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}/conjugateGradientMultiDeviceCG"
}
]
}

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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))
SAMPLE_ENABLED := 1
# This sample is not supported on Mac OSX
ifeq ($(TARGET_OS),darwin)
$(info >>> WARNING - conjugateGradientMultiDeviceCG is not supported on Mac OSX - waiving sample <<<)
SAMPLE_ENABLED := 0
endif
# This sample is not supported on ARMv7
ifeq ($(TARGET_ARCH),armv7l)
$(info >>> WARNING - conjugateGradientMultiDeviceCG is not supported on ARMv7 - waiving sample <<<)
SAMPLE_ENABLED := 0
endif
# This sample is not supported on QNX
ifeq ($(TARGET_OS),qnx)
$(info >>> WARNING - conjugateGradientMultiDeviceCG is not supported on QNX - waiving sample <<<)
SAMPLE_ENABLED := 0
endif
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 :=
################################################################################
#Detect if installed version of GCC supports required C++11
ifeq ($(TARGET_OS),linux)
empty :=
space := $(empty) $(empty)
GCCVERSIONSTRING := $(shell expr `$(HOST_COMPILER) -dumpversion`)
#Create version number without "."
GCCVERSION := $(shell expr `echo $(GCCVERSIONSTRING)` | cut -f1 -d.)
GCCVERSION += $(shell expr `echo $(GCCVERSIONSTRING)` | cut -f2 -d.)
GCCVERSION += $(shell expr `echo $(GCCVERSIONSTRING)` | cut -f3 -d.)
# Make sure the version number has at least 3 decimals
GCCVERSION += 00
# Remove spaces from the version number
GCCVERSION := $(subst $(space),$(empty),$(GCCVERSION))
#$(warning $(GCCVERSION))
IS_MIN_VERSION := $(shell expr `echo $(GCCVERSION)` \>= 47000)
ifeq ($(IS_MIN_VERSION), 1)
$(info >>> GCC Version is greater or equal to 4.7.0 <<<)
else
$(info >>> Waiving build. Minimum GCC version required is 4.7.0<<<)
SAMPLE_ENABLED := 0
endif
endif
# Gencode arguments
ifeq ($(TARGET_ARCH),$(filter $(TARGET_ARCH),armv7l aarch64 sbsa))
SMS ?= 61 70 72 75 80 86 87
else
SMS ?= 60 61 70 75 80 86
endif
ifeq ($(SMS),)
$(info >>> WARNING - no SM architectures have been specified - waiving sample <<<)
SAMPLE_ENABLED := 0
endif
ifeq ($(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)))
# 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 += -ewp -maxrregcount=64 --std=c++11 --threads 0
LIBRARIES += -lcudadevrt
ifeq ($(SAMPLE_ENABLED),0)
EXEC ?= @echo "[@]"
endif
################################################################################
# Target rules
all: build
build: conjugateGradientMultiDeviceCG
check.deps:
ifeq ($(SAMPLE_ENABLED),0)
@echo "Sample will be waived due to the above missing dependencies"
else
@echo "Sample is ready - all dependencies have been met"
endif
conjugateGradientMultiDeviceCG.o:conjugateGradientMultiDeviceCG.cu
$(EXEC) $(NVCC) $(INCLUDES) $(ALL_CCFLAGS) $(GENCODE_FLAGS) -o $@ -c $<
conjugateGradientMultiDeviceCG: conjugateGradientMultiDeviceCG.o
$(EXEC) $(NVCC) $(ALL_LDFLAGS) $(GENCODE_FLAGS) -o $@ $+ $(LIBRARIES)
$(EXEC) mkdir -p ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
$(EXEC) cp $@ ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)
run: build
$(EXEC) ./conjugateGradientMultiDeviceCG
testrun: build
clean:
rm -f conjugateGradientMultiDeviceCG conjugateGradientMultiDeviceCG.o
rm -rf ../../../bin/$(TARGET_ARCH)/$(TARGET_OS)/$(BUILD_TYPE)/conjugateGradientMultiDeviceCG
clobber: clean

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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE entry SYSTEM "SamplesInfo.dtd">
<entry>
<name>conjugateGradientMultiDeviceCG</name>
<cflags>
<flag>-ewp</flag>
<flag>-maxrregcount=64</flag>
