists.txt b/cpp/5_Domain_Specific/HSOpticalFlow/CMakeLists.txt index b80b2466..acf9d5c1 100644 --- a/cpp/5_Domain_Specific/HSOpticalFlow/CMakeLists.txt +++ b/cpp/5_Domain_Specific/HSOpticalFlow/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(HSOpticalFlow LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for HSOpticalFlow add_executable(HSOpticalFlow flowCUDA.cu flowGold.cpp main.cpp) target_compile_options(HSOpticalFlow PRIVATE $<$:--extended-lambda>) target_compile_features(HSOpticalFlow PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(HSOpticalFlow PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(HSOpticalFlow PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -39,6 +31,5 @@ add_custom_command(TARGET HSOpticalFlow POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/data ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/Mandelbrot/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/Mandelbrot/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/Mandelbrot/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/Mandelbrot/.vscode/extensions.json b/cpp/5_Domain_Specific/Mandelbrot/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/Mandelbrot/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/Mandelbrot/CMakeLists.txt b/cpp/5_Domain_Specific/Mandelbrot/CMakeLists.txt index 4bfcc926..1039cd93 100644 --- a/cpp/5_Domain_Specific/Mandelbrot/CMakeLists.txt +++ b/cpp/5_Domain_Specific/Mandelbrot/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(Mandelbrot LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for Mandelbrot add_executable(Mandelbrot Mandelbrot_cuda.cu Mandelbrot_gold.cpp Mandelbrot.cpp) - -target_compile_options(Mandelbrot PRIVATE $<$:--extended-lambda>) - -target_compile_features(Mandelbrot PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(Mandelbrot PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(Mandelbrot PRIVATE $<$:--extended-lambda>) + target_compile_features(Mandelbrot PRIVATE cxx_std_17 cuda_std_17) target_include_directories(Mandelbrot PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(Mandelbrot ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to the output directory add_custom_command(TARGET Mandelbrot POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(Mandelbrot ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET Mandelbrot POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET Mandelbrot POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ target_compile_features(Mandelbrot PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'Mandelbrot'") endif() else() message(STATUS "OpenGL not found - will not build sample 'Mandelbrot'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/extensions.json b/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/MonteCarloMultiGPU/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/MonteCarloMultiGPU/CMakeLists.txt b/cpp/5_Domain_Specific/MonteCarloMultiGPU/CMakeLists.txt index 1c8522c7..73c94489 100644 --- a/cpp/5_Domain_Specific/MonteCarloMultiGPU/CMakeLists.txt +++ b/cpp/5_Domain_Specific/MonteCarloMultiGPU/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(MonteCarloMultiGPU LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for MonteCarloMultiGPU add_executable(MonteCarloMultiGPU MonteCarlo_gold.cpp MonteCarlo_kernel.cu multithreading.cpp MonteCarloMultiGPU.cpp) target_compile_options(MonteCarloMultiGPU PRIVATE $<$:--extended-lambda>) target_compile_features(MonteCarloMultiGPU PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(MonteCarloMultiGPU PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(MonteCarloMultiGPU PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -37,6 +29,5 @@ target_link_libraries(MonteCarloMultiGPU PRIVATE CUDA::curand ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/extensions.json b/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/NV12toBGRandResize/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/NV12toBGRandResize/CMakeLists.txt b/cpp/5_Domain_Specific/NV12toBGRandResize/CMakeLists.txt index 765a82a6..b2dd44bb 100644 --- a/cpp/5_Domain_Specific/NV12toBGRandResize/CMakeLists.txt +++ b/cpp/5_Domain_Specific/NV12toBGRandResize/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(NV12toBGRandResize LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for NV12toBGRandResize add_executable(NV12toBGRandResize bgr_resize.cu nv12_resize.cu nv12_to_bgr_planar.cu resize_convert_main.cpp utils.cu) target_compile_options(NV12toBGRandResize PRIVATE $<$:--extended-lambda>) target_compile_features(NV12toBGRandResize PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(NV12toBGRandResize PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(NV12toBGRandResize PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -39,6 +31,5 @@ add_custom_command(TARGET NV12toBGRandResize POST_BUILD ${CMAKE_CURRENT_BINARY_DIR}/data ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/README.md b/cpp/5_Domain_Specific/README.md index b7809e86..6d90fa77 100644 --- a/cpp/5_Domain_Specific/README.md +++ b/cpp/5_Domain_Specific/README.md @@ -10,15 +10,9 @@ Bilateral filter is an edge-preserving non-linear smoothing filter that is imple ### [binomialOptions](./binomialOptions) This sample evaluates fair call price for a given set of European options under binomial model. -### [binomialOptions_nvrtc](./binomialOptions_nvrtc) -This sample evaluates fair call price for a given set of European options under binomial model. This sample makes use of NVRTC for Runtime Compilation. - ### [BlackScholes](./BlackScholes) This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula. -### [BlackScholes_nvrtc](./BlackScholes_nvrtc) -This sample evaluates fair call and put prices for a given set of European options by Black-Scholes formula, compiling the CUDA kernels involved at runtime using NVRTC. - ### [convolutionFFT2D](./convolutionFFT2D) This sample demonstrates how 2D convolutions with very large kernel sizes can be efficiently implemented using FFT transformations. @@ -59,18 +53,12 @@ This sample demonstrates efficient all-pairs simulation of a gravitational n-bod ### [NV12toBGRandResize](./NV12toBGRandResize) This code shows two ways to convert and resize NV12 frames to BGR 3 planars frames using CUDA in batch. Way-1, Convert NV12 Input to BGR @ Input Resolution-1, then Resize to Resolution#2. Way-2, resize NV12 Input to Resolution#2 then convert it to BGR Output. NVIDIA HW Decoder, both dGPU and Tegra, normally outputs NV12 pitch format frames. For the inference using TensorRT, the input frame needs to be BGR planar format with possibly different size. So, conversion and resizing from NV12 to BGR planar is usually required for the inference following decoding. This CUDA code provides a reference implementation for conversion and resizing. -### [p2pBandwidthLatencyTest](./p2pBandwidthLatencyTest) -This application demonstrates the CUDA Peer-To-Peer (P2P) data transfers between pairs of GPUs and computes latency and bandwidth. Tests on GPU pairs using P2P and without P2P are tested. - ### [postProcessGL](./postProcessGL) This sample shows how to post-process an image rendered in OpenGL using CUDA. ### [quasirandomGenerator](./quasirandomGenerator) This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions. -### [quasirandomGenerator_nvrtc](./quasirandomGenerator_nvrtc) -This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions, compiling the CUDA kernels involved at runtime using NVRTC. - ### [recursiveGaussian](./recursiveGaussian) This sample implements a Gaussian blur using Deriche's recursive method. The advantage of this method is that the execution time is independent of the filter width. diff --git a/cpp/5_Domain_Specific/SobelFilter/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/SobelFilter/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/SobelFilter/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/SobelFilter/.vscode/extensions.json b/cpp/5_Domain_Specific/SobelFilter/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/SobelFilter/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/SobelFilter/CMakeLists.txt b/cpp/5_Domain_Specific/SobelFilter/CMakeLists.txt index 31d1694f..bc8ba5e1 100644 --- a/cpp/5_Domain_Specific/SobelFilter/CMakeLists.txt +++ b/cpp/5_Domain_Specific/SobelFilter/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(SobelFilter LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for SobelFilter add_executable(SobelFilter SobelFilter.cpp SobelFilter_kernels.cu) - target_compile_options(SobelFilter PRIVATE $<$:--extended-lambda>) - target_compile_features(SobelFilter PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(SobelFilter PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(SobelFilter PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(SobelFilter ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET SobelFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(SobelFilter ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET SobelFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET SobelFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'SobelFilter'") endif() else() message(STATUS "OpenGL not found - will not build sample 'SobelFilter'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/SobolQRNG/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/SobolQRNG/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/SobolQRNG/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/SobolQRNG/.vscode/extensions.json b/cpp/5_Domain_Specific/SobolQRNG/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/SobolQRNG/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/SobolQRNG/CMakeLists.txt b/cpp/5_Domain_Specific/SobolQRNG/CMakeLists.txt index 341c4f2d..de01c2da 100644 --- a/cpp/5_Domain_Specific/SobolQRNG/CMakeLists.txt +++ b/cpp/5_Domain_Specific/SobolQRNG/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(SobolQRNG LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for SobolQRNG add_executable(SobolQRNG sobol_gold.cpp sobol_gpu.cu sobol_primitives.cpp sobol.cpp) target_compile_options(SobolQRNG PRIVATE $<$:--extended-lambda>) target_compile_features(SobolQRNG PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(SobolQRNG PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(SobolQRNG PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/bicubicTexture/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/bicubicTexture/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/bicubicTexture/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/bicubicTexture/.vscode/extensions.json b/cpp/5_Domain_Specific/bicubicTexture/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/bicubicTexture/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/bicubicTexture/CMakeLists.txt b/cpp/5_Domain_Specific/bicubicTexture/CMakeLists.txt index 4e4f375b..020e6ceb 100644 --- a/cpp/5_Domain_Specific/bicubicTexture/CMakeLists.txt +++ b/cpp/5_Domain_Specific/bicubicTexture/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(bicubicTexture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for bicubicTexture add_executable(bicubicTexture bicubicTexture.cpp bicubicTexture_cuda.cu) - - target_compile_options(bicubicTexture PRIVATE $<$:--extended-lambda>) - + target_compile_options(bicubicTexture PRIVATE $<$:--extended-lambda;--expt-relaxed-constexpr>) target_compile_features(bicubicTexture PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(bicubicTexture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(bicubicTexture PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(bicubicTexture ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to the output directory add_custom_command(TARGET bicubicTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(bicubicTexture ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET bicubicTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET bicubicTexture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,14 +67,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'bicubicTexture'") endif() else() message(STATUS "OpenGL not found - will not build sample 'bicubicTexture'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/bilateralFilter/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/bilateralFilter/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/bilateralFilter/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/bilateralFilter/.vscode/extensions.json b/cpp/5_Domain_Specific/bilateralFilter/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/bilateralFilter/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/bilateralFilter/CMakeLists.txt b/cpp/5_Domain_Specific/bilateralFilter/CMakeLists.txt index 94a8b1ca..e56f9a6a 100644 --- a/cpp/5_Domain_Specific/bilateralFilter/CMakeLists.txt +++ b/cpp/5_Domain_Specific/bilateralFilter/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(bilateralFilter LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,49 +29,37 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for bilateralFilter add_executable(bilateralFilter bilateralFilter.cpp bilateralFilter_cpu.cpp bilateral_kernel.cu bmploader.cpp) - -target_compile_options(bilateralFilter PRIVATE $<$:--extended-lambda>) - -target_compile_features(bilateralFilter PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(bilateralFilter PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(bilateralFilter PRIVATE $<$:--extended-lambda>) + target_compile_features(bilateralFilter PRIVATE cxx_std_17 cuda_std_17) target_include_directories(bilateralFilter PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(bilateralFilter ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to the output directory add_custom_command(TARGET bilateralFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(bilateralFilter ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET bilateralFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET bilateralFilter POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -83,15 +67,11 @@ target_compile_features(bilateralFilter PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'bilateralFilter'") endif() else() message(STATUS "OpenGL not found - will not build sample 'bilateralFilter'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/binomialOptions/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/binomialOptions/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/binomialOptions/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/binomialOptions/.vscode/extensions.json b/cpp/5_Domain_Specific/binomialOptions/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/binomialOptions/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/binomialOptions/CMakeLists.txt b/cpp/5_Domain_Specific/binomialOptions/CMakeLists.txt index 63eaf820..f5640f6f 100644 --- a/cpp/5_Domain_Specific/binomialOptions/CMakeLists.txt +++ b/cpp/5_Domain_Specific/binomialOptions/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(binomialOptions LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for binomialOptions add_executable(binomialOptions binomialOptions.cpp binomialOptions_gold.cpp binomialOptions_kernel.cu) target_compile_options(binomialOptions PRIVATE $<$:--extended-lambda>) target_compile_features(binomialOptions PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(binomialOptions PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(binomialOptions PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/extensions.json b/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/CMakeLists.txt b/cpp/5_Domain_Specific/binomialOptions_nvrtc/CMakeLists.txt deleted file mode 100644 index dcde9f41..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,63 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(binomialOptions_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for binomialOptions_nvrtc -add_executable(binomialOptions_nvrtc binomialOptions.cpp binomialOptions_gold.cpp binomialOptions_gpu.cpp) - -target_compile_options(binomialOptions_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(binomialOptions_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(binomialOptions_nvrtc PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(binomialOptions_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy kernel to the output directory -add_custom_command(TARGET binomialOptions_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/binomialOptions_kernel.cu ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy header to the output directory -add_custom_command(TARGET binomialOptions_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/common_gpu_header.h ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy header to the output directory -add_custom_command(TARGET binomialOptions_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/binomialOptions_common.h ${CMAKE_CURRENT_BINARY_DIR} -) - -# Copy header to the output directory -add_custom_command(TARGET binomialOptions_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/realtype.h ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/README.md b/cpp/5_Domain_Specific/binomialOptions_nvrtc/README.md deleted file mode 100644 index 5e1ad8f4..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/README.md +++ /dev/null @@ -1,39 +0,0 @@ -# binomialOptions_nvrtc - Binomial Option Pricing with libNVRTC - -## Description - -This sample evaluates fair call price for a given set of European options under binomial model. This sample makes use of NVRTC for Runtime Compilation. - -## Key Concepts - -Computational Finance, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuLaunchKernel, cuMemcpyHtoD, cuModuleGetGlobal, cuCtxSynchronize, cuModuleGetFunction - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaBlockSize, cudaGridSize - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions.cpp b/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions.cpp deleted file mode 100644 index 6567e0e6..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions.cpp +++ /dev/null @@ -1,198 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -/* - * This sample evaluates fair call price for a - * given set of European options under binomial model. - * See supplied whitepaper for more explanations. - */ - -#include -#include -#include -#include -#include -#include -#include - -#include "binomialOptions_common.h" -#include "realtype.h" - -//////////////////////////////////////////////////////////////////////////////// -// Black-Scholes formula for binomial tree results validation -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void BlackScholesCall(real &callResult, TOptionData optionData); - -//////////////////////////////////////////////////////////////////////////////// -// Process single option on CPU -// Note that CPU code is for correctness testing only and not for benchmarking. -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void binomialOptionsCPU(real &callResult, TOptionData optionData); - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of OptN options on GPU -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void binomialOptionsGPU(real *callValue, TOptionData *optionData, int optN, int argc, char **argv); - -//////////////////////////////////////////////////////////////////////////////// -// Helper function, returning uniformly distributed -// random float in [low, high] range -//////////////////////////////////////////////////////////////////////////////// - -real randData(real low, real high) -{ - real t = (real)rand() / (real)RAND_MAX; - return ((real)1.0 - t) * low + t * high; -} - -//////////////////////////////////////////////////////////////////////////////// -// Main program -//////////////////////////////////////////////////////////////////////////////// - -int main(int argc, char **argv) -{ - printf("[%s] - Starting...\n", argv[0]); - - const int OPT_N = MAX_OPTIONS; - - TOptionData optionData[MAX_OPTIONS]; - real callValueBS[MAX_OPTIONS], callValueGPU[MAX_OPTIONS], callValueCPU[MAX_OPTIONS]; - - real sumDelta, sumRef, gpuTime, errorVal; - - StopWatchInterface *hTimer = NULL; - - int i; - - sdkCreateTimer(&hTimer); - - printf("Generating input data...\n"); - - // Generate options set - srand(123); - - for (i = 0; i < OPT_N; i++) { - optionData[i].S = randData(5.0f, 30.0f); - optionData[i].X = randData(1.0f, 100.0f); - optionData[i].T = randData(0.25f, 10.0f); - optionData[i].R = 0.06f; - optionData[i].V = 0.10f; - - BlackScholesCall(callValueBS[i], optionData[i]); - } - - printf("Running GPU binomial tree...\n"); - - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - - binomialOptionsGPU(callValueGPU, optionData, OPT_N, argc, argv); - - sdkStopTimer(&hTimer); - - gpuTime = sdkGetTimerValue(&hTimer); - - printf("Options count : %i \n", OPT_N); - printf("Time steps : %i \n", NUM_STEPS); - printf("binomialOptionsGPU() time: %f msec\n", gpuTime); - printf("Options per second : %f \n", OPT_N / (gpuTime * 0.001)); - - printf("Running CPU binomial tree...\n"); - - for (i = 0; i < OPT_N; i++) { - binomialOptionsCPU(callValueCPU[i], optionData[i]); - } - - printf("Comparing the results...\n"); - - sumDelta = 0; - sumRef = 0; - printf("GPU binomial vs. Black-Scholes\n"); - - for (i = 0; i < OPT_N; i++) { - sumDelta += fabs(callValueBS[i] - callValueGPU[i]); - sumRef += fabs(callValueBS[i]); - } - - if (sumRef > 1E-5) { - printf("L1 norm: %E\n", (double)(sumDelta / sumRef)); - } - else { - printf("Avg. diff: %E\n", (double)(sumDelta / (real)OPT_N)); - } - - printf("CPU binomial vs. Black-Scholes\n"); - sumDelta = 0; - sumRef = 0; - - for (i = 0; i < OPT_N; i++) { - sumDelta += fabs(callValueBS[i] - callValueCPU[i]); - sumRef += fabs(callValueBS[i]); - } - - if (sumRef > 1E-5) { - printf("L1 norm: %E\n", sumDelta / sumRef); - } - else { - printf("Avg. diff: %E\n", (double)(sumDelta / (real)OPT_N)); - } - - printf("CPU binomial vs. GPU binomial\n"); - sumDelta = 0; - sumRef = 0; - - for (i = 0; i < OPT_N; i++) { - sumDelta += fabs(callValueGPU[i] - callValueCPU[i]); - sumRef += callValueCPU[i]; - } - - if (sumRef > 1E-5) { - printf("L1 norm: %E\n", errorVal = sumDelta / sumRef); - } - else { - printf("Avg. diff: %E\n", (double)(sumDelta / (real)OPT_N)); - } - - printf("Shutting down...\n"); - - sdkDeleteTimer(&hTimer); - - printf("\nNOTE: The CUDA Samples are not meant for performance measurements. " - "Results may vary when GPU Boost is enabled.\n\n"); - - if (errorVal > 5e-4) { - printf("Test failed!\n"); - exit(EXIT_FAILURE); - } - - printf("Test passed\n"); - - exit(EXIT_SUCCESS); -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_common.h b/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_common.h deleted file mode 100644 index 1b4d9dfe..