add and update samples for CUDA 11.6

This commit is contained in:
Rutwik Choughule
2022-01-13 11:35:24 +05:30
parent 11de19f00c
commit 2e41896e1b
4615 changed files with 178150 additions and 181350 deletions

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{
"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
}

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{
"recommendations": [
"nvidia.nsight-vscode-edition",
"ms-vscode.cpptools",
"ms-vscode.makefile-tools"
]
}

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{
"configurations": [
{
"name": "CUDA C++: Launch",
"type": "cuda-gdb",
"request": "launch",
"program": "${workspaceFolder}/batchCUBLAS"
}
]
}

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{
"version": "2.0.0",
"tasks": [
{
"label": "sample",
"type": "shell",
"command": "make dbg=1",
"problemMatcher": ["$nvcc"],
"group": {
"kind": "build",
"isDefault": true
}
}
]
}

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################################################################################
# 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.
#
################################################################################
#
# Makefile project only supported on Mac OS X and Linux Platforms)
#
################################################################################
# Location of the CUDA Toolkit
CUDA_PATH ?= /usr/local/cuda
##############################
# start deprecated interface #
##############################
ifeq ($(x86_64),1)
$(info WARNING - x86_64 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=x86_64 instead)
TARGET_ARCH ?= x86_64
endif
ifeq ($(ARMv7),1)
$(info WARNING - ARMv7 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=armv7l instead)
TARGET_ARCH ?= armv7l
endif
ifeq ($(aarch64),1)
$(info WARNING - aarch64 variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=aarch64 instead)
TARGET_ARCH ?= aarch64
endif
ifeq ($(ppc64le),1)
$(info WARNING - ppc64le variable has been deprecated)
$(info WARNING - please use TARGET_ARCH=ppc64le instead)
TARGET_ARCH ?= ppc64le
endif
ifneq ($(GCC),)
$(info WARNING - GCC variable has been deprecated)
$(info WARNING - please use HOST_COMPILER=$(GCC) instead)
HOST_COMPILER ?= $(GCC)
endif
ifneq ($(abi),)
$(error ERROR - abi variable has been removed)
endif
############################
# end deprecated interface #
############################
# architecture
HOST_ARCH := $(shell uname -m)
TARGET_ARCH ?= $(HOST_ARCH)
ifneq (,$(filter $(TARGET_ARCH),x86_64 aarch64 sbsa ppc64le armv7l))
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifneq (,$(filter $(TARGET_ARCH),x86_64 aarch64 sbsa ppc64le))
TARGET_SIZE := 64
else ifneq (,$(filter $(TARGET_ARCH),armv7l))
TARGET_SIZE := 32
endif
else
TARGET_SIZE := $(shell getconf LONG_BIT)
endif
else
$(error ERROR - unsupported value $(TARGET_ARCH) for TARGET_ARCH!)
endif
# sbsa and aarch64 systems look similar. Need to differentiate them at host level for now.
ifeq ($(HOST_ARCH),aarch64)
ifeq ($(CUDA_PATH)/targets/sbsa-linux,$(shell ls -1d $(CUDA_PATH)/targets/sbsa-linux 2>/dev/null))
HOST_ARCH := sbsa
TARGET_ARCH := sbsa
endif
endif
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq (,$(filter $(HOST_ARCH)-$(TARGET_ARCH),aarch64-armv7l x86_64-armv7l x86_64-aarch64 x86_64-sbsa x86_64-ppc64le))
$(error ERROR - cross compiling from $(HOST_ARCH) to $(TARGET_ARCH) is not supported!)
endif
endif
# When on native aarch64 system with userspace of 32-bit, change TARGET_ARCH to armv7l
ifeq ($(HOST_ARCH)-$(TARGET_ARCH)-$(TARGET_SIZE),aarch64-aarch64-32)
TARGET_ARCH = armv7l
endif
# operating system
HOST_OS := $(shell uname -s 2>/dev/null | tr "[:upper:]" "[:lower:]")
TARGET_OS ?= $(HOST_OS)
ifeq (,$(filter $(TARGET_OS),linux darwin qnx android))
$(error ERROR - unsupported value $(TARGET_OS) for TARGET_OS!)
endif
# host compiler
ifeq ($(TARGET_OS),darwin)
ifeq ($(shell expr `xcodebuild -version | grep -i xcode | awk '{print $$2}' | cut -d'.' -f1` \>= 5),1)
HOST_COMPILER ?= clang++
endif
else ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq ($(HOST_ARCH)-$(TARGET_ARCH),x86_64-armv7l)
ifeq ($(TARGET_OS),linux)
HOST_COMPILER ?= arm-linux-gnueabihf-g++
else ifeq ($(TARGET_OS),qnx)
ifeq ($(QNX_HOST),)
$(error ERROR - QNX_HOST must be passed to the QNX host toolchain)
endif
ifeq ($(QNX_TARGET),)
$(error ERROR - QNX_TARGET must be passed to the QNX target toolchain)
endif
export QNX_HOST
export QNX_TARGET
HOST_COMPILER ?= $(QNX_HOST)/usr/bin/arm-unknown-nto-qnx6.6.0eabi-g++
else ifeq ($(TARGET_OS),android)
HOST_COMPILER ?= arm-linux-androideabi-g++
endif
else ifeq ($(TARGET_ARCH),aarch64)
ifeq ($(TARGET_OS), linux)
HOST_COMPILER ?= aarch64-linux-gnu-g++
else ifeq ($(TARGET_OS),qnx)
ifeq ($(QNX_HOST),)
$(error ERROR - QNX_HOST must be passed to the QNX host toolchain)
endif
ifeq ($(QNX_TARGET),)
$(error ERROR - QNX_TARGET must be passed to the QNX target toolchain)
endif
export QNX_HOST
export QNX_TARGET
HOST_COMPILER ?= $(QNX_HOST)/usr/bin/q++
else ifeq ($(TARGET_OS), android)
HOST_COMPILER ?= aarch64-linux-android-clang++
endif
else ifeq ($(TARGET_ARCH),sbsa)
HOST_COMPILER ?= aarch64-linux-gnu-g++
else ifeq ($(TARGET_ARCH),ppc64le)
HOST_COMPILER ?= powerpc64le-linux-gnu-g++
endif
endif
HOST_COMPILER ?= g++
NVCC := $(CUDA_PATH)/bin/nvcc -ccbin $(HOST_COMPILER)
# internal flags
NVCCFLAGS := -m${TARGET_SIZE}
CCFLAGS :=