<flag>--std=c++11</flag>
</cflags>
<cuda_api_list>
<toolkit>cudaDeviceEnablePeerAccess</toolkit>
<toolkit>cudaMemset</toolkit>
<toolkit>cudaFree</toolkit>
<toolkit>cudaMallocManaged</toolkit>
<toolkit>cudaMemPrefetchAsync</toolkit>
<toolkit>cudaHostAlloc</toolkit>
<toolkit>cudaOccupancyMaxActiveBlocksPerMultiprocessor</toolkit>
<toolkit>cudaStreamCreate</toolkit>
<toolkit>cudaGetDeviceCount</toolkit>
<toolkit>cudaFreeHost</toolkit>
<toolkit>cudaSetDevice</toolkit>
<toolkit>cudaDeviceCanAccessPeer</toolkit>
<toolkit>cudaLaunchCooperativeKernel</toolkit>
<toolkit>cudaStreamSynchronize</toolkit>
<toolkit>cudaMemAdvise</toolkit>
<toolkit>cudaGetDeviceProperties</toolkit>
</cuda_api_list>
<description><![CDATA[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.]]></description>
<devicecompilation>whole</devicecompilation>
<includepaths>
<path>./</path>
<path>../</path>
<path>../../../Common</path>
</includepaths>
<keyconcepts>
<concept level="basic">Unified Memory</concept>
<concept level="advanced">Linear Algebra</concept>
<concept level="basic">Cooperative Groups</concept>
<concept level="advanced">MultiDevice Cooperative Groups</concept>
<concept level="advanced">CUBLAS Library</concept>
<concept level="advanced">CUSPARSE Library</concept>
</keyconcepts>
<keywords>
<keyword>CUDA</keyword>
<keyword>Sparse Matrix</keyword>
<keyword>Unified Memory</keyword>
<keyword>Multi-GPU</keyword>
<keyword>CPP11</keyword>
</keywords>
<libraries>
<library>cudadevrt</library>
</libraries>
<librarypaths>
</librarypaths>
<nsight_eclipse>true</nsight_eclipse>
<primary_file>conjugateGradientMultiDeviceCG.cu</primary_file>
<required_dependencies>
<dependency>UVM</dependency>
<dependency>MDCG</dependency>
<dependency>CPP11</dependency>
</required_dependencies>
<scopes>
<scope>1:CUDA Advanced Topics</scope>
<scope>3:Linear Algebra</scope>
</scopes>
<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>
<arch>ppc64le</arch>
<platform>linux</platform>
</env>
<env>
<platform>windows</platform>
</env>
<env>
<platform>aarch64</platform>
</env>
<env>
<platform>sbsa</platform>
</env>
</supported_envs>
<supported_sm_architectures>
<from>6.0</from>
</supported_sm_architectures>
<title>conjugateGradient using MultiDevice Cooperative Groups</title>
<type>exe</type>
</entry>

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# conjugateGradientMultiDeviceCG - conjugateGradient using MultiDevice Cooperative Groups
## Description
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.
## Key Concepts
Unified Memory, Linear Algebra, Cooperative Groups, MultiDevice Cooperative Groups, CUBLAS Library, CUSPARSE Library
## Supported SM Architectures
[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, aarch64
## CUDA APIs involved
### [CUDA Runtime API](http://docs.nvidia.com/cuda/cuda-runtime-api/index.html)
cudaDeviceEnablePeerAccess, cudaMemset, cudaFree, cudaMallocManaged, cudaMemPrefetchAsync, cudaHostAlloc, cudaOccupancyMaxActiveBlocksPerMultiprocessor, cudaStreamCreate, cudaGetDeviceCount, cudaFreeHost, cudaSetDevice, cudaDeviceCanAccessPeer, cudaLaunchCooperativeKernel, cudaStreamSynchronize, cudaMemAdvise, cudaGetDeviceProperties
## Dependencies needed to build/run
[UVM](../../README.md#uvm), [MDCG](../../README.md#mdcg), [CPP11](../../README.md#cpp11)
## 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, aarch64.
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=aarch64` <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 sample implements a conjugate gradient solver on multiple GPU using
* Unified Memory optimized prefetching and usage hints.