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_common.h +++ /dev/null @@ -1,54 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef BINOMIALOPTIONS_COMMON_H -#define BINOMIALOPTIONS_COMMON_H - -//////////////////////////////////////////////////////////////////////////////// -// Global types -//////////////////////////////////////////////////////////////////////////////// - -typedef struct -{ - float S; - float X; - float T; - float R; - float V; -} TOptionData; - -//////////////////////////////////////////////////////////////////////////////// -// Global parameters -//////////////////////////////////////////////////////////////////////////////// - -// Number of time steps -#define NUM_STEPS 2048 - -// Max option batch size -#define MAX_OPTIONS 1024 - -#endif diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gold.cpp b/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gold.cpp deleted file mode 100644 index 934b70cd..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gold.cpp +++ /dev/null @@ -1,132 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include - -#include "binomialOptions_common.h" - -/////////////////////////////////////////////////////////////////////////////// -// Polynomial approximation of cumulative normal distribution function -/////////////////////////////////////////////////////////////////////////////// - -static double CND(double d) -{ - const double A1 = 0.31938153; - const double A2 = -0.356563782; - const double A3 = 1.781477937; - const double A4 = -1.821255978; - const double A5 = 1.330274429; - const double RSQRT2PI = 0.39894228040143267793994605993438; - - double K = 1.0 / (1.0 + 0.2316419 * fabs(d)); - - double cnd = RSQRT2PI * exp(-0.5 * d * d) * (K * (A1 + K * (A2 + K * (A3 + K * (A4 + K * A5))))); - - if (d > 0) - cnd = 1.0 - cnd; - - return cnd; -} - -extern "C" void BlackScholesCall(float &callResult, TOptionData optionData) -{ - double S = optionData.S; - double X = optionData.X; - double T = optionData.T; - double R = optionData.R; - double V = optionData.V; - double sqrtT = sqrt(T); - - double d1 = (log(S / X) + (R + 0.5 * V * V) * T) / (V * sqrtT); - double d2 = d1 - V * sqrtT; - - double CNDD1 = CND(d1); - double CNDD2 = CND(d2); - - // Calculate Call and Put simultaneously - double expRT = exp(-R * T); - - callResult = (float)(S * CNDD1 - X * expRT * CNDD2); -} - -//////////////////////////////////////////////////////////////////////////////// -// Process an array of OptN options on CPU -// Note that CPU code is for correctness testing only and not for benchmarking. -//////////////////////////////////////////////////////////////////////////////// - -static double expiryCallValue(double S, double X, double vDt, int i) -{ - double d = S * exp(vDt * (2.0 * i - NUM_STEPS)) - X; - return (d > 0) ? d : 0; -} - -extern "C" void binomialOptionsCPU(float &callResult, TOptionData optionData) -{ - static double Call[NUM_STEPS + 1]; - const double S = optionData.S; - const double X = optionData.X; - const double T = optionData.T; - const double R = optionData.R; - const double V = optionData.V; - - const double dt = T / (double)NUM_STEPS; - const double vDt = V * sqrt(dt); - const double rDt = R * dt; - - // Per-step interest and discount factors - const double If = exp(rDt); - const double Df = exp(-rDt); - - // Values and pseudoprobabilities of upward and downward moves - const double u = exp(vDt); - const double d = exp(-vDt); - const double pu = (If - d) / (u - d); - const double pd = 1.0 - pu; - const double puByDf = pu * Df; - const double pdByDf = pd * Df; - - /////////////////////////////////////////////////////////////////////// - // Compute values at expiration date: - // call option value at period end is V(T) = S(T) - X - // if S(T) is greater than X, or zero otherwise. - // The computation is similar for put options. - /////////////////////////////////////////////////////////////////////// - - for (int i = 0; i <= NUM_STEPS; i++) - Call[i] = expiryCallValue(S, X, vDt, i); - - //////////////////////////////////////////////////////////////////////// - // Walk backwards up binomial tree - //////////////////////////////////////////////////////////////////////// - - for (int i = NUM_STEPS; i > 0; i--) - for (int j = 0; j <= i - 1; j++) - Call[j] = puByDf * Call[j + 1] + pdByDf * Call[j]; - - callResult = (float)Call[0]; -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gpu.cpp b/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gpu.cpp deleted file mode 100644 index 0fb12d13..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_gpu.cpp +++ /dev/null @@ -1,132 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -//////////////////////////////////////////////////////////////////////////////// -// Global types and parameters -//////////////////////////////////////////////////////////////////////////////// - -#include -#include -#include - -// Other helpers -#include -#include - -// CUDA runtime -#include - -#include "binomialOptions_common.h" -#include "common_gpu_header.h" -#include "realtype.h" - - -// Preprocessed input option data -typedef struct -{ - real S; - real X; - real vDt; - real puByDf; - real pdByDf; - -} __TOptionData; - -static bool moduleLoaded = false; -char *cubin, *kernel_file; -size_t cubinSize; -CUmodule module; - -//////////////////////////////////////////////////////////////////////////////// -// Host-side interface to GPU binomialOptions -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void binomialOptionsGPU(real *callValue, TOptionData *optionData, int optN, int argc, char **argv) -{ - if (!moduleLoaded) { - kernel_file = sdkFindFilePath("binomialOptions_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - module = loadCUBIN(cubin, argc, argv); - moduleLoaded = true; - } - - __TOptionData h_OptionData[MAX_OPTIONS]; - - for (int i = 0; i < optN; i++) { - const real T = optionData[i].T; - const real R = optionData[i].R; - const real V = optionData[i].V; - - const real dt = T / (real)NUM_STEPS; - const real vDt = V * sqrt(dt); - const real rDt = R * dt; - // Per-step interest and discount factors - const real If = exp(rDt); - const real Df = exp(-rDt); - // Values and pseudoprobabilities of upward and downward moves - const real u = exp(vDt); - const real d = exp(-vDt); - const real pu = (If - d) / (u - d); - const real pd = (real)1.0 - pu; - const real puByDf = pu * Df; - const real pdByDf = pd * Df; - - h_OptionData[i].S = (real)optionData[i].S; - h_OptionData[i].X = (real)optionData[i].X; - h_OptionData[i].vDt = (real)vDt; - h_OptionData[i].puByDf = (real)puByDf; - h_OptionData[i].pdByDf = (real)pdByDf; - } - - CUfunction kernel_addr; - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "binomialOptionsKernel")); - - CUdeviceptr d_OptionData; - checkCudaErrors(cuModuleGetGlobal(&d_OptionData, NULL, module, "d_OptionData")); - checkCudaErrors(cuMemcpyHtoD(d_OptionData, h_OptionData, optN * sizeof(__TOptionData))); - - dim3 cudaBlockSize(128, 1, 1); - dim3 cudaGridSize(optN, 1, 1); - - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - cudaBlockSize.x, - cudaBlockSize.y, - cudaBlockSize.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - NULL, /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); - - CUdeviceptr d_CallValue; - checkCudaErrors(cuModuleGetGlobal(&d_CallValue, NULL, module, "d_CallValue")); - checkCudaErrors(cuMemcpyDtoH(callValue, d_CallValue, optN * sizeof(real))); -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_kernel.cu b/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_kernel.cu deleted file mode 100644 index 9d50ca82..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/binomialOptions_kernel.cu +++ /dev/null @@ -1,110 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include "binomialOptions_common.h" -#include "common_gpu_header.h" -#include "realtype.h" - -// Preprocessed input option data -typedef struct -{ - real S; - real X; - real vDt; - real puByDf; - real pdByDf; -} __TOptionData; -static __constant__ __TOptionData d_OptionData[MAX_OPTIONS]; -__device__ real d_CallValue[MAX_OPTIONS]; - -#define THREADBLOCK_SIZE 128 -#define ELEMS_PER_THREAD (NUM_STEPS / THREADBLOCK_SIZE) -#if NUM_STEPS % THREADBLOCK_SIZE -#error Bad constants -#endif - -//////////////////////////////////////////////////////////////////////////////// -// Overloaded shortcut functions for different precision modes -//////////////////////////////////////////////////////////////////////////////// - -#ifndef DOUBLE_PRECISION -__device__ inline float expiryCallValue(float S, float X, float vDt, int i) -{ - float d = S * __expf(vDt * (2.0f * i - NUM_STEPS)) - X; - return (d > 0.0F) ? d : 0.0F; -} - -#else -__device__ inline double expiryCallValue(double S, double X, double vDt, int i) -{ - double d = S * exp(vDt * (2.0 * i - NUM_STEPS)) - X; - return (d > 0.0) ? d : 0.0; -} -#endif - -//////////////////////////////////////////////////////////////////////////////// -// GPU kernel -//////////////////////////////////////////////////////////////////////////////// -extern "C" __global__ void binomialOptionsKernel() -{ - __shared__ real call_exchange[THREADBLOCK_SIZE + 1]; - - const int tid = threadIdx.x; - const real S = d_OptionData[blockIdx.x].S; - const real X = d_OptionData[blockIdx.x].X; - const real vDt = d_OptionData[blockIdx.x].vDt; - const real puByDf = d_OptionData[blockIdx.x].puByDf; - const real pdByDf = d_OptionData[blockIdx.x].pdByDf; - - real call[ELEMS_PER_THREAD + 1]; -#pragma unroll - for (int i = 0; i < ELEMS_PER_THREAD; ++i) - call[i] = expiryCallValue(S, X, vDt, tid * ELEMS_PER_THREAD + i); - - if (tid == 0) - call_exchange[THREADBLOCK_SIZE] = expiryCallValue(S, X, vDt, NUM_STEPS); - - int final_it = max(0, tid * ELEMS_PER_THREAD - 1); - -#pragma unroll 16 - for (int i = NUM_STEPS; i > 0; --i) { - call_exchange[tid] = call[0]; - __syncthreads(); - call[ELEMS_PER_THREAD] = call_exchange[tid + 1]; - __syncthreads(); - - if (i > final_it) { -#pragma unroll - for (int j = 0; j < ELEMS_PER_THREAD; ++j) - call[j] = puByDf * call[j + 1] + pdByDf * call[j]; - } - } - - if (tid == 0) { - d_CallValue[blockIdx.x] = call[0]; - } -} diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/common_gpu_header.h b/cpp/5_Domain_Specific/binomialOptions_nvrtc/common_gpu_header.h deleted file mode 100644 index 88dc2936..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/common_gpu_header.h +++ /dev/null @@ -1,31 +0,0 @@ -/* - * Copyright 1993-2015 NVIDIA Corporation. All rights reserved. - * - * Please refer to the NVIDIA end user license agreement (EULA) associated - * with this source code for terms and conditions that govern your use of - * this software. Any use, reproduction, disclosure, or distribution of - * this software and related documentation outside the terms of the EULA - * is strictly prohibited. - * - */ - -#if !defined(__COMMON_GPU_HEADER_H) -#define __COMMON_GPU_HEADER_H - -//////////////////////////////////////////////////////////////////////////////// -// Internal GPU-side constants and data structures -//////////////////////////////////////////////////////////////////////////////// - -#define TIME_STEPS 16 - -#define CACHE_DELTA (2 * TIME_STEPS) - -#define CACHE_SIZE (256) - -#define CACHE_STEP (CACHE_SIZE - CACHE_DELTA) - -#if NUM_STEPS % CACHE_DELTA -#error Bad constants -#endif - -#endif diff --git a/cpp/5_Domain_Specific/binomialOptions_nvrtc/realtype.h b/cpp/5_Domain_Specific/binomialOptions_nvrtc/realtype.h deleted file mode 100644 index 031d7b59..00000000 --- a/cpp/5_Domain_Specific/binomialOptions_nvrtc/realtype.h +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef REALTYPE_H -#define REALTYPE_H - -// To use double precision uncomment the macro DOUBLE_PRECISION below, default -// is single precision. -// #define DOUBLE_PRECISION - -#ifndef DOUBLE_PRECISION -typedef float real; -#else -typedef double real; -#endif - -#endif diff --git a/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/extensions.json b/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/convolutionFFT2D/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/convolutionFFT2D/CMakeLists.txt b/cpp/5_Domain_Specific/convolutionFFT2D/CMakeLists.txt index 4e61664f..b1e073ae 100644 --- a/cpp/5_Domain_Specific/convolutionFFT2D/CMakeLists.txt +++ b/cpp/5_Domain_Specific/convolutionFFT2D/CMakeLists.txt @@ -1,34 +1,26 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(convolutionFFT2D LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for convolutionFFT2D add_executable(convolutionFFT2D convolutionFFT2D.cu convolutionFFT2D_gold.cpp main.cpp) target_compile_options(convolutionFFT2D PRIVATE $<$:--extended-lambda>) target_compile_features(convolutionFFT2D PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(convolutionFFT2D PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(convolutionFFT2D PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) @@ -37,6 +29,5 @@ target_link_libraries(convolutionFFT2D PUBLIC CUDA::cufft ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/dwtHaar1D/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/dwtHaar1D/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/dwtHaar1D/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/dwtHaar1D/.vscode/extensions.json b/cpp/5_Domain_Specific/dwtHaar1D/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/dwtHaar1D/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/dwtHaar1D/CMakeLists.txt b/cpp/5_Domain_Specific/dwtHaar1D/CMakeLists.txt index 0e1bfdbf..de79a959 100644 --- a/cpp/5_Domain_Specific/dwtHaar1D/CMakeLists.txt +++ b/cpp/5_Domain_Specific/dwtHaar1D/CMakeLists.txt @@ -1,40 +1,31 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(dwtHaar1D LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for dwtHaar1D add_executable(dwtHaar1D dwtHaar1D.cu) target_compile_options(dwtHaar1D PRIVATE $<$:--extended-lambda>) target_compile_features(dwtHaar1D PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(dwtHaar1D PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - add_custom_command(TARGET dwtHaar1D POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/dxtc/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/dxtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/dxtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/dxtc/.vscode/extensions.json b/cpp/5_Domain_Specific/dxtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/dxtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/dxtc/CMakeLists.txt b/cpp/5_Domain_Specific/dxtc/CMakeLists.txt index 90b336ba..6d224dd7 100644 --- a/cpp/5_Domain_Specific/dxtc/CMakeLists.txt +++ b/cpp/5_Domain_Specific/dxtc/CMakeLists.txt @@ -1,40 +1,31 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(dxtc LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for dxtc add_executable(dxtc dxtc.cu) target_compile_options(dxtc PRIVATE $<$:--extended-lambda>) target_compile_features(dxtc PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(dxtc PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - add_custom_command(TARGET dxtc POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/fastWalshTransform/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/fastWalshTransform/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/fastWalshTransform/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/fastWalshTransform/.vscode/extensions.json b/cpp/5_Domain_Specific/fastWalshTransform/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/fastWalshTransform/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/fastWalshTransform/CMakeLists.txt b/cpp/5_Domain_Specific/fastWalshTransform/CMakeLists.txt index d893e7a3..944cd26f 100644 --- a/cpp/5_Domain_Specific/fastWalshTransform/CMakeLists.txt +++ b/cpp/5_Domain_Specific/fastWalshTransform/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(fastWalshTransform LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for fastWalshTransform add_executable(fastWalshTransform fastWalshTransform.cu fastWalshTransform_gold.cpp) target_compile_options(fastWalshTransform PRIVATE $<$:--extended-lambda>) target_compile_features(fastWalshTransform PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(fastWalshTransform PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/fluidsGL/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/fluidsGL/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/fluidsGL/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/fluidsGL/.vscode/extensions.json b/cpp/5_Domain_Specific/fluidsGL/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/fluidsGL/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/fluidsGL/CMakeLists.txt b/cpp/5_Domain_Specific/fluidsGL/CMakeLists.txt index b9e70239..8e075a1d 100644 --- a/cpp/5_Domain_Specific/fluidsGL/CMakeLists.txt +++ b/cpp/5_Domain_Specific/fluidsGL/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(fluidsGL LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,50 +29,38 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for fluidsGL add_executable(fluidsGL fluidsGL_kernels.cu fluidsGL.cpp) - -target_compile_options(fluidsGL PRIVATE $<$:--extended-lambda>) - -target_compile_features(fluidsGL PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(fluidsGL PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(fluidsGL PRIVATE $<$:--extended-lambda>) + target_compile_features(fluidsGL PRIVATE cxx_std_17 cuda_std_17) target_include_directories(fluidsGL PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(fluidsGL ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} CUDA::cufft ) - # Copy data files to the output directory add_custom_command(TARGET fluidsGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(fluidsGL ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET fluidsGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET fluidsGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -84,14 +68,11 @@ target_compile_features(fluidsGL PRIVATE cxx_std_17 cuda_std_17) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'fluidsGL'") endif() else() message(STATUS "OpenGL not found - will not build sample 'fluidsGL'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/marchingCubes/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/marchingCubes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/marchingCubes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/marchingCubes/.vscode/extensions.json b/cpp/5_Domain_Specific/marchingCubes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/marchingCubes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/marchingCubes/CMakeLists.txt b/cpp/5_Domain_Specific/marchingCubes/CMakeLists.txt index 7970b60b..5a847258 100644 --- a/cpp/5_Domain_Specific/marchingCubes/CMakeLists.txt +++ b/cpp/5_Domain_Specific/marchingCubes/CMakeLists.txt @@ -1,90 +1,77 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(marchingCubes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) set(PC_GLUT_INCLUDE_DIRS "${CMAKE_CURRENT_SOURCE_DIR}/../../../Common") set(PC_GLUT_LIBRARY_DIRS "${CMAKE_CURRENT_SOURCE_DIR}/../../../Common/lib/x64") + # The GLEW library built on Windows on Arm system is named as glew32.lib/glew32.dll by default. + if(EXISTS "${PC_GLUT_LIBRARY_DIRS}/glew32.lib") + set(GLEW_LIB_NAME "glew32") + else() + set(GLEW_LIB_NAME "glew64") + endif() endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for marchingCubes add_executable(marchingCubes marchingCubes_kernel.cu marchingCubes.cpp) - -target_compile_options(marchingCubes PRIVATE $<$:--extended-lambda>) - -target_compile_features(marchingCubes PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(marchingCubes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - + target_compile_options(marchingCubes PRIVATE $<$:--extended-lambda;--expt-relaxed-constexpr>) + target_compile_features(marchingCubes PRIVATE cxx_std_17 cuda_std_17) target_include_directories(marchingCubes PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(marchingCubes ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - # Copy data files to the output directory add_custom_command(TARGET marchingCubes POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(marchingCubes ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib - ${PC_GLUT_LIBRARY_DIRS}/glew64.lib + ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET marchingCubes POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET marchingCubes POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy - ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/glew64.dll + ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/${GLEW_LIB_NAME}.