LDFLAGS :=
# build flags
ifeq ($(TARGET_OS),darwin)
LDFLAGS += -rpath $(CUDA_PATH)/lib
CCFLAGS += -arch $(HOST_ARCH)
else ifeq ($(HOST_ARCH)-$(TARGET_ARCH)-$(TARGET_OS),x86_64-armv7l-linux)
LDFLAGS += --dynamic-linker=/lib/ld-linux-armhf.so.3
CCFLAGS += -mfloat-abi=hard
else ifeq ($(TARGET_OS),android)
LDFLAGS += -pie
CCFLAGS += -fpie -fpic -fexceptions
endif
ifneq ($(TARGET_ARCH),$(HOST_ARCH))
ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-linux)
ifneq ($(TARGET_FS),)
GCCVERSIONLTEQ46 := $(shell expr `$(HOST_COMPILER) -dumpversion` \<= 4.6)
ifeq ($(GCCVERSIONLTEQ46),1)
CCFLAGS += --sysroot=$(TARGET_FS)
endif
LDFLAGS += --sysroot=$(TARGET_FS)
LDFLAGS += -rpath-link=$(TARGET_FS)/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib/arm-linux-gnueabihf
endif
endif
ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-linux)
ifneq ($(TARGET_FS),)
GCCVERSIONLTEQ46 := $(shell expr `$(HOST_COMPILER) -dumpversion` \<= 4.6)
ifeq ($(GCCVERSIONLTEQ46),1)
CCFLAGS += --sysroot=$(TARGET_FS)
endif
LDFLAGS += --sysroot=$(TARGET_FS)
LDFLAGS += -rpath-link=$(TARGET_FS)/lib -L$(TARGET_FS)/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/lib/aarch64-linux-gnu -L$(TARGET_FS)/lib/aarch64-linux-gnu
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib -L$(TARGET_FS)/usr/lib
LDFLAGS += -rpath-link=$(TARGET_FS)/usr/lib/aarch64-linux-gnu -L$(TARGET_FS)/usr/lib/aarch64-linux-gnu
LDFLAGS += --unresolved-symbols=ignore-in-shared-libs
CCFLAGS += -isystem=$(TARGET_FS)/usr/include -I$(TARGET_FS)/usr/include -I$(TARGET_FS)/usr/include/libdrm
CCFLAGS += -isystem=$(TARGET_FS)/usr/include/aarch64-linux-gnu -I$(TARGET_FS)/usr/include/aarch64-linux-gnu
endif
endif
ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-qnx)
NVCCFLAGS += -D_QNX_SOURCE
NVCCFLAGS += --qpp-config 8.3.0,gcc_ntoaarch64le
CCFLAGS += -DWIN_INTERFACE_CUSTOM -I/usr/include/aarch64-qnx-gnu
LDFLAGS += -lsocket
LDFLAGS += -L/usr/lib/aarch64-qnx-gnu
CCFLAGS += "-Wl\,-rpath-link\,/usr/lib/aarch64-qnx-gnu"
ifdef TARGET_OVERRIDE
LDFLAGS += -lslog2
endif
ifneq ($(TARGET_FS),)
LDFLAGS += -L$(TARGET_FS)/usr/lib
CCFLAGS += "-Wl\,-rpath-link\,$(TARGET_FS)/usr/lib"
LDFLAGS += -L$(TARGET_FS)/usr/libnvidia
CCFLAGS += "-Wl\,-rpath-link\,$(TARGET_FS)/usr/libnvidia"
CCFLAGS += -I$(TARGET_FS)/../include
endif
endif
endif
ifdef TARGET_OVERRIDE # cuda toolkit targets override
NVCCFLAGS += -target-dir $(TARGET_OVERRIDE)
endif
# Install directory of different arch
CUDA_INSTALL_TARGET_DIR :=
ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-linux)
CUDA_INSTALL_TARGET_DIR = targets/armv7-linux-gnueabihf/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-linux)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-linux/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),sbsa-linux)
CUDA_INSTALL_TARGET_DIR = targets/sbsa-linux/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-android)
CUDA_INSTALL_TARGET_DIR = targets/armv7-linux-androideabi/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-android)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-linux-androideabi/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),armv7l-qnx)
CUDA_INSTALL_TARGET_DIR = targets/ARMv7-linux-QNX/
else ifeq ($(TARGET_ARCH)-$(TARGET_OS),aarch64-qnx)
CUDA_INSTALL_TARGET_DIR = targets/aarch64-qnx/
else ifeq ($(TARGET_ARCH),ppc64le)
CUDA_INSTALL_TARGET_DIR = targets/ppc64le-linux/
endif
# Debug build flags
ifeq ($(dbg),1)
NVCCFLAGS += -g -G
BUILD_TYPE := debug
else
BUILD_TYPE := release
endif
ALL_CCFLAGS :=
ALL_CCFLAGS += $(NVCCFLAGS)
ALL_CCFLAGS += $(EXTRA_NVCCFLAGS)
ALL_CCFLAGS += $(addprefix -Xcompiler ,$(CCFLAGS))
ALL_CCFLAGS += $(addprefix -Xcompiler ,$(EXTRA_CCFLAGS))
ALL_LDFLAGS :=
ALL_LDFLAGS += $(ALL_CCFLAGS)
ALL_LDFLAGS += $(addprefix -Xlinker ,$(LDFLAGS))
ALL_LDFLAGS += $(addprefix -Xlinker ,$(EXTRA_LDFLAGS))
# Common includes and paths for CUDA
INCLUDES := -I../../../Common
LIBRARIES :=
################################################################################
# Gencode arguments
SMS ?=
ifeq ($(GENCODE_FLAGS),)
# Generate SASS code for each SM architecture listed in $(SMS)
$(foreach sm,$(SMS),$(eval GENCODE_FLAGS += -gencode arch=compute_$(sm),code=sm_$(sm)))
ifeq ($(SMS),)
ifeq ($(TARGET_ARCH),$(filter $(TARGET_ARCH),armv7l aarch64 sbsa))
# Generate PTX code from SM 53
GENCODE_FLAGS += -gencode arch=compute_53,code=compute_53
else
# Generate PTX code from SM 35
GENCODE_FLAGS += -gencode arch=compute_35,code=compute_35
endif
endif
# Generate PTX code from the highest SM architecture in $(SMS) to guarantee forward-compatibility
HIGHEST_SM := $(lastword $(sort $(SMS)))
ifneq ($(HIGHEST_SM),)
GENCODE_FLAGS += -gencode arch=compute_$(HIGHEST_SM),code=compute_$(HIGHEST_SM)
endif
endif
ALL_CCFLAGS += --threads 0 --std=c++11
LIBRARIES += -lcublas
################################################################################
# Target rules
all: build
build: batchCUBLAS
batchCUBLAS.o:batchCUBLAS.cpp
$(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
batchCUBLAS: batchCUBLAS.o
$(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
mkdir -p ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
cp $@ ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
run: build
./batchCUBLAS
testrun: build
clean:
rm -f batchCUBLAS batchCUBLAS.o
rm -rf ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/batchCUBLAS
clobber: clean