*
*/
// includes, system
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <map>
#include <iostream>
#include <set>
#include <utility>
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_cuda.h> // helper function CUDA error checking and initialization
#include <helper_functions.h> // helper for shared functions common to CUDA Samples
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
namespace cg = cooperative_groups;
const char *sSDKname = "conjugateGradientMultiDeviceCG";
#define ENABLE_CPU_DEBUG_CODE 0
#define THREADS_PER_BLOCK 512
__device__ double grid_dot_result = 0.0;
/* 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;
}
// I - contains location of the given non-zero element in the row of the matrix
// J - contains location of the given non-zero element in the column of the
// matrix val - contains values of the given non-zero elements of the matrix
// inputVecX - input vector to be multiplied
// outputVecY - resultant vector
void cpuSpMV(int *I, int *J, float *val, int nnz, int num_rows, float alpha,
float *inputVecX, float *outputVecY) {
for (int i = 0; i < num_rows; i++) {
int num_elems_this_row = I[i + 1] - I[i];
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++) {
output += alpha * val[I[i] + j] * inputVecX[J[I[i] + j]];
}
outputVecY[i] = output;
}
return;
}
float dotProduct(float *vecA, float *vecB, int size) {
float result = 0.0;
for (int i = 0; i < size; i++) {
result = result + (vecA[i] * vecB[i]);
}
return result;
}
void scaleVector(float *vec, float alpha, int size) {
for (int i = 0; i < size; i++) {
vec[i] = alpha * vec[i];
}
}
void saxpy(float *x, float *y, float a, int size) {
for (int i = 0; i < size; i++) {
y[i] = a * x[i] + y[i];
}
}
void cpuConjugateGrad(int *I, int *J, float *val, float *x, float *Ax, float *p,
float *r, int nnz, int N, float tol) {
int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, b, a, na;
cpuSpMV(I, J, val, nnz, N, alpha, x, Ax);
saxpy(Ax, r, alpham1, N);
float r1 = dotProduct(r, r, N);
int k = 1;
while (r1 > tol * tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
scaleVector(p, b, N);
saxpy(r, p, alpha, N);
} else {
for (int i = 0; i < N; i++) p[i] = r[i];
}
cpuSpMV(I, J, val, nnz, N, alpha, p, Ax);
float dot = dotProduct(p, Ax, N);
a = r1 / dot;
saxpy(p, x, a, N);
na = -a;
saxpy(Ax, r, na, N);
r0 = r1;
r1 = dotProduct(r, r, N);
printf("\nCPU code iteration = %3d, residual = %e\n", k, sqrt(r1));
k++;
}
}
// Data filled on CPU needed for MultiGPU operations.
struct MultiDeviceData {
unsigned char *hostMemoryArrivedList;
unsigned int numDevices;
unsigned int deviceRank;
};
// Class used for coordination of multiple devices.
class PeerGroup {
const MultiDeviceData &data;
const cg::grid_group &grid;
__device__ unsigned char load_arrived(unsigned char *arrived) const {
#if __CUDA_ARCH__ < 700
return *(volatile unsigned char *)arrived;
#else
unsigned int result;
asm volatile("ld.acquire.sys.global.u8 %0, [%1];"
: "=r"(result)
: "l"(arrived)
: "memory");
return result;
#endif
}
__device__ void store_arrived(unsigned char *arrived,
unsigned char val) const {
#if __CUDA_ARCH__ < 700
*(volatile unsigned char *)arrived = val;
#else
unsigned int reg_val = val;
asm volatile(
"st.release.sys.global.u8 [%1], %0;" ::"r"(reg_val) "l"(arrived)
: "memory");
// Avoids compiler warnings from unused variable val.
(void)(reg_val = reg_val);
#endif
}
public:
__device__ PeerGroup(const MultiDeviceData &data, const cg::grid_group &grid)
: data(data), grid(grid){};
__device__ unsigned int size() const { return data.numDevices * grid.size(); }
__device__ unsigned int thread_rank() const {
return data.deviceRank * grid.size() + grid.thread_rank();
}
__device__ void sync() const {
grid.sync();
// One thread from each grid participates in the sync.
if (grid.thread_rank() == 0) {
if (data.deviceRank == 0) {
// Leader grid waits for others to join and then releases them.
