LLVM 編譯器/安裝
< LLVM 編譯器
在 Ubuntu 上
- sudo apt-get install clang llvm
硬體/作業系統
- Azure VM | 標準 NC6_Promo (6 個 vcpu，56 GiB 記憶體)
- Tesla K80，具有 3.7 的計算能力。您可以透過 lshw -C display 找出您獲得的卡；您可以找到您卡的計算能力 https://developer.nvidia.com/cuda-gpus。
- Linux (ubuntu 18.04)
sudo apt update sudo apt install build-essential sudo apt install cmake sudo apt install -y libelf-dev libffi-dev sudo apt install -y pkg-config
安裝 CUDA 10.2，使用說明 https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=debnetwork
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" sudo apt-get update sudo apt-get -y install cuda
安裝後，匯出兩個環境變數，vim ~/.bashrc 新增兩行
- export PATH=$PATH:/usr/local/cuda-10.2/bin/
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib/
假設您位於您的主目錄中
cd
git clone https://github.com/llvm/llvm-project.git
mkdir build
cd build/
cmake -DLLVM_ENABLE_PROJECTS="clang;clang-tools-extra;libcxx;libcxxabi;lld;openmp" \
-DCMAKE_BUILD_TYPE=Release \
-DLLVM_TARGETS_TO_BUILD="X86;NVPTX" \
-DCMAKE_INSTALL_PREFIX=$(pwd)/../llvm \
-DCLANG_OPENMP_NVPTX_DEFAULT_ARCH=sm_37 \
-DLIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=35,37,50,52,60,61,70,75 \
-DCMAKE_C_COMPILER=gcc \
-DCMAKE_CXX_COMPILER=g++ \
-G "Unix Makefiles" ../llvm-project/llvm
time make -j
time make -j install
配置選項說明
- -DCMAKE_C_COMPILER=gcc // 用於編譯 clang/llvm 的 C 編譯器，GCC
- -DCMAKE_CXX_COMPILER=g++ // 用於編譯 clang/llvm 的 C++ 編譯器：G++
- -DLLVM_TARGETS_TO_BUILD=X86;PowerPC;NVPTX;AMDGPU // 明確指定要支援的目標裝置，英特爾、英偉達、IBM 和 AMD CPU 或 GPU
- -DCLANG_OPENMP_NVPTX_DEFAULT_ARCH=sm_70 // 要支援的預設 GPU 計算能力版本，https://developer.nvidia.com/cuda-gpus 列出了此類資訊。
- -DLIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=37,60,70 // libomptarget 中要構建的所有 GPU 計算能力版本
其他可選選項
- -DGCC_INSTALL_PREFIX=${GCC_PATH}
- -DCMAKE_C_COMPILER=${GCC_PATH}/bin/gcc
- -DCMAKE_CXX_COMPILER=${GCC_PATH}/bin/g++
- -DCMAKE_Fortran_COMPILER=${GCC_PATH}/bin/gfortran
- -DCUDA_PATH= // 如果 cuda 在您的搜尋路徑中，此選項應自動設定
- -DCUDA_TOOLKIT_ROOT_DIR= // 如果 cuda 在您的搜尋路徑中，此選項應自動設定
- -DOPENMP_ENABLE_LIBOMPTARGET=ON // 此選項應預設開啟
- -DLIBOMP_FORTRAN_MODULES=ON
- -DBUILD_SHARED_LIBS=OFF // 關閉共享庫
更多示例
- cmake -G Ninja -DLLVM_ENABLE_PROJECTS="clang;clang-tools-extra;libcxx;libcxxabi;lld;openmp" -DCMAKE_INSTALL_PREFIX=/Users/abc/llvm-research/inst-10.0.1 -DCMAKE_BUILD_TYPE=Debug ../llvm/
- ninja -j8
- ninja install
將路徑新增到已安裝的 clang，vim ~/.bashrc
export PATH=~/llvm/bin:$PATH export LD_LIBRARY_PATH=~/llvm/lib:$LD_LIBRARY_PATH cd build-openmp cd build-openmp/ cmake -DLLVM_ENABLE_PROJECTS="clang;clang-tools-extra;libcxx;libcxxabi;lld;openmp" \ -DCMAKE_BUILD_TYPE=Release \ -DLLVM_TARGETS_TO_BUILD="X86;NVPTX" \ -DCMAKE_INSTALL_PREFIX=$(pwd)/../llvm \ -DCLANG_OPENMP_NVPTX_DEFAULT_ARCH=sm_37 \ -DLIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=35,37,50,52,60,61,70,75 \ -DCMAKE_C_COMPILER=clang \ -DCMAKE_CXX_COMPILER=clang++ \ -G "Unix Makefiles" ../llvm-project/llvm make -j make -j install
可選選項，明確開啟 bitcode 庫，以及編譯器/連結器來構建它
- -DLIBOMPTARGET_NVPTX_ENABLE_BCLIB=true
- -DLIBOMPTARGET_NVPTX_CUDA_COMPILER=${PREFIX}/bin/clang
- -DLIBOMPTARGET_NVPTX_BC_LINKER=${PREFIX}/bin/llvm-link
硬體/作業系統
- Azure VM | 標準 NC6_Promo (6 個 vcpu，56 GiB 記憶體)
- Tesla K80，具有 3.7 的計算能力。您可以透過 lshw -C display 找出您獲得的卡；您可以在這裡找到您卡的計算能力。
- Linux (ubuntu 18.04)
sudo apt update sudo apt install build-essential sudo apt install cmake sudo apt install -y libelf-dev libffi-dev sudo apt install -y pkg-config
安裝 CUDA 10.2，使用說明 https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=debnetwork
假設您當前的路徑是
- /home/ubuntu/omp5-gpu-llvm
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" sudo apt-get update sudo apt-get -y install cuda
安裝後，匯出兩個環境變數，vim ~/.bashrc 新增兩行