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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE entry SYSTEM "SamplesInfo.dtd">
<entry>
<name>batchCUBLAS</name>
<cuda_api_list>
<driver>cuRand</driver>
<driver>cuEqual</driver>
<toolkit>cudaFree</toolkit>
<toolkit>cudaStreamCreate</toolkit>
<toolkit>cudaDeviceSynchronize</toolkit>
<toolkit>cudaMalloc</toolkit>
<toolkit>cudaGetLastError</toolkit>
<toolkit>cudaMemcpy</toolkit>
<toolkit>cudaGetErrorString</toolkit>
<toolkit>cudaGetDeviceProperties</toolkit>
<toolkit>cudaGetDevice</toolkit>
</cuda_api_list>
<description><![CDATA[A CUDA Sample that demonstrates how using batched CUBLAS API calls to improve overall performance.]]></description>
<devicecompilation>whole</devicecompilation>
<fallback_min_ptx>true</fallback_min_ptx>
<includepaths>
<path>./</path>
<path>../</path>
<path>../../../Common</path>
</includepaths>
<keyconcepts>
<concept level="basic">Linear Algebra</concept>
<concept level="basic">CUBLAS Library</concept>
</keyconcepts>
<keywords>
<keyword>CUBLAS</keyword>
<keyword>Linear Algebra</keyword>
</keywords>
<libraries>
<library>cublas</library>
</libraries>
<librarypaths>
</librarypaths>
<nsight_eclipse>true</nsight_eclipse>
<primary_file>batchCUBLAS.cpp</primary_file>
<required_dependencies>
<dependency>CUBLAS</dependency>
</required_dependencies>
<scopes>
<scope>1:CUDA Basic Topics</scope>
<scope>3:Linear Algebra</scope>
</scopes>
<sm-arch>sm35</sm-arch>
<sm-arch>sm37</sm-arch>
<sm-arch>sm50</sm-arch>
<sm-arch>sm52</sm-arch>
<sm-arch>sm53</sm-arch>
<sm-arch>sm60</sm-arch>
<sm-arch>sm61</sm-arch>
<sm-arch>sm70</sm-arch>
<sm-arch>sm72</sm-arch>
<sm-arch>sm75</sm-arch>
<sm-arch>sm80</sm-arch>
<sm-arch>sm86</sm-arch>
<sm-arch>sm87</sm-arch>
<supported_envs>
<env>
<arch>x86_64</arch>
<platform>linux</platform>
</env>
<env>
<platform>windows7</platform>
</env>
<env>
<arch>x86_64</arch>
<platform>macosx</platform>
</env>
<env>
<arch>arm</arch>
</env>
<env>
<arch>sbsa</arch>
</env>
<env>
<arch>ppc64le</arch>
<platform>linux</platform>
</env>
</supported_envs>
<supported_sm_architectures>
<include>all</include>
</supported_sm_architectures>
<title>batchCUBLAS</title>
<type>exe</type>
</entry>