// Other GPUs can arrive in any order, so the leader have to wait for
// all others.
for (int i = 0; i < data.numDevices - 1; i++) {
while (load_arrived(&data.hostMemoryArrivedList[i]) == 0)
;
}
for (int i = 0; i < data.numDevices - 1; i++) {
store_arrived(&data.hostMemoryArrivedList[i], 0);
}
__threadfence_system();
} else {
// Other grids note their arrival and wait to be released.
store_arrived(&data.hostMemoryArrivedList[data.deviceRank - 1], 1);
while (load_arrived(&data.hostMemoryArrivedList[data.deviceRank - 1]) ==
1)
;
}
}
grid.sync();
}
};
__device__ void gpuSpMV(int *I, int *J, float *val, int nnz, int num_rows,
float alpha, float *inputVecX, float *outputVecY,
const PeerGroup &peer_group) {
for (int i = peer_group.thread_rank(); i < num_rows; i += peer_group.size()) {
int row_elem = I[i];
int next_row_elem = I[i + 1];
int num_elems_this_row = next_row_elem - row_elem;
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++) {
output += alpha * val[row_elem + j] * inputVecX[J[row_elem + j]];
}
outputVecY[i] = output;
}
}
__device__ void gpuSaxpy(float *x, float *y, float a, int size,
const PeerGroup &peer_group) {
for (int i = peer_group.thread_rank(); i < size; i += peer_group.size()) {
y[i] = a * x[i] + y[i];
}
}
__device__ void gpuDotProduct(float *vecA, float *vecB, int size,
const cg::thread_block &cta,
const PeerGroup &peer_group) {
extern __shared__ double tmp[];
double temp_sum = 0.0;
for (int i = peer_group.thread_rank(); i < size; i += peer_group.size()) {
temp_sum += (double)(vecA[i] * vecB[i]);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
tmp[tile32.meta_group_rank()] = temp_sum;
}
cg::sync(cta);
if (tile32.meta_group_rank() == 0) {
temp_sum = tile32.thread_rank() < tile32.meta_group_size()
? tmp[tile32.thread_rank()]
: 0.0;
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
atomicAdd(&grid_dot_result, temp_sum);
}
}
}
__device__ void gpuCopyVector(float *srcA, float *destB, int size,
const PeerGroup &peer_group) {
for (int i = peer_group.thread_rank(); i < size; i += peer_group.size()) {
destB[i] = srcA[i];
}
}
__device__ void gpuScaleVectorAndSaxpy(float *x, float *y, float a, float scale,
int size, const PeerGroup &peer_group) {
for (int i = peer_group.thread_rank(); i < size; i += peer_group.size()) {
y[i] = a * x[i] + scale * y[i];
}
}
extern "C" __global__ void multiGpuConjugateGradient(
int *I, int *J, float *val, float *x, float *Ax, float *p, float *r,
double *dot_result, int nnz, int N, float tol,
MultiDeviceData multi_device_data) {
cg::thread_block cta = cg::this_thread_block();
cg::grid_group grid = cg::this_grid();
PeerGroup peer_group(multi_device_data, grid);
const int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, r1, b, a, na;
for (int i = peer_group.thread_rank(); i < N; i += peer_group.size()) {
r[i] = 1.0;
x[i] = 0.0;
}
cg::sync(grid);
gpuSpMV(I, J, val, nnz, N, alpha, x, Ax, peer_group);
cg::sync(grid);
gpuSaxpy(Ax, r, alpham1, N, peer_group);
cg::sync(grid);
gpuDotProduct(r, r, N, cta, peer_group);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
peer_group.sync();
r1 = *dot_result;
int k = 1;
while (r1 > tol * tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
gpuScaleVectorAndSaxpy(r, p, alpha, b, N, peer_group);
} else {
gpuCopyVector(r, p, N, peer_group);
}
peer_group.sync();
gpuSpMV(I, J, val, nnz, N, alpha, p, Ax, peer_group);
if (peer_group.thread_rank() == 0) {
*dot_result = 0.0;
}
peer_group.sync();
gpuDotProduct(p, Ax, N, cta, peer_group);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
peer_group.sync();
a = r1 / *dot_result;
gpuSaxpy(p, x, a, N, peer_group);
na = -a;
gpuSaxpy(Ax, r, na, N, peer_group);
r0 = r1;
peer_group.sync();
if (peer_group.thread_rank() == 0) {
*dot_result = 0.0;
}
peer_group.sync();
