mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 14:49:16 +08:00
344 lines
14 KiB
C++
344 lines
14 KiB
C++
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/* This sample queries the properties of the CUDA devices present in the system
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* via CUDA Runtime API. */
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// std::system includes
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#include <cuda_runtime.h>
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#include <helper_cuda.h>
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#include <iostream>
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#include <memory>
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#include <string>
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int *pArgc = NULL;
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char **pArgv = NULL;
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#if CUDART_VERSION < 5000
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// CUDA-C includes
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#include <cuda.h>
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// This function wraps the CUDA Driver API into a template function
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template <class T>
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inline void getCudaAttribute(T *attribute, CUdevice_attribute device_attribute,
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int device) {
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CUresult error = cuDeviceGetAttribute(attribute, device_attribute, device);
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if (CUDA_SUCCESS != error) {
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fprintf(
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stderr,
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"cuSafeCallNoSync() Driver API error = %04d from file <%s>, line %i.\n",
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error, __FILE__, __LINE__);
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exit(EXIT_FAILURE);
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}
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}
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#endif /* CUDART_VERSION < 5000 */
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////////////////////////////////////////////////////////////////////////////////
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// Program main
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////////////////////////////////////////////////////////////////////////////////
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int main(int argc, char **argv) {
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pArgc = &argc;
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pArgv = argv;
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printf("%s Starting...\n\n", argv[0]);
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printf(
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" CUDA Device Query (Runtime API) version (CUDART static linking)\n\n");
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int deviceCount = 0;
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cudaError_t error_id = cudaGetDeviceCount(&deviceCount);
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if (error_id != cudaSuccess) {
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printf("cudaGetDeviceCount returned %d\n-> %s\n",
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static_cast<int>(error_id), cudaGetErrorString(error_id));
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printf("Result = FAIL\n");
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exit(EXIT_FAILURE);
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}
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// This function call returns 0 if there are no CUDA capable devices.
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if (deviceCount == 0) {
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printf("There are no available device(s) that support CUDA\n");
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} else {
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printf("Detected %d CUDA Capable device(s)\n", deviceCount);
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}
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int dev, driverVersion = 0, runtimeVersion = 0;
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for (dev = 0; dev < deviceCount; ++dev) {
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cudaSetDevice(dev);
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cudaDeviceProp deviceProp;
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cudaGetDeviceProperties(&deviceProp, dev);
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printf("\nDevice %d: \"%s\"\n", dev, deviceProp.name);
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// Console log
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cudaDriverGetVersion(&driverVersion);
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cudaRuntimeGetVersion(&runtimeVersion);
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printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n",
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driverVersion / 1000, (driverVersion % 100) / 10,
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runtimeVersion / 1000, (runtimeVersion % 100) / 10);
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printf(" CUDA Capability Major/Minor version number: %d.%d\n",
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deviceProp.major, deviceProp.minor);
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char msg[256];
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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sprintf_s(msg, sizeof(msg),
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" Total amount of global memory: %.0f MBytes "
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"(%llu bytes)\n",
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static_cast<float>(deviceProp.totalGlobalMem / 1048576.0f),
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(unsigned long long)deviceProp.totalGlobalMem);
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#else
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snprintf(msg, sizeof(msg),
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" Total amount of global memory: %.0f MBytes "
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"(%llu bytes)\n",
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static_cast<float>(deviceProp.totalGlobalMem / 1048576.0f),
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(unsigned long long)deviceProp.totalGlobalMem);
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#endif
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printf("%s", msg);
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printf(" (%03d) Multiprocessors, (%03d) CUDA Cores/MP: %d CUDA Cores\n",
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deviceProp.multiProcessorCount,
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_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
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_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) *
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deviceProp.multiProcessorCount);
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printf(
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" GPU Max Clock rate: %.0f MHz (%0.2f "
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"GHz)\n",
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deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);
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#if CUDART_VERSION >= 5000
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// This is supported in CUDA 5.0 (runtime API device properties)
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printf(" Memory Clock rate: %.0f MHz\n",
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deviceProp.memoryClockRate * 1e-3f);