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# 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 3.5 ](https://developer.nvidia.com/cuda-gpus) [SM 3.7 ](https://developer.nvidia.com/cuda-gpus) [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)
## Supported OSes
Linux, Windows
## Supported CPU Architecture
x86_64, ppc64le, 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)
cudaFree, cudaStreamCreate, cudaDeviceSynchronize, cudaMalloc, cudaGetLastError, cudaMemcpy, cudaGetErrorString, cudaGetDeviceProperties, cudaGetDevice
## Dependencies needed to build/run
[CUBLAS](../../README.md#cublas)
## Prerequisites
Download and install the [CUDA Toolkit 11.6](https://developer.nvidia.com/cuda-downloads) for your corresponding platform.
Make sure the dependencies mentioned in [Dependencies]() section above are installed.
## Build and Run
### Windows
The Windows samples are built using the Visual Studio IDE. Solution files (.sln) are provided for each supported version of Visual Studio, using the format:
```
*_vs<version>.sln - for Visual Studio <version>
```
Each individual sample has its own set of solution files in its directory:
To build/examine all the samples at once, the complete solution files should be used. To build/examine a single sample, the individual sample solution files should be used.
> **Note:** Some samples require that the Microsoft DirectX SDK (June 2010 or newer) be installed and that the VC++ directory paths are properly set up (**Tools > Options...**). Check DirectX Dependencies section for details."
### Linux
The Linux samples are built using makefiles. To use the makefiles, change the current directory to the sample directory you wish to build, and run make:
```
$ cd <sample_dir>
$ make
```
The samples makefiles can take advantage of certain options:
* **TARGET_ARCH=<arch>** - cross-compile targeting a specific architecture. Allowed architectures are x86_64, ppc64le, armv7l.
By default, TARGET_ARCH is set to HOST_ARCH. On a x86_64 machine, not setting TARGET_ARCH is the equivalent of setting TARGET_ARCH=x86_64.<br/>
`$ make TARGET_ARCH=x86_64` <br/> `$ make TARGET_ARCH=ppc64le` <br/> `$ make TARGET_ARCH=armv7l` <br/>
See [here](http://docs.nvidia.com/cuda/cuda-samples/index.html#cross-samples) for more details.
* **dbg=1** - build with debug symbols
```
$ make dbg=1
```
* **SMS="A B ..."** - override the SM architectures for which the sample will be built, where `"A B ..."` is a space-delimited list of SM architectures. For example, to generate SASS for SM 50 and SM 60, use `SMS="50 60"`.
```
$ make SMS="50 60"
```
* **HOST_COMPILER=<host_compiler>** - override the default g++ host compiler. See the [Linux Installation Guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#system-requirements) for a list of supported host compilers.
```
$ make HOST_COMPILER=g++
```
## References (for more details)