gpuDotProduct(r, r, N, cta, peer_group);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
peer_group.sync();
r1 = *dot_result;
k++;
}
}
// Map of device version to device number
std::multimap<std::pair<int, int>, int> getIdenticalGPUs() {
int numGpus = 0;
checkCudaErrors(cudaGetDeviceCount(&numGpus));
std::multimap<std::pair<int, int>, int> identicalGpus;
for (int i = 0; i < numGpus; i++) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, i));
// Filter unsupported devices
if (deviceProp.cooperativeLaunch && deviceProp.concurrentManagedAccess) {
identicalGpus.emplace(std::make_pair(deviceProp.major, deviceProp.minor),
i);
}
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", i,
deviceProp.name, deviceProp.major, deviceProp.minor);
}
return identicalGpus;
}
int main(int argc, char **argv) {
constexpr size_t kNumGpusRequired = 2;
int N = 0, nz = 0, *I = NULL, *J = NULL;
float *val = NULL;
const float tol = 1e-5f;
float *x;
float rhs = 1.0;
float r1;
float *r, *p, *Ax;
printf("Starting [%s]...\n", sSDKname);
auto gpusByArch = getIdenticalGPUs();
auto it = gpusByArch.begin();
auto end = gpusByArch.end();
auto bestFit = std::make_pair(it, it);
// use std::distance to find the largest number of GPUs amongst architectures
auto distance = [](decltype(bestFit) p) {
return std::distance(p.first, p.second);
};
// Read each unique key/pair element in order
for (; it != end; it = gpusByArch.upper_bound(it->first)) {
// first and second are iterators bounded within the architecture group
auto testFit = gpusByArch.equal_range(it->first);
// Always use devices with highest architecture version or whichever has the
// most devices available
if (distance(bestFit) <= distance(testFit)) bestFit = testFit;
}
if (distance(bestFit) < kNumGpusRequired) {
printf(
"No two or more GPUs with same architecture capable of "
"concurrentManagedAccess found. "
"\nWaiving the sample\n");
exit(EXIT_WAIVED);
}
std::set<int> bestFitDeviceIds;
// Check & select peer-to-peer access capable GPU devices as enabling p2p
// access between participating GPUs gives better performance.
for (auto itr = bestFit.first; itr != bestFit.second; itr++) {
int deviceId = itr->second;
checkCudaErrors(cudaSetDevice(deviceId));
std::for_each(
itr, bestFit.second,
[&deviceId, &bestFitDeviceIds,
&kNumGpusRequired](decltype(*itr) mapPair) {
if (deviceId != mapPair.second) {
int access = 0;
checkCudaErrors(
cudaDeviceCanAccessPeer(&access, deviceId, mapPair.second));
printf("Device=%d %s Access Peer Device=%d\n", deviceId,
access ? "CAN" : "CANNOT", mapPair.second);
if (access && bestFitDeviceIds.size() < kNumGpusRequired) {
bestFitDeviceIds.emplace(deviceId);
bestFitDeviceIds.emplace(mapPair.second);
} else {
printf("Ignoring device %i (max devices exceeded)\n",
mapPair.second);
}
}
});
if (bestFitDeviceIds.size() >= kNumGpusRequired) {
printf("Selected p2p capable devices - ");
for (auto devicesItr = bestFitDeviceIds.begin();
devicesItr != bestFitDeviceIds.end(); devicesItr++) {
printf("deviceId = %d ", *devicesItr);
}
printf("\n");
break;
}
}
// if bestFitDeviceIds.size() == 0 it means the GPUs in system are not p2p
// capable, hence we add it without p2p capability check.
if (!bestFitDeviceIds.size()) {
printf("Devices involved are not p2p capable.. selecting %zu of them\n",
kNumGpusRequired);
std::for_each(bestFit.first, bestFit.second,
[&bestFitDeviceIds,
&kNumGpusRequired](decltype(*bestFit.first) mapPair) {
if (bestFitDeviceIds.size() < kNumGpusRequired) {
bestFitDeviceIds.emplace(mapPair.second);
} else {
printf("Ignoring device %i (max devices exceeded)\n",
mapPair.second);
}
// Insert the sequence into the deviceIds set
});
} else {
// perform cudaDeviceEnablePeerAccess in both directions for all
// participating devices.