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'marchingCubes'") endif() else() message(STATUS "OpenGL not found - will not build sample 'marchingCubes'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/marchingCubes/marchingCubes.cpp b/cpp/5_Domain_Specific/marchingCubes/marchingCubes.cpp index c6bcb450..0438d88c 100644 --- a/cpp/5_Domain_Specific/marchingCubes/marchingCubes.cpp +++ b/cpp/5_Domain_Specific/marchingCubes/marchingCubes.cpp @@ -431,6 +431,9 @@ int main(int argc, char **argv) //////////////////////////////////////////////////////////////////////////////// void initMC(int argc, char **argv) { + // Enlarge the per-thread device stack: under -G the CUB warpspeed exclusive_scan is not inlined and its deep call chain overflows the default 1KB stack. + checkCudaErrors(cudaDeviceSetLimit(cudaLimitStackSize, 8192)); + // parse command line arguments int n; diff --git a/cpp/5_Domain_Specific/nbody/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/nbody/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/nbody/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/nbody/.vscode/extensions.json b/cpp/5_Domain_Specific/nbody/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/nbody/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/nbody/CMakeLists.txt b/cpp/5_Domain_Specific/nbody/CMakeLists.txt index e8ff90a3..d09c43cd 100644 --- a/cpp/5_Domain_Specific/nbody/CMakeLists.txt +++ b/cpp/5_Domain_Specific/nbody/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(nbody LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,42 +29,31 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for nbody add_executable(nbody bodysystemcuda.cu render_particles.cpp nbody.cpp) - target_compile_options(nbody PRIVATE $<$:--extended-lambda>) - target_compile_features(nbody PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(nbody PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(nbody PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(nbody ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - if(WIN32) target_link_libraries(nbody ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET nbody POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET nbody POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -76,14 +61,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'nbody'") endif() else() message(STATUS "OpenGL not found - will not build sample 'nbody'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/extensions.json b/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/CMakeLists.txt b/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/CMakeLists.txt deleted file mode 100644 index 0f60b103..00000000 --- a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/CMakeLists.txt +++ /dev/null @@ -1,34 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(p2pBandwidthLatencyTest LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for p2pBandwidthLatencyTest -add_executable(p2pBandwidthLatencyTest p2pBandwidthLatencyTest.cu) - -target_compile_options(p2pBandwidthLatencyTest PRIVATE $<$:--extended-lambda>) - -target_compile_features(p2pBandwidthLatencyTest PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(p2pBandwidthLatencyTest PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/README.md b/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/README.md deleted file mode 100644 index 1e5ef8c1..00000000 --- a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# p2pBandwidthLatencyTest - Peer-to-Peer Bandwidth Latency Test with Multi-GPUs - -## Description - -This application demonstrates the CUDA Peer-To-Peer (P2P) data transfers between pairs of GPUs and computes latency and bandwidth. Tests on GPU pairs using P2P and without P2P are tested. - -## Key Concepts - -Performance Strategies, Asynchronous Data Transfers, Unified Virtual Address Space, Peer to Peer Data Transfers, Multi-GPU - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows - -## Supported CPU Architecture - -x86_64, armv7l - -## CUDA APIs involved - -### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html) -cudaSetDevice, cudaEventDestroy, cudaOccupancyMaxPotentialBlockSize, cudaCheckError, cudaFreeHost, cudaGetDeviceCount, cudaDeviceCanAccessPeer, cudaStreamCreateWithFlags, cudaStreamDestroy, cudaGetLastError, cudaMemset, cudaStreamWaitEvent, cudaEventElapsedTime, cudaEventCreate, cudaHostAlloc, cudaFree, cudaGetErrorString, cudaMemcpyPeerAsync, cudaDeviceDisablePeerAccess, cudaEventRecord, cudaStreamSynchronize, cudaDeviceEnablePeerAccess, cudaMalloc, cudaGetDeviceProperties - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. - -## References (for more details) diff --git a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/p2pBandwidthLatencyTest.cu b/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/p2pBandwidthLatencyTest.cu deleted file mode 100644 index 0c642fcd..00000000 --- a/cpp/5_Domain_Specific/p2pBandwidthLatencyTest/p2pBandwidthLatencyTest.cu +++ /dev/null @@ -1,836 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include -#include -#include - -using namespace std; - -const char *sSampleName = "P2P (Peer-to-Peer) GPU Bandwidth Latency Test"; - -typedef enum { - P2P_WRITE = 0, - P2P_READ = 1, -} P2PDataTransfer; - -typedef enum { - CE = 0, - SM = 1, -} P2PEngine; - -P2PEngine p2p_mechanism = CE; // By default use Copy Engine - -// Macro for checking cuda errors following a cuda launch or api call -#define cudaCheckError() \ - { \ - cudaError_t e = cudaGetLastError(); \ - if (e != cudaSuccess) { \ - printf("Cuda failure %s:%d: '%s'\n", __FILE__, __LINE__, cudaGetErrorString(e)); \ - exit(EXIT_FAILURE); \ - } \ - } -__global__ void delay(volatile int *flag, unsigned long long timeout_clocks = 10000000) -{ - // Wait until the application notifies us that it has completed queuing up the - // experiment, or timeout and exit, allowing the application to make progress - long long int start_clock, sample_clock; - start_clock = clock64(); - - while (!*flag) { - sample_clock = clock64(); - - if (sample_clock - start_clock > timeout_clocks) { - break; - } - } -} - -// This kernel is for demonstration purposes only, not a performant kernel for -// p2p transfers. -__global__ void copyp2p(int4 *__restrict__ dest, int4 const *__restrict__ src, size_t num_elems) -{ - size_t globalId = blockIdx.x * blockDim.x + threadIdx.x; - size_t gridSize = blockDim.x * gridDim.x; - -#pragma unroll(5) - for (size_t i = globalId; i < num_elems; i += gridSize) { - dest[i] = src[i]; - } -} - -/////////////////////////////////////////////////////////////////////////// -// Print help screen -/////////////////////////////////////////////////////////////////////////// -void printHelp(void) -{ - printf("Usage: p2pBandwidthLatencyTest [OPTION]...\n"); - printf("Tests bandwidth/latency of GPU pairs using P2P and without P2P\n"); - printf("\n"); - - printf("Options:\n"); - printf("--help\t\tDisplay this help menu\n"); - printf("--p2p_read\tUse P2P reads for data transfers between GPU pairs and show " - "corresponding results.\n \t\tDefault used is P2P write operation.\n"); - printf("--sm_copy Use SM intiated p2p transfers instead of Copy Engine\n"); - printf("--numElems= Number of integer elements to be used in p2p copy.\n"); -} - -/////////////////////////////////////////////////////////////////////////// -// Detect if cudaMemcpyPeerAsync will automatically fall back to -// host-staged copies when P2P is disabled. -// -// We probe a single representative pair (device 0 -> device 1). -// On a given system, confidential-computing (CC) and security -// policies are uniform across GPUs, so if this pair is blocked -// with cudaErrorNotSupported in P2P-off mode, it is reasonable -// to assume all cross-GPU pairs behave the same. -// -// For a production application that must handle heterogeneous -// environments, users may want to probe all device pairs. -/////////////////////////////////////////////////////////////////////////// -bool detectFallback(int numGPUs) -{ - if (numGPUs <= 1) - return false; - - cudaSetDevice(0); - int *tmp0 = nullptr, *tmp1 = nullptr; - cudaStream_t s; - cudaStreamCreateWithFlags(&s, cudaStreamNonBlocking); - - size_t testElems = 1; - cudaMalloc(&tmp0, testElems * sizeof(int)); - cudaSetDevice(1); - cudaMalloc(&tmp1, testElems * sizeof(int)); - cudaCheckError(); - - // Explicitly ensure P2P is disabled for this test - // (Clear any pre-existing P2P access if it happens to be enabled) - cudaSetDevice(0); - cudaDeviceDisablePeerAccess(1); - cudaGetLastError(); // Clear error if peer access was not enabled - - cudaSetDevice(1); - cudaDeviceDisablePeerAccess(0); - cudaGetLastError(); // Clear error if peer access was not enabled - - bool needsFallback = false; - cudaError_t testErr = cudaMemcpyPeerAsync(tmp1, 1, tmp0, 0, testElems * sizeof(int), s); - - if (testErr == cudaErrorPeerAccessNotEnabled || testErr == cudaErrorNotSupported) { - needsFallback = true; - printf("Note: cudaMemcpyPeerAsync reported '%s' - will use host-mediated copy when P2P is disabled\n", - cudaGetErrorString(testErr)); - cudaGetLastError(); - } - - cudaStreamSynchronize(s); - cudaFree(tmp0); - cudaFree(tmp1); - cudaStreamDestroy(s); - cudaCheckError(); - - return needsFallback; -} - -void checkP2Paccess(int numGPUs) -{ - for (int i = 0; i < numGPUs; i++) { - cudaSetDevice(i); - cudaCheckError(); - - for (int j = 0; j < numGPUs; j++) { - int access; - if (i != j) { - cudaDeviceCanAccessPeer(&access, i, j); - cudaCheckError(); - printf("Device=%d %s Access Peer Device=%d\n", i, access ? "CAN" : "CANNOT", j); - } - } - } - printf("\n***NOTE: In case a device doesn't have P2P access to other one, it " - "falls back to normal memcopy procedure.\nSo you can see lesser " - "Bandwidth (GB/s) and unstable Latency (us) in those cases.\n\n"); -} - -void performP2PCopy(int *dest, - int destDevice, - int *src, - int srcDevice, - int num_elems, - int repeat, - bool p2paccess, - cudaStream_t streamToRun, - bool useFallback, - int *hostBuffer) -{ - int blockSize = 0; - int numBlocks = 0; - - cudaOccupancyMaxPotentialBlockSize(&numBlocks, &blockSize, copyp2p); - cudaCheckError(); - - if (p2p_mechanism == SM && p2paccess) { - for (int r = 0; r < repeat; r++) { - copyp2p<<>>((int4 *)dest, (int4 *)src, num_elems / 4); - } - cudaCheckError(); - } - else if (useFallback && srcDevice != destDevice) { - // Use host-mediated copy for cross-GPU transfers when cudaMemcpyPeerAsync is not supported - for (int r = 0; r < repeat; r++) { - cudaMemcpyAsync(hostBuffer, src, sizeof(int) * num_elems, cudaMemcpyDeviceToHost, streamToRun); - cudaMemcpyAsync(dest, hostBuffer, sizeof(int) * num_elems, cudaMemcpyHostToDevice, streamToRun); - } - cudaCheckError(); - } - else if (useFallback && srcDevice == destDevice) { - // Same device copy - for (int r = 0; r < repeat; r++) { - cudaMemcpyAsync(dest, src, sizeof(int) * num_elems, cudaMemcpyDeviceToDevice, streamToRun); - } - cudaCheckError(); - } - else { - // Use cudaMemcpyPeerAsync - for (int r = 0; r < repeat; r++) { - cudaMemcpyPeerAsync(dest, destDevice, src, srcDevice, sizeof(int) * num_elems, streamToRun); - } - cudaCheckError(); - } -} - -void outputBandwidthMatrix(int numElems, int numGPUs, bool p2p, P2PDataTransfer p2p_method, bool needsFallback) -{ - int repeat = 5; - volatile int *flag = NULL; - vector buffers(numGPUs); - vector buffersD2D(numGPUs); // buffer for D2D, that is, intra-GPU copy - vector start(numGPUs); - vector stop(numGPUs); - vector stream(numGPUs); - - cudaHostAlloc((void **)&flag, sizeof(*flag), cudaHostAllocPortable); - cudaCheckError(); - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaStreamCreateWithFlags(&stream[d], cudaStreamNonBlocking); - cudaMalloc(&buffers[d], numElems * sizeof(int)); - cudaCheckError(); - cudaMemset(buffers[d], 0, numElems * sizeof(int)); - cudaCheckError(); - cudaMalloc(&buffersD2D[d], numElems * sizeof(int)); - cudaCheckError(); - cudaMemset(buffersD2D[d], 0, numElems * sizeof(int)); - cudaCheckError(); - cudaEventCreate(&start[d]); - cudaCheckError(); - cudaEventCreate(&stop[d]); - cudaCheckError(); - } - - // Decide if fallback is needed based on global detection result and current test scenario - bool useFallback = (!p2p && needsFallback); - int *hostBuffer = nullptr; - - if (useFallback) { - // Allocate hostBuffer for this function's numElems - cudaHostAlloc((void **)&hostBuffer, sizeof(int) * numElems, cudaHostAllocDefault); - cudaCheckError(); - } - - vector bandwidthMatrix(numGPUs * numGPUs); - - for (int i = 0; i < numGPUs; i++) { - cudaSetDevice(i); - - for (int j = 0; j < numGPUs; j++) { - int access = 0; - if (p2p) { - cudaDeviceCanAccessPeer(&access, i, j); - if (access) { - cudaDeviceEnablePeerAccess(j, 0); - cudaCheckError(); - cudaSetDevice(j); - cudaCheckError(); - cudaDeviceEnablePeerAccess(i, 0); - cudaCheckError(); - cudaSetDevice(i); - cudaCheckError(); - } - } - - cudaStreamSynchronize(stream[i]); - cudaCheckError(); - - // Block the stream until all the work is queued up - // DANGER! - cudaMemcpy*Async may infinitely block waiting for - // room to push the operation, so keep the number of repeatitions - // relatively low. Higher repeatitions will cause the delay kernel - // to timeout and lead to unstable results. - *flag = 0; - delay<<<1, 1, 0, stream[i]>>>(flag); - cudaCheckError(); - cudaEventRecord(start[i], stream[i]); - cudaCheckError(); - - if (i == j) { - // Perform intra-GPU, D2D copies - performP2PCopy( - buffers[i], i, buffersD2D[i], i, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - else { - if (p2p_method == P2P_WRITE) { - performP2PCopy( - buffers[j], j, buffers[i], i, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - else { - performP2PCopy( - buffers[i], i, buffers[j], j, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - } - cudaCheckError(); - - cudaEventRecord(stop[i], stream[i]); - cudaCheckError(); - - // Release the queued events - *flag = 1; - cudaStreamSynchronize(stream[i]); - cudaCheckError(); - - float time_ms; - cudaEventElapsedTime(&time_ms, start[i], stop[i]); - double time_s = time_ms / 1e3; - - double gb = numElems * sizeof(int) * repeat / (double)1e9; - if (i == j) { - gb *= 2; // must count both the read and the write here - } - bandwidthMatrix[i * numGPUs + j] = gb / time_s; - if (p2p && access) { - cudaDeviceDisablePeerAccess(j); - cudaSetDevice(j); - cudaDeviceDisablePeerAccess(i); - cudaSetDevice(i); - cudaCheckError(); - } - } - } - - printf(" D\\D"); - - for (int j = 0; j < numGPUs; j++) { - printf("%6d ", j); - } - - printf("\n"); - - for (int i = 0; i < numGPUs; i++) { - printf("%6d ", i); - - for (int j = 0; j < numGPUs; j++) { - printf("%6.02f ", bandwidthMatrix[i * numGPUs + j]); - } - - printf("\n"); - } - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaFree(buffers[d]); - cudaFree(buffersD2D[d]); - cudaCheckError(); - cudaEventDestroy(start[d]); - cudaCheckError(); - cudaEventDestroy(stop[d]); - cudaCheckError(); - cudaStreamDestroy(stream[d]); - cudaCheckError(); - } - - if (hostBuffer) { - cudaFreeHost(hostBuffer); - cudaCheckError(); - } - - cudaFreeHost((void *)flag); - cudaCheckError(); -} - -void outputBidirectionalBandwidthMatrix(int numElems, int numGPUs, bool p2p, bool needsFallback) -{ - int repeat = 5; - volatile int *flag = NULL; - vector buffers(numGPUs); - vector buffersD2D(numGPUs); - vector start(numGPUs); - vector stop(numGPUs); - vector stream0(numGPUs); - vector stream1(numGPUs); - - cudaHostAlloc((void **)&flag, sizeof(*flag), cudaHostAllocPortable); - cudaCheckError(); - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaMalloc(&buffers[d], numElems * sizeof(int)); - cudaMemset(buffers[d], 0, numElems * sizeof(int)); - cudaMalloc(&buffersD2D[d], numElems * sizeof(int)); - cudaMemset(buffersD2D[d], 0, numElems * sizeof(int)); - cudaCheckError(); - cudaEventCreate(&start[d]); - cudaCheckError(); - cudaEventCreate(&stop[d]); - cudaCheckError(); - cudaStreamCreateWithFlags(&stream0[d], cudaStreamNonBlocking); - cudaCheckError(); - cudaStreamCreateWithFlags(&stream1[d], cudaStreamNonBlocking); - cudaCheckError(); - } - - // Decide if fallback is needed based on global detection result and current test scenario - bool useFallback = (!p2p && needsFallback); - int *hostBuffer = nullptr; - - if (useFallback) { - // Allocate hostBuffer for this function's numElems - cudaHostAlloc((void **)&hostBuffer, sizeof(int) * numElems, cudaHostAllocDefault); - cudaCheckError(); - } - - vector bandwidthMatrix(numGPUs * numGPUs); - - for (int i = 0; i < numGPUs; i++) { - cudaSetDevice(i); - - for (int j = 0; j < numGPUs; j++) { - int access = 0; - if (p2p) { - cudaDeviceCanAccessPeer(&access, i, j); - if (access) { - cudaSetDevice(i); - cudaDeviceEnablePeerAccess(j, 0); - cudaCheckError(); - cudaSetDevice(j); - cudaDeviceEnablePeerAccess(i, 0); - cudaCheckError(); - } - } - - cudaSetDevice(i); - cudaStreamSynchronize(stream0[i]); - cudaStreamSynchronize(stream1[j]); - cudaCheckError(); - - // Block the stream until all the work is queued up - // DANGER! - cudaMemcpy*Async may infinitely block waiting for - // room to push the operation, so keep the number of repeatitions - // relatively low. Higher repeatitions will cause the delay kernel - // to timeout and lead to unstable results. - *flag = 0; - cudaSetDevice(i); - // No need to block stream1 since it'll be blocked on stream0's event - delay<<<1, 1, 0, stream0[i]>>>(flag); - cudaCheckError(); - - // Force stream1 not to start until stream0 does, in order to ensure - // the events on stream0 fully encompass the time needed for all - // operations - cudaEventRecord(start[i], stream0[i]); - cudaStreamWaitEvent(stream1[j], start[i], 0); - - if (i == j) { - // For intra-GPU perform 2 memcopies buffersD2D <-> buffers - performP2PCopy( - buffers[i], i, buffersD2D[i], i, numElems, repeat, access, stream0[i], useFallback, hostBuffer); - performP2PCopy( - buffersD2D[i], i, buffers[i], i, numElems, repeat, access, stream1[i], useFallback, hostBuffer); - } - else { - if (access && p2p_mechanism == SM) { - cudaSetDevice(j); - } - performP2PCopy( - buffers[i], i, buffers[j], j, numElems, repeat, access, stream1[j], useFallback, hostBuffer); - if (access && p2p_mechanism == SM) { - cudaSetDevice(i); - } - performP2PCopy( - buffers[j], j, buffers[i], i, numElems, repeat, access, stream0[i], useFallback, hostBuffer); - } - - // Notify stream0 that stream1 is complete and record the time of - // the total transaction - cudaEventRecord(stop[j], stream1[j]); - cudaStreamWaitEvent(stream0[i], stop[j], 0); - cudaEventRecord(stop[i], stream0[i]); - - // Release the queued operations - *flag = 1; - cudaStreamSynchronize(stream0[i]); - cudaStreamSynchronize(stream1[j]); - cudaCheckError(); - - float time_ms; - cudaEventElapsedTime(&time_ms, start[i], stop[i]); - double time_s = time_ms / 1e3; - - double gb = 2.0 * numElems * sizeof(int) * repeat / (double)1e9; - if (i == j) { - gb *= 2; // must count both the read and the write here - } - bandwidthMatrix[i * numGPUs + j] = gb / time_s; - if (p2p && access) { - cudaSetDevice(i); - cudaDeviceDisablePeerAccess(j); - cudaSetDevice(j); - cudaDeviceDisablePeerAccess(i); - } - } - } - - printf(" D\\D"); - - for (int j = 0; j < numGPUs; j++) { - printf("%6d ", j); - } - - printf("\n"); - - for (int i = 0; i < numGPUs; i++) { - printf("%6d ", i); - - for (int j = 0; j < numGPUs; j++) { - printf("%6.02f ", bandwidthMatrix[i * numGPUs + j]); - } - - printf("\n"); - } - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaFree(buffers[d]); - cudaFree(buffersD2D[d]); - cudaCheckError(); - cudaEventDestroy(start[d]); - cudaCheckError(); - cudaEventDestroy(stop[d]); - cudaCheckError(); - cudaStreamDestroy(stream0[d]); - cudaCheckError(); - cudaStreamDestroy(stream1[d]); - cudaCheckError(); - } - - if (hostBuffer) { - cudaFreeHost(hostBuffer); - cudaCheckError(); - } - - cudaFreeHost((void *)flag); - cudaCheckError(); -} - -void outputLatencyMatrix(int numGPUs, bool p2p, P2PDataTransfer p2p_method, bool needsFallback) -{ - int repeat = 100; - int numElems = 4; // perform 1-int4 transfer. - volatile int *flag = NULL; - StopWatchInterface *stopWatch = NULL; - vector buffers(numGPUs); - vector buffersD2D(numGPUs); // buffer for D2D, that is, intra-GPU copy - vector stream(numGPUs); - vector start(numGPUs); - vector stop(numGPUs); - - cudaHostAlloc((void **)&flag, sizeof(*flag), cudaHostAllocPortable); - cudaCheckError(); - - if (!sdkCreateTimer(&stopWatch)) { - printf("Failed to create stop watch\n"); - exit(EXIT_FAILURE); - } - sdkStartTimer(&stopWatch); - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaStreamCreateWithFlags(&stream[d], cudaStreamNonBlocking); - cudaMalloc(&buffers[d], sizeof(int) * numElems); - cudaMemset(buffers[d], 0, sizeof(int) * numElems); - cudaMalloc(&buffersD2D[d], sizeof(int) * numElems); - cudaMemset(buffersD2D[d], 0, sizeof(int) * numElems); - cudaCheckError(); - cudaEventCreate(&start[d]); - cudaCheckError(); - cudaEventCreate(&stop[d]); - cudaCheckError(); - } - - // Decide if fallback is needed based on global detection result and current test scenario - bool useFallback = (!p2p && needsFallback); - int *hostBuffer = nullptr; - - if (useFallback) { - // Allocate hostBuffer for this function's numElems - cudaHostAlloc((void **)&hostBuffer, sizeof(int) * numElems, cudaHostAllocDefault); - cudaCheckError(); - } - - vector gpuLatencyMatrix(numGPUs * numGPUs); - vector cpuLatencyMatrix(numGPUs * numGPUs); - - for (int i = 0; i < numGPUs; i++) { - cudaSetDevice(i); - - for (int j = 0; j < numGPUs; j++) { - int access = 0; - if (p2p) { - cudaDeviceCanAccessPeer(&access, i, j); - if (access) { - cudaDeviceEnablePeerAccess(j, 0); - cudaCheckError(); - cudaSetDevice(j); - cudaDeviceEnablePeerAccess(i, 0); - cudaSetDevice(i); - cudaCheckError(); - } - } - cudaStreamSynchronize(stream[i]); - cudaCheckError(); - - // Block the stream until all the work is queued up - // DANGER! - cudaMemcpy*Async may infinitely block waiting for - // room to push the operation, so keep the number of repeatitions - // relatively low. Higher repeatitions will cause the delay kernel - // to timeout and lead to unstable results. - *flag = 0; - delay<<<1, 1, 0, stream[i]>>>(flag); - cudaCheckError(); - cudaEventRecord(start[i], stream[i]); - - sdkResetTimer(&stopWatch); - if (i == j) { - // Perform intra-GPU, D2D copies - performP2PCopy( - buffers[i], i, buffersD2D[i], i, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - else { - if (p2p_method == P2P_WRITE) { - performP2PCopy( - buffers[j], j, buffers[i], i, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - else { - performP2PCopy( - buffers[i], i, buffers[j], j, numElems, repeat, access, stream[i], useFallback, hostBuffer); - } - } - float cpu_time_ms = sdkGetTimerValue(&stopWatch); - - cudaEventRecord(stop[i], stream[i]); - // Now that the work has been queued up, release the stream - *flag = 1; - cudaStreamSynchronize(stream[i]); - cudaCheckError(); - - float gpu_time_ms; - cudaEventElapsedTime(&gpu_time_ms, start[i], stop[i]); - - gpuLatencyMatrix[i * numGPUs + j] = gpu_time_ms * 1e3 / repeat; - cpuLatencyMatrix[i * numGPUs + j] = cpu_time_ms * 1e3 / repeat; - if (p2p && access) { - cudaDeviceDisablePeerAccess(j); - cudaSetDevice(j); - cudaDeviceDisablePeerAccess(i); - cudaSetDevice(i); - cudaCheckError(); - } - } - } - - printf(" GPU"); - - for (int j = 0; j < numGPUs; j++) { - printf("%6d ", j); - } - - printf("\n"); - - for (int i = 0; i < numGPUs; i++) { - printf("%6d ", i); - - for (int j = 0; j < numGPUs; j++) { - printf("%6.02f ", gpuLatencyMatrix[i * numGPUs + j]); - } - - printf("\n"); - } - - printf("\n CPU"); - - for (int j = 0; j < numGPUs; j++) { - printf("%6d ", j); - } - - printf("\n"); - - for (int i = 0; i < numGPUs; i++) { - printf("%6d ", i); - - for (int j = 0; j < numGPUs; j++) { - printf("%6.02f ", cpuLatencyMatrix[i * numGPUs + j]); - } - - printf("\n"); - } - - for (int d = 0; d < numGPUs; d++) { - cudaSetDevice(d); - cudaFree(buffers[d]); - cudaFree(buffersD2D[d]); - cudaCheckError(); - cudaEventDestroy(start[d]); - cudaCheckError(); - cudaEventDestroy(stop[d]); - cudaCheckError(); - cudaStreamDestroy(stream[d]); - cudaCheckError(); - } - - sdkDeleteTimer(&stopWatch); - - if (hostBuffer) { - cudaFreeHost(hostBuffer); - cudaCheckError(); - } - - cudaFreeHost((void *)flag); - cudaCheckError(); -} - -int main(int argc, char **argv) -{ - int numGPUs, numElems = 40000000; - P2PDataTransfer p2p_method = P2P_WRITE; - - cudaGetDeviceCount(&numGPUs); - cudaCheckError(); - - // process command line args - if (checkCmdLineFlag(argc, (const char **)argv, "help")) { - printHelp(); - return 0; - } - - if (checkCmdLineFlag(argc, (const char **)argv, "p2p_read")) { - p2p_method = P2P_READ; - } - - if (checkCmdLineFlag(argc, (const char **)argv, "sm_copy")) { - p2p_mechanism = SM; - } - - // number of elements of int to be used in copy. - if (checkCmdLineFlag(argc, (const char **)argv, "numElems")) { - numElems = getCmdLineArgumentInt(argc, (const char **)argv, "numElems"); - } - - printf("[%s]\n", sSampleName); - - // output devices - for (int i = 0; i < numGPUs; i++) { - cudaDeviceProp prop; - cudaGetDeviceProperties(&prop, i); - cudaCheckError(); - printf("Device: %d, %s, pciBusID: %x, pciDeviceID: %x, pciDomainID:%x\n", - i, - prop.name, - prop.pciBusID, - prop.pciDeviceID, - prop.pciDomainID); - } - - checkP2Paccess(numGPUs); - - // Environment detection: One-time check if cudaMemcpyPeerAsync is supported when P2P is disabled - bool needsFallback = detectFallback(numGPUs); - - // Check peer-to-peer connectivity - printf("P2P Connectivity Matrix\n"); - printf(" D\\D"); - - for (int j = 0; j < numGPUs; j++) { - printf("%6d", j); - } - printf("\n"); - - for (int i = 0; i < numGPUs; i++) { - printf("%6d\t", i); - for (int j = 0; j < numGPUs; j++) { - if (i != j) { - int access; - cudaDeviceCanAccessPeer(&access, i, j); - cudaCheckError(); - printf("%6d", (access) ? 