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/* 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 <ctype.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
#include <float.h>
#endif
/* Using updated (v2) interfaces to cublas and cusparse */
#include <cublas_v2.h>
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_cuda.h>
#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 <typename T_ELEM>
void fillupMatrix(T_ELEM *A, int lda, int rows, int cols, int seed = 0);
template <typename T_ELEM>
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<T_ELEM>(
((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>(float *A, int lda, int rows, int cols,
int seed);
template void fillupMatrix<double>(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 <typename T_ELEM>
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 <typename T_ELEM>
static int TESTGEN(gemm)(const struct gemmOpts *opts, int matrixM, int matrixN,
int matrixK, int &numTests,
struct gemmTestParams<T_ELEM> *params) {
static T_ELEM alpha[] = {cuGet<T_ELEM>(0, 0), cuGet<T_ELEM>(-1, -1),
cuGet<T_ELEM>(1, -2), cuGet<T_ELEM>(2, -1),
cuGet<T_ELEM>(0, -3)};
static T_ELEM beta[] = {cuGet<T_ELEM>(0, 0), cuGet<T_ELEM>(-1, -1),
cuGet<T_ELEM>(1, -2), cuGet<T_ELEM>(2, -1),
cuGet<T_ELEM>(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 <typename T_ELEM>
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<T_ELEM>(i + j);
}
}
}
template <typename T_ELEM>
int test_gemm_loop(struct gemmOpts &opts, float err, double max_relative_error,
cublasHandle_t handle) {
struct gemmTestParams<T_ELEM> 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, &params) ==
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<T_ELEM>(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, &params.alpha,
(const T_ELEM **)devPtrA_dev, rowsA,
(const T_ELEM **)devPtrB_dev, rowsB,
&params.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, &params.alpha, devPtrA[i], rowsA,
devPtrB[i], rowsB, &params.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<float>(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<double>(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<float>(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<double>(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);
}

View File

@@ -0,0 +1,187 @@
/* 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 <cublas_v2.h>
#include <cuda_runtime_api.h>
#include <math.h>
#include <stdlib.h>
#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 <typename T_ELEM>
__inline__ __device__ __host__ T_ELEM cuGet(double);
template <>
__inline__ __device__ __host__ float cuGet<float>(double x) {
return float(x);
}
template <>
__inline__ __device__ __host__ double cuGet<double>(double x) {
return double(x);
}
template <typename T_ELEM>
__inline__ __device__ __host__ T_ELEM cuGet(double, double);
template <>
__inline__ __device__ __host__ float cuGet<float>(double x, double y) {
return float(x);
}
template <>
__inline__ __device__ __host__ double cuGet<double>(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 <windows.h>
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 <stddef.h>
#include <sys/resource.h>
#include <sys/time.h>
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 <stddef.h>
#include <sys/resource.h>
#include <sys/sysctl.h>
#include <sys/time.h>
#include <sys/types.h>
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