- export PATH=$PATH:/usr/local/cuda-10.2/bin/
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.2/lib/
三個步驟來下載、解壓並將其放入正確的位置。
wget https://github.com/llvm/llvm-project/releases/download/llvmorg-10.0.0/llvm-10.0.0.src.tar.xz wget https://github.com/llvm/llvm-project/releases/download/llvmorg-10.0.0/clang-10.0.0.src.tar.xz wget https://github.com/llvm/llvm-project/releases/download/llvmorg-10.0.0/openmp-10.0.0.src.tar.xz wget https://github.com/llvm/llvm-project/releases/download/llvmorg-10.0.0/compiler-rt-10.0.0.src.tar.xz tar xf llvm-10.0.0.src.tar.xz tar xf clang-10.0.0.src.tar.xz tar xf openmp-10.0.0.src.tar.xz tar xf compiler-rt-10.0.0.src.tar.xz mv clang-10.0.0.src llvm-10.0.0.src/tools/clang mv openmp-10.0.0.src llvm-10.0.0.src/projects/openmp mv compiler-rt-10.0.0.src llvm-10.0.0.src/projects/compiler-rt
最後，目錄佈局應該如下所示
- llvm-10.0.0.src
- tools/clang
- projects/openmp
- projects/compiler-rt
您需要知道 GPU 的計算能力版本。 https://developer.nvidia.com/cuda-gpus 列出了此類資訊。例如，一些典型的 GPU 及其 CC 版本是
- Tesla K80 3.7，sm_37
- Tesla P100 6.0，sm_37
- Tesla V100 7.0，sm_37
mkdir build
cd build
# this step is to generate a make file using cmake. picking gcc/g++ as the compiler
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=$(pwd)/../install \
-DCLANG_OPENMP_NVPTX_DEFAULT_ARCH=sm_37 \
-DCMAKE_C_COMPILER=gcc \
-DCMAKE_CXX_COMPILER=g++ \
-DLIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=37,60,70 ../llvm-10.0.0.src
# the screen output of the step above should show the following info:
# -- Found LIBOMPTARGET_DEP_CUDA_DRIVER: /usr/lib/x86_64-linux-gnu/libcuda.so
# -- LIBOMPTARGET: Building offloading runtime library libomptarget.
# -- LIBOMPTARGET: Building CUDA offloading plugin.
# -- LIBOMPTARGET: Building x86_64 offloading plugin.
make -j6
make install -j6
安裝後，您應該再次擴充套件您的 PATH 和 LD_LIBRARY_PATH
- export PATH=$PATH:/home/ubuntu/omp5-gpu-llvm/install/bin
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/ubuntu/omp5-gpu-llvm/install/lib
您應該使用新安裝的 clang 來重建 OpenMP 執行時庫
cd /home/ubuntu/omp5-gpu-llvm mkdir build-openmp cd build-openmp cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=$(pwd)/../install \ -DCMAKE_C_COMPILER=$(pwd)/../install/bin/clang \ -DCMAKE_CXX_COMPILER=$(pwd)/../install/bin/clang++ \ -DLIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=37,60,70 \ ../llvm-10.0.0.src/projects/openmp make -j6 make install -j6
將以下程式碼儲存到名為 ongpu.c 的檔案中
#include <stdio.h>
#include <omp.h>
int main()
{
int runningOnGPU = 0;
/* Test if GPU is available using OpenMP4.5 */
#pragma omp target map(from:runningOnGPU)
{
if (omp_is_initial_device() == 0)
runningOnGPU = 1;
}
/* If still running on CPU, GPU must not be available */
if (runningOnGPU)
printf("### Able to use the GPU! ### \n");
else
printf("### Unable to use the GPU, using CPU! ###\n");
return 0;
}
編譯並執行它
clang -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda ongpu.c ./a.out ### Able to use the GPU! ###
./a.out: 錯誤 while loading shared libraries: libomp.so: 無法開啟共享物件檔案: 沒有此檔案或目錄 解決方法
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/pathTo/installed-llvm/lib
- OpenMP 5 GPU offloading 指令 [Fabio]: https://github.com/devitocodes/devito/wiki/OpenMP-5-GPU-offloading-instructions--%5BFabio%5D
- Clang_with_OpenMP_Offloading_to_NVIDIA_GPUs: https://hpc-wiki.info/hpc/Building_LLVM/Clang_with_OpenMP_Offloading_to_NVIDIA_GPUs
- OpenMP 5 GPU offloading 指令與 Clang 11 [Italo] https://github.com/devitocodes/devito/wiki/OpenMP-5-GPU-offloading-instructions-with-Clang-11-%5BItalo%5D
- 一個展示 LLVM 安裝的 docker 檔案: https://github.com/freeCompilerCamp/play-with-compiler/blob/master/dockerfiles/dind/Dockerfile.llvm10