1 : 0); - } - else { - printf("%6d", 1); - } - } - printf("\n"); - } - - printf("Unidirectional P2P=Disabled Bandwidth Matrix (GB/s)\n"); - outputBandwidthMatrix(numElems, numGPUs, false, P2P_WRITE, needsFallback); - printf("Unidirectional P2P=Enabled Bandwidth (P2P Writes) Matrix (GB/s)\n"); - outputBandwidthMatrix(numElems, numGPUs, true, P2P_WRITE, needsFallback); - if (p2p_method == P2P_READ) { - printf("Unidirectional P2P=Enabled Bandwidth (P2P Reads) Matrix (GB/s)\n"); - outputBandwidthMatrix(numElems, numGPUs, true, p2p_method, needsFallback); - } - printf("Bidirectional P2P=Disabled Bandwidth Matrix (GB/s)\n"); - outputBidirectionalBandwidthMatrix(numElems, numGPUs, false, needsFallback); - printf("Bidirectional P2P=Enabled Bandwidth Matrix (GB/s)\n"); - outputBidirectionalBandwidthMatrix(numElems, numGPUs, true, needsFallback); - - printf("P2P=Disabled Latency Matrix (us)\n"); - outputLatencyMatrix(numGPUs, false, P2P_WRITE, needsFallback); - printf("P2P=Enabled Latency (P2P Writes) Matrix (us)\n"); - outputLatencyMatrix(numGPUs, true, P2P_WRITE, needsFallback); - if (p2p_method == P2P_READ) { - printf("P2P=Enabled Latency (P2P Reads) Matrix (us)\n"); - outputLatencyMatrix(numGPUs, true, p2p_method, needsFallback); - } - - printf("\nNOTE: The CUDA Samples are not meant for performance measurements. " - "Results may vary when GPU Boost is enabled.\n"); - - exit(EXIT_SUCCESS); -} diff --git a/cpp/5_Domain_Specific/postProcessGL/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/postProcessGL/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/postProcessGL/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/postProcessGL/.vscode/extensions.json b/cpp/5_Domain_Specific/postProcessGL/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/postProcessGL/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/postProcessGL/CMakeLists.txt b/cpp/5_Domain_Specific/postProcessGL/CMakeLists.txt index a8bf5460..05bbf342 100644 --- a/cpp/5_Domain_Specific/postProcessGL/CMakeLists.txt +++ b/cpp/5_Domain_Specific/postProcessGL/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(postProcessGL LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for postProcessGL add_executable(postProcessGL main.cpp postProcessGL.cu) - target_compile_options(postProcessGL PRIVATE $<$:--extended-lambda>) - target_compile_features(postProcessGL PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(postProcessGL PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(postProcessGL PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(postProcessGL ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET postProcessGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(postProcessGL ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET postProcessGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET postProcessGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'postProcessGL'") endif() else() message(STATUS "OpenGL not found - will not build sample 'postProcessGL'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/extensions.json b/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator/CMakeLists.txt b/cpp/5_Domain_Specific/quasirandomGenerator/CMakeLists.txt index 55b4308b..a57cc52d 100644 --- a/cpp/5_Domain_Specific/quasirandomGenerator/CMakeLists.txt +++ b/cpp/5_Domain_Specific/quasirandomGenerator/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(quasirandomGenerator LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for quasirandomGenerator add_executable(quasirandomGenerator quasirandomGenerator.cpp quasirandomGenerator_gold.cpp quasirandomGenerator_kernel.cu) target_compile_options(quasirandomGenerator PRIVATE $<$:--extended-lambda>) target_compile_features(quasirandomGenerator PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(quasirandomGenerator PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(quasirandomGenerator PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/extensions.json b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/CMakeLists.txt b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/CMakeLists.txt deleted file mode 100644 index 1b9d16e5..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/CMakeLists.txt +++ /dev/null @@ -1,45 +0,0 @@ -cmake_minimum_required(VERSION 3.20) - -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") - -project(quasirandomGenerator_nvrtc LANGUAGES C CXX CUDA) - -find_package(CUDAToolkit REQUIRED) - -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries -include_directories(../../../Common) - -# Source file -# Add target for quasirandomGenerator_nvrtc -add_executable(quasirandomGenerator_nvrtc quasirandomGenerator.cpp quasirandomGenerator_gold.cpp) - -target_compile_options(quasirandomGenerator_nvrtc PRIVATE $<$:--extended-lambda>) - -target_compile_features(quasirandomGenerator_nvrtc PRIVATE cxx_std_17 cuda_std_17) - -set_target_properties(quasirandomGenerator_nvrtc PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -target_link_libraries(quasirandomGenerator_nvrtc PRIVATE - CUDA::nvrtc - CUDA::cuda_driver -) - -# Copy kernel to the output directory -add_custom_command(TARGET quasirandomGenerator_nvrtc POST_BUILD - COMMAND ${CMAKE_COMMAND} -E copy_if_different - ${CMAKE_CURRENT_SOURCE_DIR}/quasirandomGenerator_kernel.cu ${CMAKE_CURRENT_SOURCE_DIR}/quasirandomGenerator_gpu.cuh ${CMAKE_CURRENT_SOURCE_DIR}/quasirandomGenerator_common.h ${CMAKE_CURRENT_BINARY_DIR} -) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/README.md b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/README.md deleted file mode 100644 index 642db3f1..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# quasirandomGenerator_nvrtc - Niederreiter Quasirandom Sequence Generator with libNVRTC - -## Description - -This sample implements Niederreiter Quasirandom Sequence Generator and Inverse Cumulative Normal Distribution functions for the generation of Standard Normal Distributions, compiling the CUDA kernels involved at runtime using NVRTC. - -## Key Concepts - -Computational Finance, Runtime Compilation - -## Supported SM Architectures - -[SM 5.0 ](https://developer.nvidia.com/cuda-gpus) [SM 5.2 ](https://developer.nvidia.com/cuda-gpus) [SM 5.3 ](https://developer.nvidia.com/cuda-gpus) [SM 6.0 ](https://developer.nvidia.com/cuda-gpus) [SM 6.1 ](https://developer.nvidia.com/cuda-gpus) [SM 7.0 ](https://developer.nvidia.com/cuda-gpus) [SM 7.2 ](https://developer.nvidia.com/cuda-gpus) [SM 7.5 ](https://developer.nvidia.com/cuda-gpus) [SM 8.0 ](https://developer.nvidia.com/cuda-gpus) [SM 8.6 ](https://developer.nvidia.com/cuda-gpus) [SM 8.7 ](https://developer.nvidia.com/cuda-gpus) [SM 8.9 ](https://developer.nvidia.com/cuda-gpus) [SM 9.0 ](https://developer.nvidia.com/cuda-gpus) - -## Supported OSes - -Linux, Windows, QNX - -## Supported CPU Architecture - -x86_64, aarch64 - -## CUDA APIs involved - -### [CUDA Driver API](http://docs.nvidia.com/cuda/cuda-driver-api/index.html) -cuMemcpyDtoH, cuMemAlloc, cuMemFree - -## Dependencies needed to build/run -[NVRTC](../../../README.md#nvrtc) - -## Prerequisites - -Download and install the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) for your corresponding platform. -Make sure the dependencies mentioned in [Dependencies]() section above are installed. - -## References (for more details) diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator.cpp b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator.cpp deleted file mode 100644 index 3f4c0636..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator.cpp +++ /dev/null @@ -1,180 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -// CUDA Runtime -#include -#include - -// Utilities and system includes -#include - -#include "quasirandomGenerator_common.h" -#include "quasirandomGenerator_gpu.cuh" - -//////////////////////////////////////////////////////////////////////////////// -// CPU code -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void initQuasirandomGenerator(unsigned int table[QRNG_DIMENSIONS][QRNG_RESOLUTION]); - -extern "C" float getQuasirandomValue(unsigned int table[QRNG_DIMENSIONS][QRNG_RESOLUTION], int i, int dim); - -extern "C" double getQuasirandomValue63(INT64 i, int dim); -extern "C" double MoroInvCNDcpu(unsigned int p); - -const int N = 1048576; - -int main(int argc, char **argv) -{ - // Start logs - printf("%s Starting...\n\n", argv[0]); - - // Compile the kernels - char *kernel_file = sdkFindFilePath("quasirandomGenerator_kernel.cu", argv[0]); - compileFileToCUBIN(kernel_file, argc, argv, &cubin, &cubinSize, 0); - module = loadCUBIN(cubin, argc, argv); - - unsigned int tableCPU[QRNG_DIMENSIONS][QRNG_RESOLUTION]; - float *h_OutputGPU; - CUdeviceptr d_Output; - - int dim, pos; - double delta, ref, sumDelta, sumRef, L1norm, gpuTime; - - StopWatchInterface *hTimer = NULL; - - if (sizeof(INT64) != 8) { - printf("sizeof(INT64) != 8\n"); - return 0; - } - - sdkCreateTimer(&hTimer); - - printf("Allocating GPU memory...\n"); - checkCudaErrors(cuMemAlloc(&d_Output, QRNG_DIMENSIONS * N * sizeof(float))); - - printf("Allocating CPU memory...\n"); - h_OutputGPU = (float *)malloc(QRNG_DIMENSIONS * N * sizeof(float)); - - printf("Initializing QRNG tables...\n\n"); - initQuasirandomGenerator(tableCPU); - - initTableGPU(tableCPU); - - printf("Testing QRNG...\n\n"); - - checkCudaErrors(cuMemsetD8(d_Output, 0, QRNG_DIMENSIONS * N * sizeof(float))); - - int numIterations = 20; - - for (int i = -1; i < numIterations; i++) { - if (i == 0) { - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - } - - quasirandomGeneratorGPU(d_Output, 0, N); - } - - sdkStopTimer(&hTimer); - gpuTime = sdkGetTimerValue(&hTimer) / (double)numIterations * 1e-3; - printf("quasirandomGenerator, Throughput = %.4f GNumbers/s, Time = %.5f s, Size " - "= %u Numbers, NumDevsUsed = %u, Workgroup = %u\n", - (double)QRNG_DIMENSIONS * (double)N * 1.0E-9 / gpuTime, - gpuTime, - QRNG_DIMENSIONS * N, - 1, - 128 * QRNG_DIMENSIONS); - - printf("\nReading GPU results...\n"); - - checkCudaErrors(cuMemcpyDtoH(h_OutputGPU, d_Output, QRNG_DIMENSIONS * N * sizeof(float))); - - printf("Comparing to the CPU results...\n\n"); - sumDelta = 0; - sumRef = 0; - - for (dim = 0; dim < QRNG_DIMENSIONS; dim++) - for (pos = 0; pos < N; pos++) { - ref = getQuasirandomValue63(pos, dim); - delta = (double)h_OutputGPU[dim * N + pos] - ref; - sumDelta += fabs(delta); - sumRef += fabs(ref); - } - - printf("L1 norm: %E\n", sumDelta / sumRef); - - printf("\nTesting inverseCNDgpu()...\n\n"); - - checkCudaErrors(cuMemsetD8(d_Output, 0, QRNG_DIMENSIONS * N * sizeof(float))); - - for (int i = -1; i < numIterations; i++) { - if (i == 0) { - sdkResetTimer(&hTimer); - sdkStartTimer(&hTimer); - } - - inverseCNDgpu(d_Output, QRNG_DIMENSIONS * N); - } - - sdkStopTimer(&hTimer); - gpuTime = sdkGetTimerValue(&hTimer) / (double)numIterations * 1e-3; - printf("quasirandomGenerator-inverse, Throughput = %.4f GNumbers/s, Time = %.5f " - "s, Size = %u Numbers, NumDevsUsed = %u, Workgroup = %u\n", - (double)QRNG_DIMENSIONS * (double)N * 1E-9 / gpuTime, - gpuTime, - QRNG_DIMENSIONS * N, - 1, - 128); - - printf("Reading GPU results...\n"); - checkCudaErrors(cuMemcpyDtoH(h_OutputGPU, d_Output, QRNG_DIMENSIONS * N * sizeof(float))); - - printf("\nComparing to the CPU results...\n"); - - sumDelta = 0; - sumRef = 0; - unsigned int distance = ((unsigned int)-1) / (QRNG_DIMENSIONS * N + 1); - - for (pos = 0; pos < QRNG_DIMENSIONS * N; pos++) { - unsigned int d = (pos + 1) * distance; - ref = MoroInvCNDcpu(d); - delta = (double)h_OutputGPU[pos] - ref; - sumDelta += fabs(delta); - sumRef += fabs(ref); - } - - printf("L1 norm: %E\n\n", L1norm = sumDelta / sumRef); - printf("Shutting down...\n"); - - sdkDeleteTimer(&hTimer); - free(h_OutputGPU); - - checkCudaErrors(cuMemFree(d_Output)); - - exit(L1norm < 1e-6 ? EXIT_SUCCESS : EXIT_FAILURE); -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_common.h b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_common.h deleted file mode 100644 index f6e1b124..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_common.h +++ /dev/null @@ -1,41 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef QUASIRANDOMGENERATOR_COMMON_H -#define QUASIRANDOMGENERATOR_COMMON_H - -//////////////////////////////////////////////////////////////////////////////// -// Global types and constants -//////////////////////////////////////////////////////////////////////////////// - -typedef long long int INT64; - -#define QRNG_DIMENSIONS 3 -#define QRNG_RESOLUTION 31 -#define INT_SCALE (1.0f / (float)0x80000001U) - -#endif diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gold.cpp b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gold.cpp deleted file mode 100644 index 5037967e..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gold.cpp +++ /dev/null @@ -1,339 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#include -#include - -#include "quasirandomGenerator_common.h" - -//////////////////////////////////////////////////////////////////////////////// -// Table generation functions -//////////////////////////////////////////////////////////////////////////////// - -// Internal 64(63)-bit table -static INT64 cjn[63][QRNG_DIMENSIONS]; - -static int GeneratePolynomials(int buffer[QRNG_DIMENSIONS], bool primitive) -{ - int i, j, n, p1, p2, l; - int e_p1, e_p2, e_b; - - // generate all polynomials to buffer - for (n = 1, buffer[0] = 0x2, p2 = 0, l = 0; n < QRNG_DIMENSIONS; ++n) { - // search for the next irreducible polynomial - for (p1 = buffer[n - 1] + 1;; ++p1) { - // find degree of polynomial p1 - for (e_p1 = 30; (p1 & (1 << e_p1)) == 0; --e_p1) { - } - - // try to divide p1 by all polynomials in buffer - for (i = 0; i < n; ++i) { - // find the degree of buffer[i] - for (e_b = e_p1; (buffer[i] & (1 << e_b)) == 0; --e_b) { - } - - // divide p2 by buffer[i] until the end - for (p2 = (buffer[i] << ((e_p2 = e_p1) - e_b)) ^ p1; p2 >= buffer[i]; - p2 = (buffer[i] << (e_p2 - e_b)) ^ p2) { - for (; (p2 & (1 << e_p2)) == 0; --e_p2) { - } - } // compute new degree of p2 - - // division without remainder!!! p1 is not irreducible - if (p2 == 0) { - break; - } - } - - // all divisions were with remainder - p1 is irreducible - if (p2 != 0) { - e_p2 = 0; - - if (primitive) { - // check that p1 has only one cycle (i.e. is monic, or primitive) - j = ~(0xffffffff << (e_p1 + 1)); - e_b = (1 << e_p1) | 0x1; - - for (p2 = e_b, e_p2 = (1 << e_p1) - 2; e_p2 > 0; --e_p2) { - p2 <<= 1; - i = p2 & p1; - i = (i & 0x55555555) + ((i >> 1) & 0x55555555); - i = (i & 0x33333333) + ((i >> 2) & 0x33333333); - i = (i & 0x07070707) + ((i >> 4) & 0x07070707); - p2 |= (i % 255) & 1; - - if ((p2 & j) == e_b) - break; - } - } - - // it is monic - add it to the list of polynomials - if (e_p2 == 0) { - buffer[n] = p1; - l += e_p1; - break; - } - } - } - } - - return l + 1; -} - -//////////////////////////////////////////////////////////////////////////////// -// @misc{Bratley92:LDS, -// author = "B. Fox and P. Bratley and H. Niederreiter", -// title = "Implementation and test of low discrepancy sequences", -// text = "B. L. Fox, P. Bratley, and H. Niederreiter. Implementation and -// test of -// low discrepancy sequences. ACM Trans. Model. Comput. Simul., -// 2(3):195--213, -// July 1992.", -// year = "1992" } -//////////////////////////////////////////////////////////////////////////////// - -static void GenerateCJ() -{ - int buffer[QRNG_DIMENSIONS]; - int *polynomials; - int n, p1, l, e_p1; - - // Niederreiter (in contrast to Sobol) allows to use not primitive, but just - // irreducible polynomials - l = GeneratePolynomials(buffer, false); - - // convert all polynomials from buffer to polynomials table - polynomials = new int[l + 2 * QRNG_DIMENSIONS + 1]; - - for (n = 0, l = 0; n < QRNG_DIMENSIONS; ++n) { - // find degree of polynomial p1 - for (p1 = buffer[n], e_p1 = 30; (p1 & (1 << e_p1)) == 0; --e_p1) { - } - - // fill polynomials table with values for this polynomial - polynomials[l++] = 1; - - for (--e_p1; e_p1 >= 0; --e_p1) { - polynomials[l++] = (p1 >> e_p1) & 1; - } - - polynomials[l++] = -1; - } - - polynomials[l] = -1; - - // irreducible polynomial p - int *p = polynomials, e, d; - - // polynomial b - int b_arr[1024], *b, m; - - // v array - int v_arr[1024], *v; - - // temporary polynomial, required to do multiplication of p and b - int t_arr[1024], *t; - - // subsidiary variables - int i, j, u, m1, ip, it; - - // cycle over monic irreducible polynomials - for (d = 0; p[0] != -1; p += e + 2) { - // allocate memory for cj array for dimension (ip + 1) - for (i = 0; i < 63; ++i) { - cjn[i][d] = 0; - } - - // determine the power of irreducible polynomial - for (e = 0; p[e + 1] != -1; ++e) { - } - - // polynomial b in the beginning is just '1' - (b = b_arr + 1023)[m = 0] = 1; - - // v array needs only (63 + e - 2) length - v = v_arr + 1023 - (63 + e - 2); - - // cycle over all coefficients - for (j = 63 - 1, u = e; j >= 0; --j, ++u) { - if (u == e) { - u = 0; - - // multiply b by p (polynomials multiplication) - for (i = 0, t = t_arr + 1023 - (m1 = m); i <= m; ++i) { - t[i] = b[i]; - } - - b = b_arr + 1023 - (m += e); - - for (i = 0; i <= m; ++i) { - b[i] = 0; - - for (ip = e - (m - i), it = m1; ip <= e && it >= 0; ++ip, --it) { - if (ip >= 0) { - b[i] ^= p[ip] & t[it]; - } - } - } - - // multiplication of polynomials finished - // calculate v - for (i = 0; i < m1; ++i) { - v[i] = 0; - } - - for (; i < m; ++i) { - v[i] = 1; - } - - for (; i <= 63 + e - 2; ++i) { - v[i] = 0; - for (it = 1; it <= m; ++it) { - v[i] ^= v[i - it] & b[it]; - } - } - } - - // copy calculated v to cj - for (i = 0; i < 63; i++) { - cjn[i][d] |= (INT64)v[i + u] << j; - } - } - - ++d; - } - - delete[] polynomials; -} - -// Generate 63-bit quasirandom number for given index and dimension and -// normalize - -extern "C" double getQuasirandomValue63(INT64 i, int dim) -{ - const double INT63_SCALE = (1.0 / (double)0x8000000000000001ULL); - INT64 result = 0; - - for (int bit = 0; bit < 63; bit++, i >>= 1) - if (i & 1) - result ^= cjn[bit][dim]; - - return (double)(result + 1) * INT63_SCALE; -} - -//////////////////////////////////////////////////////////////////////////////// -// Initialization (table setup) -//////////////////////////////////////////////////////////////////////////////// - -extern "C" void initQuasirandomGenerator(unsigned int table[QRNG_DIMENSIONS][QRNG_RESOLUTION]) -{ - GenerateCJ(); - - for (int dim = 0; dim < QRNG_DIMENSIONS; dim++) - for (int bit = 0; bit < QRNG_RESOLUTION; bit++) - table[dim][bit] = (int)((cjn[bit][dim] >> 32) & 0x7FFFFFFF); -} - -//////////////////////////////////////////////////////////////////////////////// -// Generate 31-bit quasirandom number for given index and dimension -//////////////////////////////////////////////////////////////////////////////// -extern "C" float getQuasirandomValue(unsigned int table[QRNG_DIMENSIONS][QRNG_RESOLUTION], int i, int dim) -{ - int result = 0; - - for (int bit = 0; bit < QRNG_RESOLUTION; bit++, i >>= 1) - if (i & 1) - result ^= table[dim][bit]; - - return (float)(result + 1) * INT_SCALE; -} - -//////////////////////////////////////////////////////////////////////////////// -// Moro's Inverse Cumulative Normal Distribution function approximation -//////////////////////////////////////////////////////////////////////////////// -extern "C" double MoroInvCNDcpu(unsigned int x) -{ - const double a1 = 2.50662823884; - const double a2 = -18.61500062529; - const double a3 = 41.39119773534; - const double a4 = -25.44106049637; - const double b1 = -8.4735109309; - const double b2 = 23.08336743743; - const double b3 = -21.06224101826; - const double b4 = 3.13082909833; - const double c1 = 0.337475482272615; - const double c2 = 0.976169019091719; - const double c3 = 0.160797971491821; - const double c4 = 2.76438810333863E-02; - const double c5 = 3.8405729373609E-03; - const double c6 = 3.951896511919E-04; - const double c7 = 3.21767881768E-05; - const double c8 = 2.888167364E-07; - const double c9 = 3.960315187E-07; - - double z; - - bool negate = false; - - // Ensure the conversion to floating point will give a value in the - // range (0,0.5] by restricting the input to the bottom half of the - // input domain. We will later reflect the result if the input was - // originally in the top half of the input domain - - if (x >= 0x80000000UL) { - x = 0xffffffffUL - x; - negate = true; - } - - // x is now in the range [0,0x80000000) (i.e. [0,0x7fffffff]) - // Convert to floating point in (0,0.5] - const double x1 = 1.0 / static_cast(0xffffffffUL); - const double x2 = x1 / 2.0; - double p1 = x * x1 + x2; - - // Convert to floating point in (-0.5,0] - double p2 = p1 - 0.5; - - // The input to the Moro inversion is p2 which is in the range - // (-0.5,0]. This means that our output will be the negative side - // of the bell curve (which we will reflect if "negate" is true). - - // Main body of the bell curve for |p| < 0.42 - if (p2 > -0.42) { - z = p2 * p2; - z = p2 * (((a4 * z + a3) * z + a2) * z + a1) / ((((b4 * z + b3) * z + b2) * z + b1) * z + 1.0); - } - // Special case (Chebychev) for tail - else { - z = log(-log(p1)); - z = -(c1 + z * (c2 + z * (c3 + z * (c4 + z * (c5 + z * (c6 + z * (c7 + z * (c8 + z * c9)))))))); - } - - // If the original input (x) was in the top half of the range, reflect - // to get the positive side of the bell curve - return negate ? -z : z; -} diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gpu.cuh b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gpu.cuh deleted file mode 100644 index ca100d30..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_gpu.cuh +++ /dev/null @@ -1,108 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef QUASIRANDOMGENERATOR_GPU_CUH -#define QUASIRANDOMGENERATOR_GPU_CUH - -#include - -#include "quasirandomGenerator_common.h" - -// Fast integer multiplication -#define MUL(a, b) __umul24(a, b) - -// Global variables for nvrtc outputs -char *cubin; -size_t cubinSize; -CUmodule module; - -//////////////////////////////////////////////////////////////////////////////// -// GPU code -//////////////////////////////////////////////////////////////////////////////// - -//////////////////////////////////////////////////////////////////////////////// -// Niederreiter quasirandom number generation kernel -//////////////////////////////////////////////////////////////////////////////// - -// Table initialization routine -void initTableGPU(unsigned int tableCPU[QRNG_DIMENSIONS][QRNG_RESOLUTION]) -{ - CUdeviceptr c_Table; - checkCudaErrors(cuModuleGetGlobal(&c_Table, NULL, module, "c_Table")); - checkCudaErrors(cuMemcpyHtoD(c_Table, tableCPU, QRNG_DIMENSIONS * QRNG_RESOLUTION * sizeof(unsigned int))); -} - -// Host-side interface -void quasirandomGeneratorGPU(CUdeviceptr d_Output, unsigned int seed, unsigned int N) -{ - dim3 threads(128, QRNG_DIMENSIONS); - dim3 cudaGridSize(128, 1, 1); - - CUfunction kernel_addr; - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "quasirandomGeneratorKernel")); - - void *args[] = {(void *)&d_Output, (void *)&seed, (void *)&N}; - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - threads.x, - threads.y, - threads.