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printf(" Memory Bus Width: %d-bit\n",
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deviceProp.memoryBusWidth);
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if (deviceProp.l2CacheSize) {
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printf(" L2 Cache Size: %d bytes\n",
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deviceProp.l2CacheSize);
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}
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#else
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// This only available in CUDA 4.0-4.2 (but these were only exposed in the
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// CUDA Driver API)
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int memoryClock;
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getCudaAttribute<int>(&memoryClock, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE,
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dev);
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printf(" Memory Clock rate: %.0f MHz\n",
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memoryClock * 1e-3f);
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int memBusWidth;
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getCudaAttribute<int>(&memBusWidth,
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CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, dev);
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printf(" Memory Bus Width: %d-bit\n",
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memBusWidth);
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int L2CacheSize;
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getCudaAttribute<int>(&L2CacheSize, CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE, dev);
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if (L2CacheSize) {
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printf(" L2 Cache Size: %d bytes\n",
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L2CacheSize);
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}
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#endif
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printf(
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" Maximum Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d, "
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"%d), 3D=(%d, %d, %d)\n",
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deviceProp.maxTexture1D, deviceProp.maxTexture2D[0],
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deviceProp.maxTexture2D[1], deviceProp.maxTexture3D[0],
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deviceProp.maxTexture3D[1], deviceProp.maxTexture3D[2]);
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printf(
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" Maximum Layered 1D Texture Size, (num) layers 1D=(%d), %d layers\n",
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deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1]);
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printf(
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" Maximum Layered 2D Texture Size, (num) layers 2D=(%d, %d), %d "
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"layers\n",
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deviceProp.maxTexture2DLayered[0], deviceProp.maxTexture2DLayered[1],
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deviceProp.maxTexture2DLayered[2]);
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printf(" Total amount of constant memory: %zu bytes\n",
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deviceProp.totalConstMem);
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printf(" Total amount of shared memory per block: %zu bytes\n",
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deviceProp.sharedMemPerBlock);
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printf(" Total shared memory per multiprocessor: %zu bytes\n",
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deviceProp.sharedMemPerMultiprocessor);
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printf(" Total number of registers available per block: %d\n",
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deviceProp.regsPerBlock);
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printf(" Warp size: %d\n",
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deviceProp.warpSize);
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printf(" Maximum number of threads per multiprocessor: %d\n",
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deviceProp.maxThreadsPerMultiProcessor);
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printf(" Maximum number of threads per block: %d\n",
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deviceProp.maxThreadsPerBlock);
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printf(" Max dimension size of a thread block (x,y,z): (%d, %d, %d)\n",
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deviceProp.maxThreadsDim[0], deviceProp.maxThreadsDim[1],
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deviceProp.maxThreadsDim[2]);
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printf(" Max dimension size of a grid size (x,y,z): (%d, %d, %d)\n",
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deviceProp.maxGridSize[0], deviceProp.maxGridSize[1],
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deviceProp.maxGridSize[2]);
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printf(" Maximum memory pitch: %zu bytes\n",
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deviceProp.memPitch);
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printf(" Texture alignment: %zu bytes\n",
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deviceProp.textureAlignment);
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printf(
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" Concurrent copy and kernel execution: %s with %d copy "
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"engine(s)\n",
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(deviceProp.deviceOverlap ? "Yes" : "No"), deviceProp.asyncEngineCount);
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printf(" Run time limit on kernels: %s\n",
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deviceProp.kernelExecTimeoutEnabled ? "Yes" : "No");
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printf(" Integrated GPU sharing Host Memory: %s\n",
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deviceProp.integrated ? "Yes" : "No");
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printf(" Support host page-locked memory mapping: %s\n",
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deviceProp.canMapHostMemory ? "Yes" : "No");
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printf(" Alignment requirement for Surfaces: %s\n",
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deviceProp.surfaceAlignment ? "Yes" : "No");
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printf(" Device has ECC support: %s\n",
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deviceProp.ECCEnabled ? "Enabled" : "Disabled");
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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printf(" CUDA Device Driver Mode (TCC or WDDM): %s\n",
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deviceProp.tccDriver ? "TCC (Tesla Compute Cluster Driver)"
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: "WDDM (Windows Display Driver Model)");
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#endif
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printf(" Device supports Unified Addressing (UVA): %s\n",
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deviceProp.unifiedAddressing ? "Yes" : "No");
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printf(" Device supports Managed Memory: %s\n",
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deviceProp.managedMemory ? "Yes" : "No");
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printf(" Device supports Compute Preemption: %s\n",