for (auto p1_itr = bestFitDeviceIds.begin();
p1_itr != bestFitDeviceIds.end(); p1_itr++) {
checkCudaErrors(cudaSetDevice(*p1_itr));
for (auto p2_itr = bestFitDeviceIds.begin();
p2_itr != bestFitDeviceIds.end(); p2_itr++) {
if (*p1_itr != *p2_itr) {
checkCudaErrors(cudaDeviceEnablePeerAccess(*p2_itr, 0));
checkCudaErrors(cudaSetDevice(*p1_itr));
}
}
}
}
/* Generate a random tridiagonal symmetric matrix in CSR format */
N = 10485760 * 2;
nz = (N - 2) * 3 + 4;
checkCudaErrors(cudaMallocManaged((void **)&I, sizeof(int) * (N + 1)));
checkCudaErrors(cudaMallocManaged((void **)&J, sizeof(int) * nz));
checkCudaErrors(cudaMallocManaged((void **)&val, sizeof(float) * nz));
float *val_cpu = (float *)malloc(sizeof(float) * nz);
genTridiag(I, J, val_cpu, N, nz);
memcpy(val, val_cpu, sizeof(float) * nz);
checkCudaErrors(
cudaMemAdvise(I, sizeof(int) * (N + 1), cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(
cudaMemAdvise(J, sizeof(int) * nz, cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(
cudaMemAdvise(val, sizeof(float) * nz, cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(cudaMallocManaged((void **)&x, sizeof(float) * N));
double *dot_result;
checkCudaErrors(cudaMallocManaged((void **)&dot_result, sizeof(double)));
checkCudaErrors(cudaMemset(dot_result, 0, sizeof(double)));
// temp memory for ConjugateGradient
checkCudaErrors(cudaMallocManaged((void **)&r, N * sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&p, N * sizeof(float)));
checkCudaErrors(cudaMallocManaged((void **)&Ax, N * sizeof(float)));
std::cout << "\nRunning on GPUs = " << kNumGpusRequired << std::endl;
cudaStream_t nStreams[kNumGpusRequired];
int sMemSize = sizeof(double) * ((THREADS_PER_BLOCK / 32) + 1);
int numBlocksPerSm = INT_MAX;
int numThreads = THREADS_PER_BLOCK;
int numSms = INT_MAX;
auto deviceId = bestFitDeviceIds.begin();
// set numSms & numBlocksPerSm to be lowest of 2 devices
while (deviceId != bestFitDeviceIds.end()) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, *deviceId));
int numBlocksPerSm_current = 0;
checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&numBlocksPerSm_current, multiGpuConjugateGradient, numThreads,
sMemSize));
if (numBlocksPerSm > numBlocksPerSm_current) {
numBlocksPerSm = numBlocksPerSm_current;
}
if (numSms > deviceProp.multiProcessorCount) {
numSms = deviceProp.multiProcessorCount;
}
deviceId++;
}
if (!numBlocksPerSm) {
printf(
"Max active blocks per SM is returned as 0.\n Hence, Waiving the "
"sample\n");
exit(EXIT_WAIVED);
}
int device_count = 0;
int totalThreadsPerGPU = numSms * numBlocksPerSm * THREADS_PER_BLOCK;
deviceId = bestFitDeviceIds.begin();
while (deviceId != bestFitDeviceIds.end()) {
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaStreamCreate(&nStreams[device_count]));
int perGPUIter = N / (totalThreadsPerGPU * kNumGpusRequired);
int offset_Ax = device_count * totalThreadsPerGPU;
int offset_r = device_count * totalThreadsPerGPU;
int offset_p = device_count * totalThreadsPerGPU;
int offset_x = device_count * totalThreadsPerGPU;
checkCudaErrors(cudaMemPrefetchAsync(I, sizeof(int) * N, *deviceId,
nStreams[device_count]));
checkCudaErrors(cudaMemPrefetchAsync(val, sizeof(float) * nz, *deviceId,
nStreams[device_count]));
checkCudaErrors(cudaMemPrefetchAsync(J, sizeof(float) * nz, *deviceId,
nStreams[device_count]));
if (offset_Ax <= N) {
for (int i = 0; i < perGPUIter; i++) {
cudaMemAdvise(Ax + offset_Ax, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(r + offset_r, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(x + offset_x, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(p + offset_p, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(Ax + offset_Ax, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(r + offset_r, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(p + offset_p, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(x + offset_x, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
offset_Ax += totalThreadsPerGPU * kNumGpusRequired;
offset_r += totalThreadsPerGPU * kNumGpusRequired;
offset_p += totalThreadsPerGPU * kNumGpusRequired;
offset_x += totalThreadsPerGPU * kNumGpusRequired;
if (offset_Ax >= N) {
break;
}
}
}
device_count++;
deviceId++;
}
#if ENABLE_CPU_DEBUG_CODE
float *Ax_cpu = (float *)malloc(sizeof(float) * N);
float *r_cpu = (float *)malloc(sizeof(float) * N);
float *p_cpu = (float *)malloc(sizeof(float) * N);
float *x_cpu = (float *)malloc(sizeof(float) * N);
for (int i = 0; i < N; i++) {
r_cpu[i] = 1.0;
Ax_cpu[i] = x_cpu[i] = 0.0;
}
#endif
printf("Total threads per GPU = %d numBlocksPerSm = %d\n",
numSms * numBlocksPerSm * THREADS_PER_BLOCK, numBlocksPerSm);
dim3 dimGrid(numSms * numBlocksPerSm, 1, 1),
dimBlock(THREADS_PER_BLOCK, 1, 1);
// Structure used for cross-grid synchronization.