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &args[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); -} - -void inverseCNDgpu(CUdeviceptr d_Output, unsigned int N) -{ - dim3 threads(128, 1, 1); - dim3 cudaGridSize(128, 1, 1); - - CUfunction kernel_addr; - checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "inverseCNDKernel")); - - void *args[] = {(void *)&d_Output, (void *)&N}; - checkCudaErrors(cuLaunchKernel(kernel_addr, - cudaGridSize.x, - cudaGridSize.y, - cudaGridSize.z, /* grid dim */ - threads.x, - threads.y, - threads.z, /* block dim */ - 0, - 0, /* shared mem, stream */ - &args[0], /* arguments */ - 0)); - - checkCudaErrors(cuCtxSynchronize()); -} - -#endif diff --git a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_kernel.cu b/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_kernel.cu deleted file mode 100644 index 3b445c31..00000000 --- a/cpp/5_Domain_Specific/quasirandomGenerator_nvrtc/quasirandomGenerator_kernel.cu +++ /dev/null @@ -1,157 +0,0 @@ -/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of NVIDIA CORPORATION nor the names of its - * contributors may be used to endorse or promote products derived - * from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY - * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR - * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR - * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, - * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, - * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR - * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY - * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - */ - -#ifndef QUASIRANDOMGENERATOR_KERNEL_CUH -#define QUASIRANDOMGENERATOR_KERNEL_CUH - -#include "quasirandomGenerator_common.h" - -// Fast integer multiplication -#define MUL(a, b) __umul24(a, b) - -//////////////////////////////////////////////////////////////////////////////// -// Niederreiter quasirandom number generation kernel -//////////////////////////////////////////////////////////////////////////////// -__constant__ unsigned int c_Table[QRNG_DIMENSIONS][QRNG_RESOLUTION]; - -extern "C" __global__ void quasirandomGeneratorKernel(float *d_Output, unsigned int seed, unsigned int N) -{ - unsigned int *dimBase = &c_Table[threadIdx.y][0]; - unsigned int tid = MUL(blockDim.x, blockIdx.x) + threadIdx.x; - unsigned int threadN = MUL(blockDim.x, gridDim.x); - - for (unsigned int pos = tid; pos < N; pos += threadN) { - unsigned int result = 0; - unsigned int data = seed + pos; - - for (int bit = 0; bit < QRNG_RESOLUTION; bit++, data >>= 1) - if (data & 1) { - result ^= dimBase[bit]; - } - - d_Output[MUL(threadIdx.y, N) + pos] = (float)(result + 1) * INT_SCALE; - } -} - -//////////////////////////////////////////////////////////////////////////////// -// Moro's Inverse Cumulative Normal Distribution function approximation -//////////////////////////////////////////////////////////////////////////////// -__device__ inline float MoroInvCNDgpu(unsigned int x) -{ - const float a1 = 2.50662823884f; - const float a2 = -18.61500062529f; - const float a3 = 41.39119773534f; - const float a4 = -25.44106049637f; - const float b1 = -8.4735109309f; - const float b2 = 23.08336743743f; - const float b3 = -21.06224101826f; - const float b4 = 3.13082909833f; - const float c1 = 0.337475482272615f; - const float c2 = 0.976169019091719f; - const float c3 = 0.160797971491821f; - const float c4 = 2.76438810333863E-02f; - const float c5 = 3.8405729373609E-03f; - const float c6 = 3.951896511919E-04f; - const float c7 = 3.21767881768E-05f; - const float c8 = 2.888167364E-07f; - const float c9 = 3.960315187E-07f; - - float z; - - bool negate = false; - - // Ensure the conversion to floating point will give a value in the - // range (0,0.5] by restricting the input to the bottom half of the - // input domain. We will later reflect the result if the input was - // originally in the top half of the input domain - if (x >= 0x80000000UL) { - x = 0xffffffffUL - x; - negate = true; - } - - // x is now in the range [0,0x80000000) (i.e. [0,0x7fffffff]) - // Convert to floating point in (0,0.5] - const float x1 = 1.0f / static_cast(0xffffffffUL); - const float x2 = x1 / 2.0f; - float p1 = x * x1 + x2; - // Convert to floating point in (-0.5,0] - float p2 = p1 - 0.5f; - - // The input to the Moro inversion is p2 which is in the range - // (-0.5,0]. This means that our output will be the negative side - // of the bell curve (which we will reflect if "negate" is true). - - // Main body of the bell curve for |p| < 0.42 - if (p2 > -0.42f) { - z = p2 * p2; - z = p2 * (((a4 * z + a3) * z + a2) * z + a1) / ((((b4 * z + b3) * z + b2) * z + b1) * z + 1.0f); - } - // Special case (Chebychev) for tail - else { - z = __logf(-__logf(p1)); - z = -(c1 + z * (c2 + z * (c3 + z * (c4 + z * (c5 + z * (c6 + z * (c7 + z * (c8 + z * c9)))))))); - } - - // If the original input (x) was in the top half of the range, reflect - // to get the positive side of the bell curve - - return negate ? -z : z; -} - -//////////////////////////////////////////////////////////////////////////////// -// Main kernel. Choose between transforming -// input sequence and uniform ascending (0, 1) sequence -//////////////////////////////////////////////////////////////////////////////// - -extern "C" __global__ void inverseCNDKernel(float *d_Output, unsigned int pathN) -{ - unsigned int distance = ((unsigned int)-1) / (pathN + 1); - unsigned int tid = MUL(blockDim.x, blockIdx.x) + threadIdx.x; - unsigned int threadN = MUL(blockDim.x, gridDim.x); - - // Transform input number sequence if it's supplied - if (0) // d_Input) - { - /* - for (unsigned int pos = tid; pos < pathN; pos += threadN) - { - unsigned int d = d_Input[pos]; - d_Output[pos] = (float)MoroInvCNDgpu(d); - } - */ - } - // Else generate input uniformly placed samples on the fly - // and write to destination - else { - for (unsigned int pos = tid; pos < pathN; pos += threadN) { - unsigned int d = (pos + 1) * distance; - d_Output[pos] = (float)MoroInvCNDgpu(d); - } - } -} - -#endif diff --git a/cpp/5_Domain_Specific/recursiveGaussian/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/recursiveGaussian/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/recursiveGaussian/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/recursiveGaussian/.vscode/extensions.json b/cpp/5_Domain_Specific/recursiveGaussian/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/recursiveGaussian/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/recursiveGaussian/CMakeLists.txt b/cpp/5_Domain_Specific/recursiveGaussian/CMakeLists.txt index f62df8d2..a61a4837 100644 --- a/cpp/5_Domain_Specific/recursiveGaussian/CMakeLists.txt +++ b/cpp/5_Domain_Specific/recursiveGaussian/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(recursiveGaussian LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for recursiveGaussian add_executable(recursiveGaussian recursiveGaussian_cuda.cu recursiveGaussian.cpp) - target_compile_options(recursiveGaussian PRIVATE $<$:--extended-lambda>) - target_compile_features(recursiveGaussian PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(recursiveGaussian PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(recursiveGaussian PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(recursiveGaussian ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET recursiveGaussian POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(recursiveGaussian ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET recursiveGaussian POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET recursiveGaussian POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'recursiveGaussian'") endif() else() message(STATUS "OpenGL not found - will not build sample 'recursiveGaussian'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleD3D11/CMakeLists.txt b/cpp/5_Domain_Specific/simpleD3D11/CMakeLists.txt index 713ce7ea..49c33c99 100644 --- a/cpp/5_Domain_Specific/simpleD3D11/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleD3D11/CMakeLists.txt @@ -1,59 +1,44 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleD3D11 LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) - # Source file - # Add target for simpleD3D11 add_executable(simpleD3D11 simpleD3D11.cpp sinewave_cuda.cu ../../../Common/rendercheck_d3d11.cpp ) - target_compile_options(simpleD3D11 PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleD3D11 PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleD3D11 PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleD3D11 PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(simpleD3D11 PRIVATE d3d11 dxgi dxguid d3dcompiler ) - add_custom_command(TARGET simpleD3D11 POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "Sample 'simpleD3D11' is Windows-only - skipping") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleD3D11Texture/CMakeLists.txt b/cpp/5_Domain_Specific/simpleD3D11Texture/CMakeLists.txt index 86c90844..abac7d6b 100644 --- a/cpp/5_Domain_Specific/simpleD3D11Texture/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleD3D11Texture/CMakeLists.txt @@ -1,27 +1,21 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleD3D11Texture LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) - # Source file - # Add target for simpleD3D11Texture add_executable(simpleD3D11Texture simpleD3D11Texture.cpp ../../../Common/rendercheck_d3d11.cpp @@ -29,33 +23,24 @@ if(WIN32) texture_3d.cu texture_cube.cu ) - target_compile_options(simpleD3D11Texture PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleD3D11Texture PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleD3D11Texture PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleD3D11Texture PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(simpleD3D11Texture PRIVATE d3d11 dxgi dxguid d3dcompiler ) - add_custom_command(TARGET simpleD3D11Texture POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "Sample 'simpleD3D11Texture' is Windows-only - skipping") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleD3D12/CMakeLists.txt b/cpp/5_Domain_Specific/simpleD3D12/CMakeLists.txt index 7291c8db..0b79baec 100644 --- a/cpp/5_Domain_Specific/simpleD3D12/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleD3D12/CMakeLists.txt @@ -1,27 +1,21 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleD3D12 LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) - # Source file - # Add target for simpleD3D12 add_executable(simpleD3D12 WIN32 DX12CudaSample.cpp Main.cpp @@ -30,34 +24,24 @@ if(WIN32) stdafx.cpp sinewave_cuda.cu ) - target_compile_options(simpleD3D12 PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleD3D12 PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleD3D12 PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleD3D12 PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(simpleD3D12 PRIVATE d3d12 dxgi dxguid d3dcompiler ) - add_custom_command(TARGET simpleD3D12 POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/shaders.hlsl ${CMAKE_CURRENT_BINARY_DIR}/shaders.hlsl ) - + include(InstallSamples) + setup_samples_install() else() message(STATUS "Sample 'simpleD3D12' is Windows-only - skipping") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleGL/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/simpleGL/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/simpleGL/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/simpleGL/.vscode/extensions.json b/cpp/5_Domain_Specific/simpleGL/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/simpleGL/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/simpleGL/CMakeLists.txt b/cpp/5_Domain_Specific/simpleGL/CMakeLists.txt index 782a4737..3c46652f 100644 --- a/cpp/5_Domain_Specific/simpleGL/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleGL/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleGL LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,42 +29,31 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for simpleGL add_executable(simpleGL simpleGL.cu) - target_compile_options(simpleGL PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleGL PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleGL PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleGL PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(simpleGL ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - if(WIN32) target_link_libraries(simpleGL ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET simpleGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET simpleGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E @@ -76,20 +61,16 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - add_custom_command(TARGET simpleGL POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'simpleGL'") endif() else() message(STATUS "OpenGL not found - will not build sample 'simpleGL'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleVulkan/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/simpleVulkan/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/simpleVulkan/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/simpleVulkan/.vscode/extensions.json b/cpp/5_Domain_Specific/simpleVulkan/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/simpleVulkan/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/simpleVulkan/CMakeLists.txt b/cpp/5_Domain_Specific/simpleVulkan/CMakeLists.txt index 14624790..92b5ee82 100644 --- a/cpp/5_Domain_Specific/simpleVulkan/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleVulkan/CMakeLists.txt @@ -1,28 +1,23 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleVulkan LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(Vulkan) find_package(OpenGL) - # Include the check_include_file macro include(CheckIncludeFile) @@ -52,19 +47,12 @@ if(WIN32 OR CMAKE_CROSSCOMPILING OR SUSE_LINUX) endif() endif() -# Source file if(${Vulkan_FOUND}) if(${OPENGL_FOUND}) if(${HAVE_GLFW3_H}) - # Add target for simpleVulkan add_executable(simpleVulkan main.cpp SineWaveSimulation.cu VulkanBaseApp.cpp) - target_compile_options(simpleVulkan PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleVulkan PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleVulkan PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleVulkan PUBLIC ${Vulkan_INCLUDE_DIRS} ${CUDAToolkit_INCLUDE_DIRS} @@ -99,6 +87,8 @@ if(${Vulkan_FOUND}) ${CMAKE_CURRENT_SOURCE_DIR}/frag.spv ${CMAKE_CURRENT_BINARY_DIR} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "glfw3 not found - will not build sample 'simpleVulkan'") endif() @@ -108,7 +98,3 @@ if(${Vulkan_FOUND}) else() message(STATUS "Vulkan not found - will not build sample 'simpleVulkan'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/extensions.json b/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/simpleVulkanMMAP/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/simpleVulkanMMAP/CMakeLists.txt b/cpp/5_Domain_Specific/simpleVulkanMMAP/CMakeLists.txt index cc185068..9990cf49 100644 --- a/cpp/5_Domain_Specific/simpleVulkanMMAP/CMakeLists.txt +++ b/cpp/5_Domain_Specific/simpleVulkanMMAP/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleVulkanMMAP LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(Vulkan) @@ -51,19 +47,12 @@ if(WIN32 OR CMAKE_CROSSCOMPILING OR SUSE_LINUX) endif() endif() -# Source file if(${Vulkan_FOUND}) if(${OPENGL_FOUND}) if(${HAVE_GLFW3_H}) - # Add target for simpleVulkanMMAP add_executable(simpleVulkanMMAP ../../../Common/helper_multiprocess.cpp MonteCarloPi.cu VulkanBaseApp.cpp main.cpp) - target_compile_options(simpleVulkanMMAP PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleVulkanMMAP PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleVulkanMMAP PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleVulkanMMAP PUBLIC ${Vulkan_INCLUDE_DIRS} ${CUDAToolkit_INCLUDE_DIRS} @@ -99,6 +88,8 @@ if(${Vulkan_FOUND}) ${CMAKE_CURRENT_SOURCE_DIR}/frag.spv ${CMAKE_CURRENT_BINARY_DIR} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "glfw3 not found - will not build sample 'simpleVulkanMMAP'") endif() @@ -108,7 +99,3 @@ if(${Vulkan_FOUND}) else() message(STATUS "Vulkan not found - will not build sample 'simpleVulkanMMAP'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/smokeParticles/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/smokeParticles/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/smokeParticles/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/smokeParticles/.vscode/extensions.json b/cpp/5_Domain_Specific/smokeParticles/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/smokeParticles/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/smokeParticles/CMakeLists.txt b/cpp/5_Domain_Specific/smokeParticles/CMakeLists.txt index ccee88f8..672f6ccf 100644 --- a/cpp/5_Domain_Specific/smokeParticles/CMakeLists.txt +++ b/cpp/5_Domain_Specific/smokeParticles/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(smokeParticles LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for smokeParticles add_executable(smokeParticles GLSLProgram.cpp ParticleSystem.cpp ParticleSystem_cuda.cu SmokeRenderer.cpp SmokeShaders.cpp framebufferObject.cpp particleDemo.cpp renderbuffer.cpp) - target_compile_options(smokeParticles PRIVATE $<$:--extended-lambda>) - target_compile_features(smokeParticles PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(smokeParticles PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(smokeParticles PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(smokeParticles ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET smokeParticles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(smokeParticles ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET smokeParticles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET smokeParticles POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'smokeParticles'") endif() else() message(STATUS "OpenGL not found - will not build sample 'smokeParticles'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/stereoDisparity/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/stereoDisparity/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/stereoDisparity/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/stereoDisparity/.vscode/extensions.json b/cpp/5_Domain_Specific/stereoDisparity/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/stereoDisparity/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/stereoDisparity/CMakeLists.txt b/cpp/5_Domain_Specific/stereoDisparity/CMakeLists.txt index ad2ff7be..41c1c064 100644 --- a/cpp/5_Domain_Specific/stereoDisparity/CMakeLists.txt +++ b/cpp/5_Domain_Specific/stereoDisparity/CMakeLists.txt @@ -1,40 +1,31 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(stereoDisparity LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for stereoDisparity add_executable(stereoDisparity stereoDisparity.cu) target_compile_options(stereoDisparity PRIVATE $<$:--extended-lambda>) target_compile_features(stereoDisparity PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(stereoDisparity PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - add_custom_command(TARGET stereoDisparity POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/5_Domain_Specific/volumeFiltering/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/volumeFiltering/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/volumeFiltering/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/volumeFiltering/.vscode/extensions.json b/cpp/5_Domain_Specific/volumeFiltering/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/volumeFiltering/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/volumeFiltering/CMakeLists.txt b/cpp/5_Domain_Specific/volumeFiltering/CMakeLists.txt index 639b4f1e..7b06ae9b 100644 --- a/cpp/5_Domain_Specific/volumeFiltering/CMakeLists.txt +++ b/cpp/5_Domain_Specific/volumeFiltering/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(volumeFiltering LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for volumeFiltering add_executable(volumeFiltering volume.cpp volumeFilter_kernel.cu volumeFiltering.cpp volumeRender_kernel.cu) - target_compile_options(volumeFiltering PRIVATE $<$:--extended-lambda>) - target_compile_features(volumeFiltering PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(volumeFiltering PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(volumeFiltering PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(volumeFiltering ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET volumeFiltering POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(volumeFiltering ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET volumeFiltering POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET volumeFiltering POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'volumeFiltering'") endif() else() message(STATUS "OpenGL not found - will not build sample 'volumeFiltering'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/volumeRender/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/volumeRender/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/volumeRender/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/volumeRender/.vscode/extensions.json b/cpp/5_Domain_Specific/volumeRender/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/volumeRender/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/volumeRender/CMakeLists.txt b/cpp/5_Domain_Specific/volumeRender/CMakeLists.txt index bd9928ec..fcd77b14 100644 --- a/cpp/5_Domain_Specific/volumeRender/CMakeLists.txt +++ b/cpp/5_Domain_Specific/volumeRender/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(volumeRender LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) if(WIN32) @@ -33,48 +29,36 @@ endif() find_package(OpenGL) find_package(GLUT) -# Source file if(${OpenGL_FOUND}) if (${GLUT_FOUND}) - # Add target for volumeRender add_executable(volumeRender volumeRender_kernel.cu volumeRender.cpp) - target_compile_options(volumeRender PRIVATE $<$:--extended-lambda>) - target_compile_features(volumeRender PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(volumeRender PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(volumeRender PUBLIC ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ${GLUT_INCLUDE_DIRS} ) - target_link_libraries(volumeRender ${OPENGL_LIBRARIES} ${GLUT_LIBRARIES} ) - add_custom_command(TARGET volumeRender POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/data ${CMAKE_CURRENT_BINARY_DIR}/data ) - if(WIN32) target_link_libraries(volumeRender ${PC_GLUT_LIBRARY_DIRS}/freeglut.lib ${PC_GLUT_LIBRARY_DIRS}/${GLEW_LIB_NAME}.lib ) - add_custom_command(TARGET volumeRender POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${CMAKE_CURRENT_SOURCE_DIR}/../../../bin/win64/$/freeglut.dll ${CMAKE_CURRENT_BINARY_DIR}/$ ) - add_custom_command(TARGET volumeRender POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy @@ -82,14 +66,11 @@ if(${OpenGL_FOUND}) ${CMAKE_CURRENT_BINARY_DIR}/$ ) endif() - + include(InstallSamples) + setup_samples_install() else() message(STATUS "GLUT not found - will not build sample 'volumeRender'") endif() else() message(STATUS "OpenGL not found - will not build sample 'volumeRender'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/c_cpp_properties.json b/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/extensions.json b/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/5_Domain_Specific/vulkanImageCUDA/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/5_Domain_Specific/vulkanImageCUDA/CMakeLists.txt b/cpp/5_Domain_Specific/vulkanImageCUDA/CMakeLists.txt index c7dcd993..68bf475e 100644 --- a/cpp/5_Domain_Specific/vulkanImageCUDA/CMakeLists.txt +++ b/cpp/5_Domain_Specific/vulkanImageCUDA/CMakeLists.txt @@ -1,22 +1,18 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(vulkanImageCUDA LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) find_package(Vulkan) @@ -51,19 +47,12 @@ if(WIN32 OR CMAKE_CROSSCOMPILING OR SUSE_LINUX) endif() endif() -# Source file if(${Vulkan_FOUND}) if(${OPENGL_FOUND}) if(${HAVE_GLFW3_H}) - # Add target for vulkanImageCUDA add_executable(vulkanImageCUDA vulkanImageCUDA.cu) - target_compile_options(vulkanImageCUDA PRIVATE $<$:--extended-lambda>) - target_compile_features(vulkanImageCUDA PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(vulkanImageCUDA PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(vulkanImageCUDA PUBLIC ${Vulkan_INCLUDE_DIRS} ${CUDAToolkit_INCLUDE_DIRS} @@ -99,6 +88,8 @@ if(${Vulkan_FOUND}) ${CMAKE_CURRENT_SOURCE_DIR}/teapot1024.ppm ${CMAKE_CURRENT_BINARY_DIR} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "glfw3 not found - will not build sample 'vulkanImageCUDA'") endif() @@ -108,7 +99,3 @@ if(${Vulkan_FOUND}) else() message(STATUS "Vulkan not found - will not build sample 'vulkanImageCUDA'") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/6_Performance/LargeKernelParameter/.vscode/c_cpp_properties.json b/cpp/6_Performance/LargeKernelParameter/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/6_Performance/LargeKernelParameter/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/6_Performance/LargeKernelParameter/.vscode/extensions.json b/cpp/6_Performance/LargeKernelParameter/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/6_Performance/LargeKernelParameter/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/6_Performance/LargeKernelParameter/CMakeLists.txt b/cpp/6_Performance/LargeKernelParameter/CMakeLists.txt index 81317224..34030401 100644 --- a/cpp/6_Performance/LargeKernelParameter/CMakeLists.txt +++ b/cpp/6_Performance/LargeKernelParameter/CMakeLists.txt @@ -1,35 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(LargeKernelParameter LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for LargeKernelParameter add_executable(LargeKernelParameter LargeKernelParameter.cu) target_compile_options(LargeKernelParameter PRIVATE $<$:--extended-lambda>) target_compile_features(LargeKernelParameter PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(LargeKernelParameter PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/6_Performance/UnifiedMemoryPerf/.vscode/c_cpp_properties.json b/cpp/6_Performance/UnifiedMemoryPerf/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/6_Performance/UnifiedMemoryPerf/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/6_Performance/UnifiedMemoryPerf/.vscode/extensions.json b/cpp/6_Performance/UnifiedMemoryPerf/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/6_Performance/UnifiedMemoryPerf/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/6_Performance/UnifiedMemoryPerf/CMakeLists.txt b/cpp/6_Performance/UnifiedMemoryPerf/CMakeLists.txt index 9ebabf27..b44c2fa7 100644 --- a/cpp/6_Performance/UnifiedMemoryPerf/CMakeLists.txt +++ b/cpp/6_Performance/UnifiedMemoryPerf/CMakeLists.txt @@ -1,38 +1,29 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(UnifiedMemoryPerf LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for UnifiedMemoryPerf add_executable(UnifiedMemoryPerf helperFunctions.cpp matrixMultiplyPerf.cu commonKernels.cu) target_compile_options(UnifiedMemoryPerf PRIVATE $<$:--extended-lambda>) target_compile_features(UnifiedMemoryPerf PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(UnifiedMemoryPerf PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(UnifiedMemoryPerf PRIVATE ${CUDAToolkit_INCLUDE_DIRS} ) -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/6_Performance/alignedTypes/.vscode/c_cpp_properties.json b/cpp/6_Performance/alignedTypes/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/6_Performance/alignedTypes/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/6_Performance/alignedTypes/.vscode/extensions.json b/cpp/6_Performance/alignedTypes/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/6_Performance/alignedTypes/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/6_Performance/alignedTypes/CMakeLists.txt b/cpp/6_Performance/alignedTypes/CMakeLists.txt index fc74c459..dd0ec46c 100644 --- a/cpp/6_Performance/alignedTypes/CMakeLists.txt +++ b/cpp/6_Performance/alignedTypes/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(alignedTypes LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for alignedTypes add_executable(alignedTypes alignedTypes.cu) target_compile_options(alignedTypes PRIVATE $<$:--extended-lambda>) target_compile_features(alignedTypes PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(alignedTypes PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/c_cpp_properties.json b/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/extensions.json b/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/6_Performance/cudaGraphsPerfScaling/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/6_Performance/cudaGraphsPerfScaling/CMakeLists.txt b/cpp/6_Performance/cudaGraphsPerfScaling/CMakeLists.txt index 0a89460c..63f2dad6 100644 --- a/cpp/6_Performance/cudaGraphsPerfScaling/CMakeLists.txt +++ b/cpp/6_Performance/cudaGraphsPerfScaling/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaGraphsPerfScaling LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for cudaGraphsPerfScaling add_executable(cudaGraphsPerfScaling cudaGraphPerfScaling.cu) target_compile_options(cudaGraphsPerfScaling PRIVATE $<$:--extended-lambda>) target_compile_features(cudaGraphsPerfScaling PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(cudaGraphsPerfScaling PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/6_Performance/transpose/.vscode/c_cpp_properties.json b/cpp/6_Performance/transpose/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/6_Performance/transpose/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/6_Performance/transpose/.vscode/extensions.json b/cpp/6_Performance/transpose/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/6_Performance/transpose/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/6_Performance/transpose/CMakeLists.txt b/cpp/6_Performance/transpose/CMakeLists.txt index 5c92d03c..08b82280 100644 --- a/cpp/6_Performance/transpose/CMakeLists.txt +++ b/cpp/6_Performance/transpose/CMakeLists.txt @@ -1,34 +1,25 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(transpose LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 75 80 86 87 89 90 100 110 120) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../Common) -# Source file -# Add target for transpose add_executable(transpose transpose.cu) target_compile_options(transpose PRIVATE $<$:--extended-lambda>) target_compile_features(transpose PRIVATE cxx_std_17 cuda_std_17) -set_target_properties(transpose PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/InstallSamples.cmake) +include(InstallSamples) setup_samples_install() diff --git a/cpp/7_libNVVM/CMakeLists.txt b/cpp/7_libNVVM/CMakeLists.txt index 83605b83..0e3c376f 100644 --- a/cpp/7_libNVVM/CMakeLists.txt +++ b/cpp/7_libNVVM/CMakeLists.txt @@ -71,8 +71,8 @@ find_file(LIBNVVM_HOME nvvm PATHS "$ENV{LIBNVVM_HOME}" "${CUDA_HOME}") message(STATUS "Using LIBNVVM_HOME: ${LIBNVVM_HOME}") # Find libNVVM and nvvm.h. -# (Linux: nvvm/lib64, windows: nvvm/lib/x64) -find_library(NVVM_LIB nvvm PATHS "${LIBNVVM_HOME}/lib64" "${LIBNVVM_HOME}/lib/x64") +# (Linux: nvvm/lib64, windows: nvvm/lib/x64, windows on arm:nvvm/lib/arm64) +find_library(NVVM_LIB nvvm PATHS "${LIBNVVM_HOME}/lib64" "${LIBNVVM_HOME}/lib/x64" "${LIBNVVM_HOME}/lib/arm64") find_file(NVVM_H nvvm.h PATH "${LIBNVVM_HOME}/include") get_filename_component(NVVM_INCLUDE_DIR ${NVVM_H} DIRECTORY) include_directories(${NVVM_INCLUDE_DIR}) @@ -110,7 +110,9 @@ include_directories("${CMAKE_CURRENT_SOURCE_DIR}/common/include") # invocation. See the note about "cuda-c-linking" in README.md. if (ENABLE_CUDA_C_LINKING_SAMPLE) # Include the LLVM dev package which is required to build cuda-c-linking. - find_package(LLVM CONFIG PATHS "$ENV{LLVM_HOME}") + # LLVM_HOME is a HINTS entry rather than a PATHS one so that it is searched + # before the system directories, letting it select among installed LLVMs. + find_package(LLVM CONFIG HINTS "$ENV{LLVM_HOME}") if (LLVM_FOUND) add_subdirectory(cuda-c-linking) else () diff --git a/cpp/7_libNVVM/README.md b/cpp/7_libNVVM/README.md index cdabae0b..25e29750 100644 --- a/cpp/7_libNVVM/README.md +++ b/cpp/7_libNVVM/README.md @@ -71,27 +71,50 @@ update the CMake invocation in utils/build.sh or build.bat by adding A Note About the cuda-c-linking Sample -------------------------------------- -This sample requires a development package (or locally-built) LLVM library -between versions 7 to 14 inclusive. LLVM 15 defaults to using opaque pointers, -which are not supported in libNVVM for pre-Blackwell architectures. +This sample requires a development package (or locally-built) LLVM library, +version 7 or newer. -The LLVM_HOME environment variable is required for users who wish to build the -cuda-c-linking sample and have a locally built copy of LLVM that they wish to -use. That sample requires the development package of LLVM with the LLVM header -files and libraries. +Which LLVM versions are usable depends on the GPU the sample runs on. LLVM 15 +and newer emit opaque pointers, and libNVVM accepts those only for Blackwell +and later architectures; older architectures require the typed pointers that +LLVM 14 and older emit. The sample queries libNVVM for the LLVM IR version its +target accepts, so building against a newer LLVM and running on a pre-Blackwell +device reports the requirement and exits with code 2 rather than failing. Use +LLVM 7 to 14 if you need a single build that runs on any supported device. + +The LLVM_HOME environment variable selects which LLVM to build against. It is +required for users who have a locally built copy of LLVM they wish to use, and +it also picks between installed versions: several LLVM versions install side by +side, so a newer default LLVM does not prevent building this sample against an +older one. With more than one installed and LLVM_HOME unset, which of them is +found is unspecified, so set it explicitly. Whichever copy is used has to be a +development install, with the LLVM header files and libraries. If the LLVM dependencies are met, the user can enable the building of this sample by setting the CMake variable "ENABLE_CUDA_C_LINKING_SAMPLE" from either the command line invocation of CMake or by modifying the CMakeLists.txt in this directory. -Windows users should download LLVM 14 sources from llvm.org and build+install +Windows users should download LLVM sources from llvm.org and build+install LLVM locally. Using the llvm.org provided Windows installer lacks some of the required components the cuda-c-linking sample depends on. -For Ubuntu users, the "llvm-dev" package contains the LLVM headers and libraries -this sample requires, the user should not have to explicitly define an LLVM_HOME -in this case. +For Ubuntu users, the "llvm-dev" package contains the LLVM headers and +libraries this sample requires, and is found without any further configuration. +Note that "llvm-dev" is an unversioned metapackage that tracks the +distribution's current LLVM: on Ubuntu 24.04 it installs "llvm-18-dev", which +runs only on Blackwell and later. To build for an older device, add the +versioned package and select it explicitly: + +```bash +sudo apt install llvm-14-dev +LLVM_HOME=/usr/lib/llvm-14 cmake -S . -B build -DENABLE_CUDA_C_LINKING_SAMPLE=1 +``` + +LLVM 15 and newer reference zstd from their CMake package, which the Ubuntu +"llvm-*-dev" packages do not pull in. Install "libzstd-dev" alongside them, +or CMake fails while loading the LLVM package with a missing +"zstd::libzstd_shared" target. Windows users will want to build this sample using the same CMake build mode as they built LLVM with. For instance if they built LLVM in Release mode, diff --git a/cpp/7_libNVVM/cuda-c-linking/CMakeLists.txt b/cpp/7_libNVVM/cuda-c-linking/CMakeLists.txt index 719b17d1..42b3592c 100644 --- a/cpp/7_libNVVM/cuda-c-linking/CMakeLists.txt +++ b/cpp/7_libNVVM/cuda-c-linking/CMakeLists.txt @@ -25,26 +25,46 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. message(STATUS "Found LLVM Version ${LLVM_PACKAGE_VERSION}") -if (LLVM_PACKAGE_VERSION VERSION_GREATER_EQUAL "15" OR - LLVM_PACKAGE_VERSION VERSION_LESS "7") - message(STATUS "The cuda-c-linking sample is expected to build with " - "LLVM development libraries v7 to v14, opaque pointers are " - "not supported in libNVVM for pre-Blackwell architectures.") +if (LLVM_PACKAGE_VERSION VERSION_LESS "7") + message(STATUS "The cuda-c-linking sample requires LLVM development " + "libraries v7 or newer.") return() endif () +if (LLVM_PACKAGE_VERSION VERSION_GREATER_EQUAL "15") + message(STATUS "LLVM 15 and newer emit opaque pointers, which libNVVM " + "accepts only for Blackwell and later architectures. The " + "cuda-c-linking sample will build, and reports the " + "requirement at run time if the device is older. Build " + "against LLVM 14 or older to run on pre-Blackwell devices.") +endif () add_executable(cuda-c-linking cuda-c-linking.cpp) +# LLVM 16 and newer headers use C++17 constructs, so build the sample as C++17 +# when they are in use. Older LLVM versions keep the compiler default. +if (LLVM_PACKAGE_VERSION VERSION_GREATER_EQUAL "16") + target_compile_features(cuda-c-linking PRIVATE cxx_std_17) +endif () + add_test(NAME cuda-c-linking COMMAND cuda-c-linking WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}") +# The sample exits with 2 when the device cannot accept the IR this build +# emits, which is a requirement it does not meet rather than a failure. +set_tests_properties(cuda-c-linking PROPERTIES SKIP_RETURN_CODE 2) target_link_libraries(cuda-c-linking ${NVVM_LIB} ${CUDA_LIB}) # See https://llvm.org/docs/CMake.html#developing-llvm-passes-out-of-source separate_arguments(LLVM_DEFINITIONS_LIST NATIVE_COMMAND ${LLVM_DEFINITIONS}) add_definitions(${LLVM_DEFINITIONS_LIST}) include_directories(${LLVM_INCLUDE_DIRS}) -llvm_map_components_to_libnames(llvm_libs core support) +# Distributions often ship LLVM only as one shared library instead of the +# per-component static libraries, so link that library when it is in use. +if (LLVM_LINK_LLVM_DYLIB) + set(llvm_libs LLVM) +else () + llvm_map_components_to_libnames(llvm_libs core support) +endif () target_link_libraries(cuda-c-linking ${llvm_libs}) if (UNIX) diff --git a/cpp/7_libNVVM/cuda-c-linking/README.md b/cpp/7_libNVVM/cuda-c-linking/README.md index 3c150354..13ca57f4 100644 --- a/cpp/7_libNVVM/cuda-c-linking/README.md +++ b/cpp/7_libNVVM/cuda-c-linking/README.md @@ -27,6 +27,21 @@ This sample is optionally built as part of the libnvvm samples from the CUDA samples tree. Please see the README file at the root of the libnvvm samples for build instructions. +It requires the LLVM development headers and libraries, version 7 or newer. +LLVM 15 and newer emit opaque pointers, which libNVVM accepts only for +Blackwell and later architectures, so such a build cannot run on an older GPU: + + $ ./cuda-c-linking + Using CUDA Device [0]: NVIDIA L4 + Device Compute Capability: 8.9 + This sample was built against LLVM 18, which emits opaque pointers, but + libNVVM accepts only LLVM 7 IR for compute_89. Build against LLVM 14 or + older to run on this device, or run on a Blackwell or later device. + +The sample exits with code 2 in that case, reporting an unmet requirement +rather than a failure. Building against LLVM 7 to 14 produces a binary that +runs on any device this sample supports. + Usage ----- diff --git a/cpp/7_libNVVM/cuda-c-linking/cuda-c-linking.cpp b/cpp/7_libNVVM/cuda-c-linking/cuda-c-linking.cpp index de7d1be1..ffe0e8e5 100644 --- a/cpp/7_libNVVM/cuda-c-linking/cuda-c-linking.cpp +++ b/cpp/7_libNVVM/cuda-c-linking/cuda-c-linking.cpp @@ -27,6 +27,7 @@ #include #include #include +#include #include #include #include @@ -35,6 +36,9 @@ #include #include #include +#if LLVM_VERSION_MAJOR >= 21 +#include +#endif #include #include #include @@ -67,7 +71,9 @@ static void __checkCudaErrors(CUresult err, const char *filename, int line) ((CUDA_SUCCESS == res) ? ename : "Unknown"), filename, line); - exit(err); + // Exit with EXIT_FAILURE rather than the error code, which 'main' + // reserves to report an unmet requirement. + exit(EXIT_FAILURE); } } @@ -76,10 +82,14 @@ void checkNVVMCall(nvvmResult res) { if (res != NVVM_SUCCESS) { errs() << "libnvvm call failed\n"; - exit(res); + exit(EXIT_FAILURE); } } +// The libNVVM architecture string for a compute capability, e.g. 10.0 becomes +// "compute_100". +std::string computeArch(int devMajor, int devMinor) { return "compute_" + utostr(devMajor) + utostr(devMinor); } + /// generateModule - Generate and LLVM IR module that calls an /// externally-defined function std::unique_ptr generateModule(LLVMContext &context) @@ -89,13 +99,22 @@ std::unique_ptr generateModule(LLVMContext &context) mod->setDataLayout("e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-i128:128:128-" "f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:64-v128:128:128-n16:32:" "64"); +#if LLVM_VERSION_MAJOR >= 21 + // LLVM 21 dropped the overload that takes the triple as a string. + mod->setTargetTriple(Triple("nvptx64-nvidia-cuda")); +#else mod->setTargetTriple("nvptx64-nvidia-cuda"); +#endif // Get pointers to some commonly-used types. - Type *voidTy = Type::getVoidTy(context); - Type *floatTy = Type::getFloatTy(context); - Type *i32Ty = Type::getInt32Ty(context); - Type *floatGenericPtrTy = PointerType::get(floatTy, /* address space */ 0); + Type *voidTy = Type::getVoidTy(context); +#if LLVM_VERSION_MAJOR >= 17 + // Pointers are opaque from LLVM 15 on, and LLVM 17 dropped the overload + // that takes a pointee type. The resulting IR spells this type 'ptr'. + Type *floatGenericPtrTy = PointerType::get(context, /* address space */ 0); +#else + Type *floatGenericPtrTy = PointerType::get(Type::getFloatTy(context), /* address space */ 0); +#endif // void @mandelbrot(float*) Type *mandelbrotParamTys[] = {floatGenericPtrTy}; @@ -139,7 +158,7 @@ std::unique_ptr generateModule(LLVMContext &context) } // Use libNVVM to compile an NVVM IR module to PTX. -std::string generatePtx(const std::string &module, int devMajor, int devMinor, const char *moduleName) +std::string generatePtx(const std::string &module, const std::string &arch, const char *moduleName) { assert(moduleName); @@ -149,9 +168,7 @@ std::string generatePtx(const std::string &module, int devMajor, int devMinor, c // Create a libNVVM compilation unit from the NVVM IR. checkNVVMCall(nvvmAddModuleToProgram(compileUnit, module.c_str(), module.size(), moduleName)); - std::string computeArg = "-arch=compute_"; - computeArg += utostr(devMajor); - computeArg += utostr(devMinor); + std::string computeArg = "-arch=" + arch; // Compile the NVVM IR into PTX. const char *options[] = {computeArg.c_str()}; @@ -216,9 +233,36 @@ int main(int argc, char **argv) checkCudaErrors(cuDeviceGetAttribute(&devMajor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, device)); checkCudaErrors(cuDeviceGetAttribute(&devMinor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, device)); outs() << "Device Compute Capability: " << devMajor << "." << devMinor << "\n"; - if (devMajor < 7 && devMinor < 5) { - errs() << "ERROR: Device 0 is not sm_75 or later.\n"; - return 1; + if (devMajor < 7 || (devMajor == 7 && devMinor < 5)) { + outs() << "Device 0 is not sm_75 or later.\n"; + // 2 reports an unmet hardware/toolchain requirement rather than a failure. + return 2; + } + + const std::string arch = computeArch(devMajor, devMinor); + + // The LLVM IR that libNVVM accepts depends on the target architecture: + // pre-Blackwell targets take LLVM 7 IR, which spells pointers with their + // pointee type ('float*'), while Blackwell and later take a much newer IR + // that also accepts the opaque pointers ('ptr') LLVM 15 and later emit. + // Ask libNVVM which one applies to this device rather than assuming. + int nvvmLLVMMajor = 0; + const nvvmResult archSupported = nvvmLLVMVersion(arch.c_str(), &nvvmLLVMMajor); + if (archSupported == NVVM_ERROR_INVALID_INPUT) { + // The device is newer than the toolkit this sample builds against. + outs() << "The libNVVM of this CUDA toolkit does not support " << arch + << ". Use a toolkit that supports this device.\n"; + // 2 reports an unmet hardware/toolchain requirement rather than a failure. + return 2; + } + checkNVVMCall(archSupported); + if (LLVM_VERSION_MAJOR >= 15 && nvvmLLVMMajor < 15) { + outs() << "This sample was built against LLVM " << LLVM_VERSION_MAJOR + << ", which emits opaque pointers, but libNVVM accepts only LLVM " << nvvmLLVMMajor << " IR for " << arch + << ". Build against LLVM 14 or older to run on this device, or run on a " + "Blackwell or later device.