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deviceProp.computePreemptionSupported ? "Yes" : "No");
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printf(" Supports Cooperative Kernel Launch: %s\n",
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deviceProp.cooperativeLaunch ? "Yes" : "No");
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printf(" Supports MultiDevice Co-op Kernel Launch: %s\n",
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deviceProp.cooperativeMultiDeviceLaunch ? "Yes" : "No");
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printf(" Device PCI Domain ID / Bus ID / location ID: %d / %d / %d\n",
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deviceProp.pciDomainID, deviceProp.pciBusID, deviceProp.pciDeviceID);
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const char *sComputeMode[] = {
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"Default (multiple host threads can use ::cudaSetDevice() with device "
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"simultaneously)",
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"Exclusive (only one host thread in one process is able to use "
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"::cudaSetDevice() with this device)",
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"Prohibited (no host thread can use ::cudaSetDevice() with this "
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"device)",
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"Exclusive Process (many threads in one process is able to use "
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"::cudaSetDevice() with this device)",
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"Unknown", NULL};
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printf(" Compute Mode:\n");
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printf(" < %s >\n", sComputeMode[deviceProp.computeMode]);
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}
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// If there are 2 or more GPUs, query to determine whether RDMA is supported
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if (deviceCount >= 2) {
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cudaDeviceProp prop[64];
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int gpuid[64]; // we want to find the first two GPUs that can support P2P
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int gpu_p2p_count = 0;
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for (int i = 0; i < deviceCount; i++) {
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checkCudaErrors(cudaGetDeviceProperties(&prop[i], i));
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// Only boards based on Fermi or later can support P2P
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if ((prop[i].major >= 2)
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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// on Windows (64-bit), the Tesla Compute Cluster driver for windows
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// must be enabled to support this
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&& prop[i].tccDriver
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#endif
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) {
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// This is an array of P2P capable GPUs
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gpuid[gpu_p2p_count++] = i;
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}
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}
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// Show all the combinations of support P2P GPUs
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int can_access_peer;
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if (gpu_p2p_count >= 2) {
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for (int i = 0; i < gpu_p2p_count; i++) {
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for (int j = 0; j < gpu_p2p_count; j++) {
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if (gpuid[i] == gpuid[j]) {
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continue;
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}
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checkCudaErrors(
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cudaDeviceCanAccessPeer(&can_access_peer, gpuid[i], gpuid[j]));
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printf("> Peer access from %s (GPU%d) -> %s (GPU%d) : %s\n",
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prop[gpuid[i]].name, gpuid[i], prop[gpuid[j]].name, gpuid[j],
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can_access_peer ? "Yes" : "No");
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}
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}
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}
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}
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// csv masterlog info
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// *****************************
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// exe and CUDA driver name
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printf("\n");
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std::string sProfileString = "deviceQuery, CUDA Driver = CUDART";
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char cTemp[16];
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// driver version
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sProfileString += ", CUDA Driver Version = ";
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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sprintf_s(cTemp, 10, "%d.%d", driverVersion / 1000,
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(driverVersion % 100) / 10);
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#else
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snprintf(cTemp, sizeof(cTemp), "%d.%d", driverVersion / 1000,
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(driverVersion % 100) / 10);
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#endif
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sProfileString += cTemp;
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// Runtime version
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sProfileString += ", CUDA Runtime Version = ";
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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sprintf_s(cTemp, 10, "%d.%d", runtimeVersion / 1000,
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(runtimeVersion % 100) / 10);
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#else
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snprintf(cTemp, sizeof(cTemp), "%d.%d", runtimeVersion / 1000,
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(runtimeVersion % 100) / 10);
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#endif
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sProfileString += cTemp;
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// Device count
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sProfileString += ", NumDevs = ";
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#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
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sprintf_s(cTemp, 10, "%d", deviceCount);
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#else
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snprintf(cTemp, sizeof(cTemp), "%d", deviceCount);
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#endif
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sProfileString += cTemp;
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sProfileString += "\n";
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printf("%s", sProfileString.c_str());
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printf("Result = PASS\n");
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// finish
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exit(EXIT_SUCCESS);
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}
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