MultiDeviceData multi_device_data;
checkCudaErrors(cudaHostAlloc(
&multi_device_data.hostMemoryArrivedList,
(kNumGpusRequired - 1) * sizeof(*multi_device_data.hostMemoryArrivedList),
cudaHostAllocPortable));
memset(multi_device_data.hostMemoryArrivedList, 0,
(kNumGpusRequired - 1) *
sizeof(*multi_device_data.hostMemoryArrivedList));
multi_device_data.numDevices = kNumGpusRequired;
multi_device_data.deviceRank = 0;
void *kernelArgs[] = {
(void *)&I, (void *)&J, (void *)&val, (void *)&x,
(void *)&Ax, (void *)&p, (void *)&r, (void *)&dot_result,
(void *)&nz, (void *)&N, (void *)&tol, (void *)&multi_device_data,
};
printf("Launching kernel\n");
deviceId = bestFitDeviceIds.begin();
device_count = 0;
while (deviceId != bestFitDeviceIds.end()) {
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaLaunchCooperativeKernel(
(void *)multiGpuConjugateGradient, dimGrid, dimBlock, kernelArgs,
sMemSize, nStreams[device_count++]));
multi_device_data.deviceRank++;
deviceId++;
}
checkCudaErrors(cudaMemPrefetchAsync(x, sizeof(float) * N, cudaCpuDeviceId));
checkCudaErrors(
cudaMemPrefetchAsync(dot_result, sizeof(double), cudaCpuDeviceId));
deviceId = bestFitDeviceIds.begin();
device_count = 0;
while (deviceId != bestFitDeviceIds.end()) {
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaStreamSynchronize(nStreams[device_count++]));
deviceId++;
}
r1 = (float)*dot_result;
printf("GPU Final, residual = %e \n ", sqrt(r1));
#if ENABLE_CPU_DEBUG_CODE
cpuConjugateGrad(I, J, val, x_cpu, Ax_cpu, p_cpu, r_cpu, nz, N, tol);
#endif
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_cpu[j] * x[J[j]];
}
diff = fabs(rsum - rhs);
if (diff > err) {
err = diff;
}
}
checkCudaErrors(cudaFreeHost(multi_device_data.hostMemoryArrivedList));
checkCudaErrors(cudaFree(I));
checkCudaErrors(cudaFree(J));
checkCudaErrors(cudaFree(val));
checkCudaErrors(cudaFree(x));
checkCudaErrors(cudaFree(r));
checkCudaErrors(cudaFree(p));
checkCudaErrors(cudaFree(Ax));
checkCudaErrors(cudaFree(dot_result));
free(val_cpu);
#if ENABLE_CPU_DEBUG_CODE
free(Ax_cpu);
free(r_cpu);
free(p_cpu);
free(x_cpu);
#endif
printf("Test Summary: Error amount = %f \n", err);
fprintf(stdout, "&&&& conjugateGradientMultiDeviceCG %s\n",
(sqrt(r1) < tol) ? "PASSED" : "FAILED");
exit((sqrt(r1) < tol) ? EXIT_SUCCESS : EXIT_FAILURE);
}

View File

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

Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio 2017
Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "conjugateGradientMultiDeviceCG", "conjugateGradientMultiDeviceCG_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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