\n"; + // 2 reports an unmet hardware/toolchain requirement rather than a failure. + return 2; } // Generate the IR module @@ -239,7 +283,7 @@ int main(int argc, char **argv) } // Generate PTX. - std::string ptx = generatePtx(moduleStr, devMajor, devMinor, module->getModuleIdentifier().c_str()); + std::string ptx = generatePtx(moduleStr, arch, module->getModuleIdentifier().c_str()); if (SavePTX) { std::error_code err; raw_fd_ostream out("cuda-c-linking.kernel.ptx", err); diff --git a/cpp/7_libNVVM/device-side-launch/README.md b/cpp/7_libNVVM/device-side-launch/README.md index e8949677..d9ef3e9c 100644 --- a/cpp/7_libNVVM/device-side-launch/README.md +++ b/cpp/7_libNVVM/device-side-launch/README.md @@ -10,8 +10,8 @@ This document assumes the CUDA runtime is used. The method for device-side launch using the OpenCL runtime is similar but different. This document is written after the "Device-Side Launch from PTX" -section from CUDA C Programming Guide -(http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#device-side-launch-from-ptx). +section from CUDA Programming Guide +(https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/dynamic-parallelism.html#device-side-launch-from-ptx). Kernel Launch APIs ------------------ diff --git a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/CMakeLists.txt index 32b2a0be..fa563ee4 100644 --- a/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/EGLSync_CUDAEvent_Interop/CMakeLists.txt @@ -1,22 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(EGLSync_CUDAEvent_Interop LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(EGL) @@ -24,26 +21,18 @@ find_package(X11) find_package(OpenGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${OpenGL_FOUND}) if(${EGL_FOUND}) if(${X11_FOUND}) - # Add target for EGLSync_CUDAEvent_Interop add_executable(EGLSync_CUDAEvent_Interop EGLSync_CUDAEvent_Interop.cu) - target_compile_options(EGLSync_CUDAEvent_Interop PRIVATE $<$:--extended-lambda>) - target_compile_features(EGLSync_CUDAEvent_Interop PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(EGLSync_CUDAEvent_Interop PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(EGLSync_CUDAEvent_Interop PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${EGL_INCLUDE_DIR} ${X11_INCLUDE_DIR} ${OPENGL_INCLUDE_DIR} ) - target_link_libraries(EGLSync_CUDAEvent_Interop CUDA::cuda_driver #CUDA::cudart @@ -51,8 +40,10 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") ${X11_LIBRARIES} ${OPENGL_LIBRARIES} ) + include(InstallSamples) + setup_samples_install() else() - message(STATUS "X11 libraries not found - will not build sample 'EGLSyncCUDAEvent_Interop'") + message(STATUS "X11 libraries not found - will not build sample 'EGLSync_CUDAEvent_Interop'") endif() else() message(STATUS "EGL not found - will not build sample 'EGLSync_CUDAEvent_Interop'") @@ -64,7 +55,3 @@ else() message(STATUS "Will not build sample EGLSync_CUDAEvent_Interop - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/CMakeLists.txt index 10580406..3f7a50d4 100644 --- a/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cuDLAErrorReporting/CMakeLists.txt @@ -1,53 +1,39 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cuDLAErrorReporting LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PATH}) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(CUDLA_LIB) - # Source file - # Add target for cuDLAErrorReporting add_executable(cuDLAErrorReporting main.cu) - target_compile_options(cuDLAErrorReporting PRIVATE $<$:--extended-lambda>) - target_compile_features(cuDLAErrorReporting PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cuDLAErrorReporting PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cuDLAErrorReporting PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(cuDLAErrorReporting ${CUDLA_LIB} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "CUDLA not found - will not build sample 'cuDLAErrorReporting'") endif() else() message(STATUS "Will not build sample cuDLAErrorReporting - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/CMakeLists.txt index b0dab5ca..42dcf6fd 100644 --- a/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cuDLAHybridMode/CMakeLists.txt @@ -1,53 +1,39 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cuDLAHybridMode LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PATH}) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(CUDLA_LIB) - # Source file - # Add target for cuDLAHybridMode add_executable(cuDLAHybridMode main.cu) - target_compile_options(cuDLAHybridMode PRIVATE $<$:--extended-lambda>) - target_compile_features(cuDLAHybridMode PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cuDLAHybridMode PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cuDLAHybridMode PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(cuDLAHybridMode ${CUDLA_LIB} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "CUDLA not found - will not build sample 'cuDLAHybridMode'") endif() else() message(STATUS "Will not build sample cuDLAHybridMode - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/CMakeLists.txt index be17b563..197b908b 100644 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsHybrid/CMakeLists.txt @@ -1,53 +1,39 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cuDLALayerwiseStatsHybrid LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PATH}) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(CUDLA_LIB) - # Source file - # Add target for cuDLALayerwiseStatsHybrid add_executable(cuDLALayerwiseStatsHybrid main.cu) - target_compile_options(cuDLALayerwiseStatsHybrid PRIVATE $<$:--extended-lambda>) - target_compile_features(cuDLALayerwiseStatsHybrid PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cuDLALayerwiseStatsHybrid PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cuDLALayerwiseStatsHybrid PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(cuDLALayerwiseStatsHybrid ${CUDLA_LIB} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "CUDLA not found - will not build sample 'cuDLALayerwiseStatsHybrid'") endif() else() message(STATUS "Will not build sample cuDLALayerwiseStatsHybrid - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/CMakeLists.txt index 8d7eee3a..69b06ab4 100644 --- a/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cuDLALayerwiseStatsStandalone/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cuDLALayerwiseStatsStandalone LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(NVSCI) @@ -26,25 +22,19 @@ find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PA if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(CUDLA_LIB) if(NVSCI_FOUND) - # Source file - # Add target for cuDLALayerwiseStatsStandalone add_executable(cuDLALayerwiseStatsStandalone main.cpp) - target_compile_options(cuDLALayerwiseStatsStandalone PRIVATE $<$:--extended-lambda>) - target_compile_features(cuDLALayerwiseStatsStandalone PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cuDLALayerwiseStatsStandalone PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cuDLALayerwiseStatsStandalone PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${NVSCI_INCLUDE_DIRS} ) - target_link_libraries(cuDLALayerwiseStatsStandalone ${CUDLA_LIB} ${NVSCI_LIBRARIES} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "NvSCI not found - will not build sample 'cuDLALayerwiseStatsStandalone'") endif() @@ -54,7 +44,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample cuDLALayerwiseStatsStandalone - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/CMakeLists.txt index 86b3ffed..68178f9b 100644 --- a/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cuDLAStandaloneMode/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cuDLAStandaloneMode LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(NVSCI) @@ -26,25 +22,19 @@ find_library(CUDLA_LIB cudla PATHS ${CUDAToolkit_LIBRARY_DIR} ${CMAKE_LIBRARY_PA if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(CUDLA_LIB) if(NVSCI_FOUND) - # Source file - # Add target for cuDLAStandaloneMode add_executable(cuDLAStandaloneMode main.cpp) - target_compile_options(cuDLAStandaloneMode PRIVATE $<$:--extended-lambda>) - target_compile_features(cuDLAStandaloneMode PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cuDLAStandaloneMode PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cuDLAStandaloneMode PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${NVSCI_INCLUDE_DIRS} ) - target_link_libraries(cuDLAStandaloneMode ${CUDLA_LIB} ${NVSCI_LIBRARIES} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "NvSCI not found - will not build sample 'cuDLAStandaloneMode'") endif() @@ -54,7 +44,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample cuDLAStandaloneMode - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/CMakeLists.txt index b6708b16..b29a5b2e 100644 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cudaNvSciBufMultiplanar/CMakeLists.txt @@ -1,44 +1,32 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaNvSciBufMultiplanar LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(NVSCI) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(NVSCI_FOUND) - # Source file - # Add target for cudaNvSciBufMultiplanar add_executable(cudaNvSciBufMultiplanar imageKernels.cu cudaNvSciBufMultiplanar.cpp main.cpp) - target_compile_options(cudaNvSciBufMultiplanar PRIVATE $<$:--extended-lambda>) - target_compile_features(cudaNvSciBufMultiplanar PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cudaNvSciBufMultiplanar PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cudaNvSciBufMultiplanar PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${NVSCI_INCLUDE_DIRS} ) - target_link_libraries(cudaNvSciBufMultiplanar CUDA::cuda_driver ${NVSCI_LIBRARIES} @@ -52,13 +40,11 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") set_target_properties(cudaNvSciBufMultiplanar PROPERTIES ADDITIONAL_CLEAN_FILES "image_out.yuv" ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "NvSCI not found - will not build sample 'cudaNvSciBufMultiplanar'") endif() else() message(STATUS "Will not build sample cudaNvSciBufMultiplanar - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/CMakeLists.txt index b043622f..8ebaaff2 100644 --- a/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/cudaNvSciNvMedia/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(cudaNvSciNvMedia LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(NVSCI) @@ -49,22 +45,14 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") if(NVMEDIA_LIB AND NVMEDIA_INCLUDE_DIR) message(STATUS "FOUND NVMEDIA libs: ${NVMEDIA_LIB}") message(STATUS "Using NVMEDIA headers path: ${NVMEDIA_INCLUDE_DIR}") - # Source file - # Add target for cudaNvSciNvMedia add_executable(cudaNvSciNvMedia imageKernels.cu cudaNvSciNvMedia.cpp main.cpp) - target_compile_options(cudaNvSciNvMedia PRIVATE $<$:--extended-lambda>) - target_compile_features(cudaNvSciNvMedia PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(cudaNvSciNvMedia PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(cudaNvSciNvMedia PUBLIC ${CUDAToolkit_INCLUDE_DIRS} ${NVSCI_INCLUDE_DIRS} ${NVMEDIA_INCLUDE_DIR} ) - target_link_libraries(cudaNvSciNvMedia CUDA::cuda_driver ${NVSCI_LIBRARIES} @@ -75,11 +63,12 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CMAKE_CURRENT_SOURCE_DIR}/teapot.rgba ${CMAKE_CURRENT_BINARY_DIR}/teapot.rgba ) - # Specify additional clean files set_target_properties(cudaNvSciNvMedia PROPERTIES ADDITIONAL_CLEAN_FILES "teapot_out.rgba" ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "NvMedia not found - will not build sample 'cudaNvSciNvMedia'") endif() @@ -89,7 +78,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample cudaNvSciNvMedia - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/fluidsGLES/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/fluidsGLES/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/fluidsGLES/CMakeLists.txt index 9f04ae7a..b449afb1 100644 --- a/cpp/8_Platform_Specific/Tegra/fluidsGLES/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/fluidsGLES/CMakeLists.txt @@ -1,22 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(fluidsGLES LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(EGL) @@ -24,25 +21,17 @@ find_package(X11) find_package(OpenGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${OpenGL_FOUND}) if(${EGL_FOUND}) if(${X11_FOUND}) - # Add target for fluidsGLES add_executable(fluidsGLES fluidsGLES.cpp fluidsGLES_kernels.cu) - target_compile_options(fluidsGLES PRIVATE $<$:--extended-lambda>) - target_compile_features(fluidsGLES PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(fluidsGLES PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(fluidsGLES PUBLIC ${EGL_INCLUDE_DIR} ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(fluidsGLES CUDA::cuda_driver CUDA::cufft @@ -50,7 +39,6 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") ${X11_LIBRARIES} ${OPENGL_LIBRARIES} ) - # Copy the .glsl files to the output directory add_custom_command(TARGET fluidsGLES POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -58,6 +46,8 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") ${CMAKE_CURRENT_SOURCE_DIR}/mesh.vert.glsl ${CMAKE_CURRENT_BINARY_DIR} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "X11 libraries not found - will not build sample 'fluidsGLES'") endif() @@ -70,7 +60,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample fluidsGLES - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/nbody_opengles/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/nbody_opengles/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/nbody_opengles/CMakeLists.txt index d55101c5..4c56a287 100644 --- a/cpp/8_Platform_Specific/Tegra/nbody_opengles/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/nbody_opengles/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(nbody_opengles LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(EGL) @@ -25,30 +21,24 @@ find_package(X11) find_package(OpenGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${OpenGL_FOUND}) if(${EGL_FOUND}) if(${X11_FOUND}) - # Add target for nbody_opengles add_executable(nbody_opengles bodysystemcuda.cu render_particles.cpp nbody_opengles.cpp) - target_compile_options(nbody_opengles PRIVATE $<$:--extended-lambda>) - target_compile_features(nbody_opengles PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(nbody_opengles PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(nbody_opengles PUBLIC ${EGL_INCLUDE_DIR} ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(nbody_opengles ${EGL_LIBRARY} ${X11_LIBRARIES} ${OPENGL_LIBRARIES} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "X11 libraries not found - will not build sample 'nbody_opengles'") endif() @@ -61,7 +51,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample nbody_opengles - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/simpleGLES/CMakeLists.txt index 3d88898b..7ad3bab3 100644 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/simpleGLES/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleGLES LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(EGL) @@ -25,31 +21,22 @@ find_package(X11) find_package(OpenGL) if(CMAKE_SYSTEM_NAME STREQUAL "Linux") - # Source file if(${OpenGL_FOUND}) if(${EGL_FOUND}) if(${X11_FOUND}) - # Add target for simpleGLES add_executable(simpleGLES simpleGLES.cu) - target_compile_options(simpleGLES PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleGLES PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleGLES PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleGLES PUBLIC ${EGL_INCLUDE_DIR} ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIRS} ) - target_link_libraries(simpleGLES ${EGL_LIBRARY} ${X11_LIBRARIES} ${OPENGL_LIBRARIES} ) - # Copy the .glsl files to the output directory add_custom_command(TARGET simpleGLES POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -57,7 +44,8 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") ${CMAKE_CURRENT_SOURCE_DIR}/mesh.vert.glsl ${CMAKE_CURRENT_BINARY_DIR} ) - + include(InstallSamples) + setup_samples_install() else() message(STATUS "X11 libraries not found - will not build sample 'simpleGLES'") endif() @@ -70,7 +58,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample simpleGLES - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/c_cpp_properties.json b/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/c_cpp_properties.json deleted file mode 100644 index f0066b0f..00000000 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/c_cpp_properties.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "configurations": [ - { - "name": "Linux", - "includePath": [ - "${workspaceFolder}/**", - "${workspaceFolder}/../../../Common" - ], - "defines": [], - "compilerPath": "/usr/local/cuda/bin/nvcc", - "cStandard": "gnu17", - "cppStandard": "gnu++14", - "intelliSenseMode": "linux-gcc-x64", - "configurationProvider": "ms-vscode.makefile-tools" - } - ], - "version": 4 -} diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/extensions.json b/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/extensions.json deleted file mode 100644 index c7eb54dc..00000000 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/.vscode/extensions.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "recommendations": [ - "nvidia.nsight-vscode-edition", - "ms-vscode.cpptools", - "ms-vscode.makefile-tools" - ] -} diff --git a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/CMakeLists.txt b/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/CMakeLists.txt index 7c362e1d..7c0232e7 100644 --- a/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/CMakeLists.txt +++ b/cpp/8_Platform_Specific/Tegra/simpleGLES_EGLOutput/CMakeLists.txt @@ -1,23 +1,19 @@ cmake_minimum_required(VERSION 3.20) -list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/Modules") +set(SAMPLE_DISALLOW_ARCHS 75 80 86 89 90 100 103 107 120) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() project(simpleGLES_EGLOutput LANGUAGES C CXX CUDA) +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/CudaSampleCommon.cmake") + find_package(CUDAToolkit REQUIRED) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 87 110) -set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Wno-deprecated-gpu-targets") - -if(ENABLE_CUDA_DEBUG) - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -G") # enable cuda-gdb (may significantly affect performance on some targets) -else() - set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) -endif() - -# Include directories and libraries include_directories(../../../../Common) find_package(EGL) @@ -34,29 +30,21 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") set(CMAKE_LIBRARY_PATH "/usr/lib/${CMAKE_SYSTEM_PROCESSOR}-linux-gnu" ${CMAKE_LIBRARY_PATH}) find_library(DRM_LIB drm PATHS ${CMAKE_LIBRARY_PATH}) - # Source file - # Add target for simpleGLES_EGLOutput add_executable(simpleGLES_EGLOutput simpleGLES_EGLOutput.cu) target_compile_options(simpleGLES_EGLOutput PRIVATE $<$:--extended-lambda>) - target_compile_features(simpleGLES_EGLOutput PRIVATE cxx_std_17 cuda_std_17) - - set_target_properties(simpleGLES_EGLOutput PROPERTIES CUDA_SEPARABLE_COMPILATION ON) - target_include_directories(simpleGLES_EGLOutput PUBLIC ${EGL_INCLUDE_DIR} ${OPENGL_INCLUDE_DIR} ${CUDAToolkit_INCLUDE_DIR} ${DRM_INCLUDE_PATH} ) - target_link_libraries(simpleGLES_EGLOutput ${EGL_LIBRARY} ${X11_LIBRARIES} ${OPENGL_LIBRARIES} ${DRM_LIB} ) - # Copy the .glsl files to the output directory add_custom_command(TARGET simpleGLES_EGLOutput POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy_if_different @@ -64,6 +52,8 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") ${CMAKE_CURRENT_SOURCE_DIR}/mesh.vert.glsl ${CMAKE_CURRENT_BINARY_DIR} ) + include(InstallSamples) + setup_samples_install() else() message(STATUS "X11 libraries not found - will not build sample 'simpleGLES_EGLOutput'") endif() @@ -76,7 +66,3 @@ if(CMAKE_SYSTEM_NAME STREQUAL "Linux") else() message(STATUS "Will not build sample simpleGLES_EGLOutput - requires Linux OS") endif() - -# Include installation configuration -include(${CMAKE_CURRENT_SOURCE_DIR}/../../../../cmake/InstallSamples.cmake) -setup_samples_install() diff --git a/cpp/9_CUDA_Tile/helloTile/CMakeLists.txt b/cpp/9_CUDA_Tile/helloTile/CMakeLists.txt index 1406fea7..544e9171 100644 --- a/cpp/9_CUDA_Tile/helloTile/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/helloTile/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(helloTile LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileBmm/CMakeLists.txt b/cpp/9_CUDA_Tile/tileBmm/CMakeLists.txt index 0bcbbfcd..1b2ba8a8 100644 --- a/cpp/9_CUDA_Tile/tileBmm/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileBmm/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileBmm LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileLayerNorm/CMakeLists.txt b/cpp/9_CUDA_Tile/tileLayerNorm/CMakeLists.txt index 7010fe7a..e6b1f5c9 100644 --- a/cpp/9_CUDA_Tile/tileLayerNorm/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileLayerNorm/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileLayerNorm LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileMatmul/CMakeLists.txt b/cpp/9_CUDA_Tile/tileMatmul/CMakeLists.txt index bb496cf8..eee2dbf4 100644 --- a/cpp/9_CUDA_Tile/tileMatmul/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileMatmul/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileMatmul LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileMatmulAutotuner/CMakeLists.txt b/cpp/9_CUDA_Tile/tileMatmulAutotuner/CMakeLists.txt index 4ed4807c..1768e68c 100644 --- a/cpp/9_CUDA_Tile/tileMatmulAutotuner/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileMatmulAutotuner/CMakeLists.txt @@ -1,9 +1,26 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileMatmulAutotuner LANGUAGES C CXX CUDA) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() + find_package(CUDAToolkit REQUIRED) get_filename_component(CUDA_TOOLKIT_BIN_DIR "${CMAKE_CUDA_COMPILER}" DIRECTORY) @@ -12,9 +29,6 @@ find_program(TILEIRAS_EXECUTABLE tileiras REQUIRED ) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) - -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") if(ENABLE_CUDA_DEBUG) @@ -23,6 +37,12 @@ else() set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -lineinfo") # add line information to all builds for debug tools (exclusive to -G option) endif() +# This sample is not supported on QNX (needs tileiras/nvcc at runtime). +if(CMAKE_SYSTEM_NAME STREQUAL "QNX") + message(STATUS "Will not build sample ${PROJECT_NAME} - not supported on QNX") + return() +endif() + # Include directories and libraries include_directories(../../../Common ../Benchmark_Common) diff --git a/cpp/9_CUDA_Tile/tileRope/CMakeLists.txt b/cpp/9_CUDA_Tile/tileRope/CMakeLists.txt index e9c0d7f7..ddf1dfb4 100644 --- a/cpp/9_CUDA_Tile/tileRope/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileRope/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileRope LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileSpMV/CMakeLists.txt b/cpp/9_CUDA_Tile/tileSpMV/CMakeLists.txt index 8228c74a..8c98591a 100644 --- a/cpp/9_CUDA_Tile/tileSpMV/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileSpMV/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileSpMV LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileTranspose/CMakeLists.txt b/cpp/9_CUDA_Tile/tileTranspose/CMakeLists.txt