View File

@@ -0,0 +1,20 @@

Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2017
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "batchCUBLAS", "batchCUBLAS_vs2017.vcxproj", "{997E0757-EA74-4A4E-A0FC-47D8C8831A15}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|x64 = Debug|x64
Release|x64 = Release|x64
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.ActiveCfg = Debug|x64
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<ClCompile>
<Optimization>MaxSpeed</Optimization>
<RuntimeLibrary>MultiThreaded</RuntimeLibrary>
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<Link>
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<LinkTimeCodeGeneration>UseLinkTimeCodeGeneration</LinkTimeCodeGeneration>
</Link>
<CudaCompile>
<Runtime>MT</Runtime>
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<ClCompile Include="batchCUBLAS.cpp" />
<ClInclude Include="batchCUBLAS.h" />
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@@ -0,0 +1,20 @@

Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2019
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Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|x64 = Debug|x64
Release|x64 = Release|x64
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GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.ActiveCfg = Release|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.Build.0 = Release|x64
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GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE
EndGlobalSection
EndGlobal

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@@ -0,0 +1,108 @@
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<ItemGroup Label="ProjectConfigurations">
<ProjectConfiguration Include="Debug|x64">
<Configuration>Debug</Configuration>
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<ProjectConfiguration Include="Release|x64">
<Configuration>Release</Configuration>
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<PropertyGroup Label="Globals">
<ProjectGuid>{997E0757-EA74-4A4E-A0FC-47D8C8831A15}</ProjectGuid>
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<CudaToolkitCustomDir />
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<PropertyGroup>
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<PropertyGroup Condition="'$(Configuration)'=='Release'">
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<Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
<ImportGroup Label="ExtensionSettings">
<Import Project="$(CUDAPropsPath)\CUDA 11.6.props" />
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<Import Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" />
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<PropertyGroup Label="UserMacros" />
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<IncludePath>$(IncludePath)</IncludePath>
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<CodeAnalysisRules />
<CodeAnalysisRuleAssemblies />
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<Include>./;../../../Common</Include>
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<ItemDefinitionGroup Condition="'$(Configuration)'=='Debug'">
<ClCompile>
<Optimization>Disabled</Optimization>
<RuntimeLibrary>MultiThreadedDebug</RuntimeLibrary>
</ClCompile>
<Link>
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<LinkTimeCodeGeneration>Default</LinkTimeCodeGeneration>
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<CudaCompile>
<Runtime>MTd</Runtime>
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<ItemDefinitionGroup Condition="'$(Configuration)'=='Release'">
<ClCompile>
<Optimization>MaxSpeed</Optimization>
<RuntimeLibrary>MultiThreaded</RuntimeLibrary>
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<Link>
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<LinkTimeCodeGeneration>UseLinkTimeCodeGeneration</LinkTimeCodeGeneration>
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<CudaCompile>
<Runtime>MT</Runtime>
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<ItemGroup>
<ClCompile Include="batchCUBLAS.cpp" />
<ClInclude Include="batchCUBLAS.h" />
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<Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
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<Import Project="$(CUDAPropsPath)\CUDA 11.6.targets" />
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@@ -0,0 +1,20 @@

Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2022
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "batchCUBLAS", "batchCUBLAS_vs2022.vcxproj", "{997E0757-EA74-4A4E-A0FC-47D8C8831A15}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|x64 = Debug|x64
Release|x64 = Release|x64
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.ActiveCfg = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Debug|x64.Build.0 = Debug|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.ActiveCfg = Release|x64
{997E0757-EA74-4A4E-A0FC-47D8C8831A15}.Release|x64.Build.0 = Release|x64
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GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE
EndGlobalSection
EndGlobal

View File

@@ -0,0 +1,108 @@
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<ProjectConfiguration Include="Release|x64">
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<PlatformToolset>v143</PlatformToolset>
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<PropertyGroup Condition="'$(Configuration)'=='Debug'">
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<Link>
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