index 401c8634..ae7dd1d6 100644 --- a/cpp/9_CUDA_Tile/tileTranspose/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileTranspose/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileTranspose LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/cpp/9_CUDA_Tile/tileVectorAdd/CMakeLists.txt b/cpp/9_CUDA_Tile/tileVectorAdd/CMakeLists.txt index a7f9ab2f..44ce10fe 100644 --- a/cpp/9_CUDA_Tile/tileVectorAdd/CMakeLists.txt +++ b/cpp/9_CUDA_Tile/tileVectorAdd/CMakeLists.txt @@ -1,12 +1,27 @@ cmake_minimum_required(VERSION 3.20) +set(SAMPLE_DISALLOW_ARCHS 75) + +include("${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/DetectCudaArch.cmake") + +if(SAMPLE_SKIP_BUILD) + return() +endif() + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/../../../cmake/Modules") project(tileVectorAdd LANGUAGES C CXX CUDA) -find_package(CUDAToolkit REQUIRED) +# Tile samples need C++20. If the host or CUDA compiler can't provide it, +# skip this sample instead of aborting configure. +if(NOT "cxx_std_20" IN_LIST CMAKE_CXX_COMPILE_FEATURES + OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 11) + OR NOT "cuda_std_20" IN_LIST CMAKE_CUDA_COMPILE_FEATURES) + message(STATUS "Compiler lacks required C++20 support - skipping ${PROJECT_NAME}") + return() +endif() -set(CMAKE_CUDA_ARCHITECTURES 80 86 87 89 90 100 110 120) +find_package(CUDAToolkit REQUIRED) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} --enable-tile") diff --git a/python/1_GettingStarted/blurImageUnifiedMemory/README.md b/python/1_GettingStarted/blurImageUnifiedMemory/README.md index 93bdd26d..33d9184a 100644 --- a/python/1_GettingStarted/blurImageUnifiedMemory/README.md +++ b/python/1_GettingStarted/blurImageUnifiedMemory/README.md @@ -112,12 +112,12 @@ When returning a zero-copy view, the caller must close the buffers after use (e. ### Platform Support: -This sample relies on `ManagedMemoryResource` with **concurrent host access** -to managed allocations while GPU kernels are in flight. That behavior -requires the device property `concurrent_managed_access=True`, which is only -supported on Linux with HMM (Pascal and newer). On Windows (WDDM/MCDM/TCC) -the property is `False`, so the sample exits early with a waive message and -exit code `2` instead of attempting a run that would crash the process. +This sample builds host NumPy views over managed allocations, which requires +the device property `concurrent_managed_access=True`. Devices that report +`False` — Windows (WDDM/MCDM/TCC), and older integrated GPUs such as Jetson +Orin — keep managed allocations GPU-exclusive, so the sample reports the +missing capability and exits with code `2` instead of attempting a run that +would crash the process. ## Installation @@ -174,4 +174,4 @@ Verifying result... - [cuda.core Documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/) - [cuda.core.Program](https://nvidia.github.io/cuda-python/cuda-core/latest/generated/cuda.core.Program.html) - [cuda.core.ManagedMemoryResource](https://nvidia.github.io/cuda-python/cuda-core/latest/generated/cuda.core.ManagedMemoryResource.html) -- [CUDA Managed Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-unified-memory-programming-hd) +- [CUDA Managed Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/unified-memory.html#unified-memory) diff --git a/python/1_GettingStarted/blurImageUnifiedMemory/blurImageUnifiedMemory.py b/python/1_GettingStarted/blurImageUnifiedMemory/blurImageUnifiedMemory.py index 20a05d2e..b81f5e81 100644 --- a/python/1_GettingStarted/blurImageUnifiedMemory/blurImageUnifiedMemory.py +++ b/python/1_GettingStarted/blurImageUnifiedMemory/blurImageUnifiedMemory.py @@ -197,14 +197,6 @@ def main(): 3. Unified memory with cuda.core.ManagedMemoryResource 4. Kernel launch with cuda.core.launch and LaunchConfig """ - if sys.platform == "win32": - print( - "This sample relies on ManagedMemoryResource with concurrent host " - "access, which is not supported on Windows " - "(concurrent_managed_access=False). Waiving this sample." - ) - sys.exit(2) - print("=" * 60) print("Image Blur with Unified Memory (cuda.core)") print("=" * 60) @@ -213,6 +205,13 @@ def main(): device = Device(0) device.set_current() + # This sample builds host NumPy views over managed memory. Devices without + # concurrent managed access keep managed allocations GPU-exclusive while + # the GPU is active, so those host views fault. + if not device.properties.concurrent_managed_access: + print("Concurrent managed memory access is not supported on this platform.") + sys.exit(2) + print(f"\nDevice: {device.name}") print(f"Compute Capability: sm_{device.arch}") diff --git a/python/1_GettingStarted/copyImageArraytoGPU/copyImageArraytoGPU.py b/python/1_GettingStarted/copyImageArraytoGPU/copyImageArraytoGPU.py index 310fc361..812bd0c3 100644 --- a/python/1_GettingStarted/copyImageArraytoGPU/copyImageArraytoGPU.py +++ b/python/1_GettingStarted/copyImageArraytoGPU/copyImageArraytoGPU.py @@ -189,6 +189,14 @@ def main(): # Step 1: Set up CUDA device and stream dev = Device() # Get default CUDA device (GPU 0) dev.set_current() # Make this device the active one + + # Pinned memory resources are backed by a host memory pool, which requires + # CUDA memory-pool support. This is not available on every platform (for + # example, Windows in TCC mode), so check before using it. + if not dev.properties.host_memory_pools_supported: + print("Host pinned memory pools are not supported on this platform.") + return 2 + stream = dev.create_stream() # Create stream for async operations print(f"Device: {dev.name}") @@ -233,7 +241,8 @@ def main(): cp.cuda.Stream.null.use() # Reset CuPy's stream to default print("\nDone") + return 0 if __name__ == "__main__": - main() + sys.exit(main()) diff --git a/python/1_GettingStarted/deviceQuery/README.md b/python/1_GettingStarted/deviceQuery/README.md index 17e639c3..b1c8a2a3 100644 --- a/python/1_GettingStarted/deviceQuery/README.md +++ b/python/1_GettingStarted/deviceQuery/README.md @@ -186,4 +186,4 @@ For multi-GPU systems, the output will include information for all detected devi - [CUDA Python Documentation](https://nvidia.github.io/cuda-python/) - [cuda.core API Guide](https://nvidia.github.io/cuda-python/cuda-core/latest/) -- [CUDA Programming Guide - Device Information](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#device-enumeration) +- [CUDA Programming Guide - Device Information](https://docs.nvidia.com/cuda/cuda-programming-guide/03-advanced/multi-gpu-systems.html#device-enumeration) diff --git a/python/1_GettingStarted/deviceQuery/requirements.txt b/python/1_GettingStarted/deviceQuery/requirements.txt index a0e4feab..0a2dc302 100644 --- a/python/1_GettingStarted/deviceQuery/requirements.txt +++ b/python/1_GettingStarted/deviceQuery/requirements.txt @@ -1,4 +1,4 @@ # Device Query Sample Requirements cuda-python>=13.0.0 -cuda-core>=1.0.0 +cuda-core>=1.2.0 diff --git a/python/1_GettingStarted/simplePrint/README.md b/python/1_GettingStarted/simplePrint/README.md index 350694b0..d5834d43 100644 --- a/python/1_GettingStarted/simplePrint/README.md +++ b/python/1_GettingStarted/simplePrint/README.md @@ -259,5 +259,5 @@ Try modifying: - [nvmath-python Device APIs](https://docs.nvidia.com/cuda/nvmath-python/latest/device-apis/index.html) - Optimized math operations for Numba CUDA kernels ### CUDA References: -- [CUDA C Programming Guide - Printf](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#formatted-output) +- [CUDA C Programming Guide - Printf](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-support.html#printf) - [C++ simplePrintf Sample](https://github.com/NVIDIA/cuda-samples/tree/master/Samples/0_Introduction/simplePrintf) diff --git a/python/1_GettingStarted/simplePrint/requirements.txt b/python/1_GettingStarted/simplePrint/requirements.txt index a39c5e7e..8dce0f08 100644 --- a/python/1_GettingStarted/simplePrint/requirements.txt +++ b/python/1_GettingStarted/simplePrint/requirements.txt @@ -3,5 +3,9 @@ cuda-python>=13.0.0 cuda-core>=1.0.0 # Numba JIT uses nvJitLink from pip; keep in step with cuda-bindings (e.g. 13.2.x). +# Also pin libnvvm from pip so Numba doesn't fall back to a mismatched +# system-toolkit libnvvm (nvJitLink ERROR_OUTDATED_LIBRARY). +nvidia-nvvm>=13.2.0 nvidia-nvjitlink>=13.2.0 numba-cuda>=0.29.0 +numpy<2.5 diff --git a/python/2_CoreConcepts/blockwiseSum/README.md b/python/2_CoreConcepts/blockwiseSum/README.md index 19fcc922..486efc5c 100644 --- a/python/2_CoreConcepts/blockwiseSum/README.md +++ b/python/2_CoreConcepts/blockwiseSum/README.md @@ -98,5 +98,5 @@ Done ## See Also - [cuda.core Documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/) -- [CUDA Shared Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#shared-memory) +- [CUDA Shared Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/writing-cuda-kernels.html#shared-memory) - [CuPy Documentation](https://docs.cupy.dev/) diff --git a/python/2_CoreConcepts/cudaComputeLambdas/cudaComputeLambdas.py b/python/2_CoreConcepts/cudaComputeLambdas/cudaComputeLambdas.py index d790bb4d..219bc1a1 100644 --- a/python/2_CoreConcepts/cudaComputeLambdas/cudaComputeLambdas.py +++ b/python/2_CoreConcepts/cudaComputeLambdas/cudaComputeLambdas.py @@ -160,6 +160,13 @@ def main(): print_gpu_info(device) print() + # cuda.compute allocates temporary storage from the device's default memory + # pool, which requires CUDA memory-pool support. This is not available on + # every platform (for example, Windows in TCC mode). + if not device.properties.memory_pools_supported: + print("CUDA memory pools are not supported on this platform.") + return 2 + ok = True ok &= demo_reduce_lambda() print() diff --git a/python/2_CoreConcepts/cudaGraphs/README.md b/python/2_CoreConcepts/cudaGraphs/README.md index 60b8c5bd..5cf11957 100644 --- a/python/2_CoreConcepts/cudaGraphs/README.md +++ b/python/2_CoreConcepts/cudaGraphs/README.md @@ -137,4 +137,4 @@ your GPU and host CPU. - [CUDA Python Documentation](https://nvidia.github.io/cuda-python/) - [`cuda.core` graphs API](https://nvidia.github.io/cuda-python/cuda-core/latest/api.html#cuda-graphs) - Upstream `cuda.core` example: [`cuda_graphs.py`](https://github.com/NVIDIA/cuda-python/blob/main/cuda_core/examples/cuda_graphs.py) -- [CUDA Graphs programming guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-graphs) +- [CUDA Graphs programming guide](https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/cuda-graphs.html#cuda-graphs) diff --git a/python/2_CoreConcepts/greenContext/README.md b/python/2_CoreConcepts/greenContext/README.md index 3afd8083..291f63ab 100644 --- a/python/2_CoreConcepts/greenContext/README.md +++ b/python/2_CoreConcepts/greenContext/README.md @@ -246,5 +246,5 @@ other concurrent GPU work. ## See Also - [CUDA Python Documentation](https://nvidia.github.io/cuda-python/) -- [Green Contexts in the CUDA C++ Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#green-contexts) +- [Green Contexts in the CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-programming-guide/04-special-topics/green-contexts.html#green-contexts) - [`cuda.core` green-context test suite](https://github.com/NVIDIA/cuda-python/blob/main/cuda_core/tests/test_green_context.py) - the authoritative API reference diff --git a/python/2_CoreConcepts/launchConfigTuning/README.md b/python/2_CoreConcepts/launchConfigTuning/README.md index 6870f1d7..098e5467 100644 --- a/python/2_CoreConcepts/launchConfigTuning/README.md +++ b/python/2_CoreConcepts/launchConfigTuning/README.md @@ -94,10 +94,10 @@ elapsed_ms = (end_event - start_event) / n_iterations The benchmark loops in this sample read kernel results back from `ManagedMemoryResource` allocations between launches, which requires the -device property `concurrent_managed_access=True`. This is only supported on -Linux with HMM (Pascal and newer). On Windows (WDDM/MCDM/TCC) the property -is `False`, so the sample exits early with a waive message and exit code -`2`. +device property `concurrent_managed_access=True`. Devices that report `False` +— Windows (WDDM/MCDM/TCC), and older integrated GPUs such as Jetson Orin — +keep managed allocations GPU-exclusive, so the sample reports the missing +capability and exits with code `2`. ## Installation diff --git a/python/2_CoreConcepts/launchConfigTuning/launchConfigTuning.py b/python/2_CoreConcepts/launchConfigTuning/launchConfigTuning.py index d854ee4f..68404898 100644 --- a/python/2_CoreConcepts/launchConfigTuning/launchConfigTuning.py +++ b/python/2_CoreConcepts/launchConfigTuning/launchConfigTuning.py @@ -341,14 +341,6 @@ def main(): 3. Benchmarking different thread block configurations 4. Finding optimal threads-per-block for various kernel types """ - if sys.platform == "win32": - print( - "This sample relies on ManagedMemoryResource with concurrent host " - "access, which is not supported on Windows " - "(concurrent_managed_access=False). Waiving this sample." - ) - sys.exit(2) - print("=" * 60) print("Launch Configuration Tuning (cuda.core)") print("Finding the Best Block Size for Your Kernel") @@ -358,6 +350,13 @@ def main(): device = Device(0) device.set_current() + # This sample builds host NumPy views over managed memory. Devices without + # concurrent managed access keep managed allocations GPU-exclusive while + # the GPU is active, so those host views fault. + if not device.properties.concurrent_managed_access: + print("Concurrent managed memory access is not supported on this platform.") + sys.exit(2) + # Print GPU information print_gpu_info(device) diff --git a/python/2_CoreConcepts/memoryResources/README.md b/python/2_CoreConcepts/memoryResources/README.md index 6d6d6a94..4bebbb1e 100644 --- a/python/2_CoreConcepts/memoryResources/README.md +++ b/python/2_CoreConcepts/memoryResources/README.md @@ -72,15 +72,19 @@ verified on the host. ### Platform Support -The `ManagedMemoryResource` demo in this sample exercises **concurrent host -access** to managed allocations while the GPU is active, which requires the -device property `concurrent_managed_access=True`. This is only supported on -Linux with HMM (Pascal and newer). On Windows (WDDM/MCDM/TCC) the property -is `False`, so the sample exits early with a waive message and exit code -`2`. The `DeviceMemoryResource` + `PinnedMemoryResource` demos in this -sample would still work on Windows on their own, but to keep the sample -self-contained the entire script waives when concurrent managed access is -unavailable. +This sample needs two device capabilities, and exits with code `2` when +either is missing: + +- `PinnedMemoryResource` is backed by a host memory pool, which requires + `host_memory_pools_supported=True`. Windows in TCC mode and older + integrated GPUs such as Jetson Orin report `False`. +- The `ManagedMemoryResource` demo builds host NumPy views over managed + allocations, which requires `concurrent_managed_access=True`. Windows + (WDDM/MCDM/TCC) and older integrated GPUs such as Jetson Orin report + `False`. + +The demos are checked up front rather than individually to keep the sample +self-contained. ## Installation diff --git a/python/2_CoreConcepts/memoryResources/memoryResources.py b/python/2_CoreConcepts/memoryResources/memoryResources.py index 07776c18..5e88c54d 100644 --- a/python/2_CoreConcepts/memoryResources/memoryResources.py +++ b/python/2_CoreConcepts/memoryResources/memoryResources.py @@ -222,18 +222,23 @@ def main(): parser.add_argument("--device", type=int, default=0, help="CUDA device id") args = parser.parse_args() - if sys.platform == "win32": - print( - "This sample relies on ManagedMemoryResource with concurrent host " - "access, which is not supported on Windows " - "(concurrent_managed_access=False). Waiving this sample." - ) - sys.exit(2) - device = Device(args.device) device.set_current() print_gpu_info(device) + # PinnedMemoryResource is backed by a host memory pool, which is not + # available on every device. + if not device.properties.host_memory_pools_supported: + print("Host pinned memory pools are not supported on this platform.") + return 2 + + # This sample builds host NumPy views over managed memory. Devices without + # concurrent managed access keep managed allocations GPU-exclusive while + # the GPU is active, so those host views fault. + if not device.properties.concurrent_managed_access: + print("Concurrent managed memory access is not supported on this platform.") + return 2 + stream = device.create_stream() try: diff --git a/python/2_CoreConcepts/parallelHistogram/README.md b/python/2_CoreConcepts/parallelHistogram/README.md index 2820102d..a267f42b 100644 --- a/python/2_CoreConcepts/parallelHistogram/README.md +++ b/python/2_CoreConcepts/parallelHistogram/README.md @@ -113,5 +113,5 @@ Test PASSED ## See Also - [cuda.core Documentation](https://nvidia.github.io/cuda-python/cuda-core/latest/) -- [CUDA Atomic Functions](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#atomic-functions) -- [CUDA Shared Memory](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#shared-memory) +- [CUDA Atomic Functions](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/writing-cuda-kernels.html#atomics) +- [CUDA Shared Memory](https://docs.nvidia.com/cuda/cuda-programming-guide/02-basics/writing-cuda-kernels.html#shared-memory) diff --git a/python/2_CoreConcepts/parallelReduction/parallelReduction.py b/python/2_CoreConcepts/parallelReduction/parallelReduction.py index f056200d..b5d40696 100644 --- a/python/2_CoreConcepts/parallelReduction/parallelReduction.py +++ b/python/2_CoreConcepts/parallelReduction/parallelReduction.py @@ -290,7 +290,7 @@ def benchmark_cuda_compute( return result, float(np.mean(times)) -def main() -> bool: +def main() -> int: """Main function demonstrating parallel reduction.""" print("=" * 70) print("Parallel Reduction - Efficient GPU Array Summation") @@ -298,6 +298,14 @@ def main() -> bool: device = Device(0) device.set_current() + + # cuda.compute allocates temporary storage from the device's default memory + # pool, which requires CUDA memory-pool support. This is not available on + # every platform (for example, Windows in TCC mode). + if not device.properties.memory_pools_supported: + print("CUDA memory pools are not supported on this platform.") + return 2 + stream = device.create_stream() cp_stream = cp.cuda.Stream.from_external(stream) @@ -363,13 +371,13 @@ def main() -> bool: ) if custom_ok and compute_ok: print("\nTest PASSED!") - return True + return 0 else: print("\nTest FAILED - Error too large!") - return False + return 1 finally: stream.close() if __name__ == "__main__": - sys.exit(0 if main() else 1) + sys.exit(main()) diff --git a/python/2_CoreConcepts/persistentProgramCache/README.md b/python/2_CoreConcepts/persistentProgramCache/README.md new file mode 100644 index 00000000..dddbd474 --- /dev/null +++ b/python/2_CoreConcepts/persistentProgramCache/README.md @@ -0,0 +1,175 @@ +# Persistent Program Cache (Python) + +## Description + +This sample demonstrates how to persist and reuse compiled CUDA artifacts +with `cuda.core`. It generates a specialized matmul + epilogue kernel, +compiles it to CUBIN on a cache miss, stores the artifact in +`FileStreamProgramCache`, and reloads the CUBIN on later runs. + +## Core Cache Flow + +The central cache logic is in `compile_or_load_kernel`: + +```python +options = ProgramOptions(std="c++17", arch=f"sm_{device.arch}") +program = Program(source, code_type="c++", options=options) +key = make_program_cache_key(...) + +with FileStreamProgramCache(cache_dir) as cache: + cached = cache.get(key) + + if cached is None: + module = program.compile("cubin") + cache[key] = module + status = "MISS" + else: + module = ObjectCode.from_cubin(cached, name="persistentProgramCache") + status = "HIT" + +kernel = module.get_kernel(KERNEL_NAME) +``` + +## What You'll Learn + +- Generating a CUDA source string from workload configuration +- Building a persistent cache key with `make_program_cache_key` +- Storing compiled CUBIN bytes with `FileStreamProgramCache` +- Reconstructing a loadable `ObjectCode` with `ObjectCode.from_cubin` +- Measuring cache lookup, compile, module load, and kernel execution time +- Validating that cached and freshly compiled artifacts produce the same result + +## Key Libraries + +- [`cuda.core`][cuda-core] - Pythonic access to CUDA programs, object code, launches, and events +- `cupy` - input and output buffers on the GPU +- `numpy` - deterministic input generation and host reference computation + +## Key APIs + +### From `cuda.core` + +- `Program(...).compile("cubin")` - compile generated CUDA source to CUBIN +- `ObjectCode.from_cubin(...)` - reconstruct loadable object code from cached bytes +- `ObjectCode.get_kernel(name)` - fetch the kernel from the compiled artifact +- `LaunchConfig` and `launch(...)` - configure and launch the generated kernel +- `EventOptions(timing_enabled=True)` - time repeated kernel launches + +### From `cuda.core.utils` + +- `FileStreamProgramCache` - disk-backed, process-safe program cache +- `make_program_cache_key(...)` - derive a cache key from source, options, and target type + +## Requirements + +### Hardware + +- NVIDIA GPU with Compute Capability 7.0 or higher + +### Software + +- CUDA Toolkit 13.0 or newer (matches `cuda-python` 13.x) +- Python 3.10 or newer +- `cuda-python` (>=13.0.0) +- `cuda-core` (>=1.0.0) +- `cupy-cuda13x` (>=14.0.0) +- `numpy` (>=2.3.2) + +## Installation + +Install the required packages from `requirements.txt`: + +```bash +cd /path/to/cuda-samples/python/2_CoreConcepts/persistentProgramCache +pip install -r requirements.txt +``` + +The `requirements.txt` installs: + +- `cuda-python` (>=13.0.0) +- `cuda-core` (>=1.0.0) +- `cupy-cuda13x` (>=14.0.0) +- `numpy` (>=2.3.2) + +## How to Run + +### Basic usage + +Run once with a cleared cache to force compilation: + +```bash +cd cuda-samples/python/2_CoreConcepts/persistentProgramCache +python persistentProgramCache.py --clear-cache +``` + +Run again with the same configuration to reuse the cached artifact: + +```bash +python persistentProgramCache.py +``` + +### With custom parameters + +```bash +# Compile a different generated kernel variant +python persistentProgramCache.py --tile-size 32 --epilogue identity + +# Use a custom matrix size +python persistentProgramCache.py --m 1024 --n 1024 --k 1024 + +# Use a specific GPU +python persistentProgramCache.py --device 1 +``` + +Changing the tile size, epilogue, source code, compile options, or target +GPU architecture changes the cache key and produces a cache miss. + +## Expected Output + +The output includes the run configuration, cache status, timings, and +validation result: + +```text +Persistent Program Cache +Device: +Compute Capability: +Cache directory: +Workload: C = relu(A @ B + bias) +Matrix sizes: M=512, N=512, K=512 +Tile size: 16 +Timed launches: warmup=5, iterations=20 + +Cache status: MISS | HIT +Cache key: ... +Artifact size: KiB +Cache lookup: