#!/bin/bash
USE_CUDA=false
WORK_SPACE=~/xyt/
install_packages() {
echo -e "-------------------- Install packages --------------------"
sudo apt-get install git
sudo apt-get install cmake
sudo apt-get install python3
sudo apt-get install ninja-build
pip install psutil
}
install_onemkl() {
echo -e "-------------------- Install OneMKL --------------------"
if [ -d ~/intel/oneapi/mkl/2021.3.0/ ]; then
echo "OneMKL found"
echo "~/intel/oneapi/mkl/2021.3.0/"
else
wget https://registrationcenter-download.intel.com/akdlm/irc_nas/17901/l_onemkl_p_2021.3.0.520_offline.sh
sh l_onemkl_p_2021.3.0.520_offline.sh
rm -rf l_onemkl_p_2021.3.0.520_offline.sh
fi
}
install_dpcpp() {
echo -e "-------------------- Install CLANG++ and DPCPP --------------------"
export DPCPP_HOME=~/sycl_workspace/
if [ -d $DPCPP_HOME ]; then
echo "CLANG++ and DPCPP found"
echo $DPCPP_HOME
else
mkdir $DPCPP_HOME
cd $DPCPP_HOME
git clone https://github.com.cnpmjs.org/intel/llvm -b sycl
if [ $USE_CUDA ]; then
python $DPCPP_HOME/llvm/buildbot/configure.py --cuda
else
python $DPCPP_HOME/llvm/buildbot/configure.py
fi
python $DPCPP_HOME/llvm/buildbot/configure.py
python $DPCPP_HOME/llvm/buildbot/compile.py
python $DPCPP_HOME/llvm/buildbot/check.py
fi
}
create_env() {
echo -e "-------------------- Create OneAPI env --------------------"
export DPCPP_HOME=~/sycl_workspace/
cd $WORK_SPACE
rm -rf env_oneapi.sh
echo "# TODO: configure DPCPP_ROOT/MKL_DPCPP_ROOT
export DPCPP_ROOT=${DPCPP_HOME}llvm/build
export MKL_DPCPP_ROOT=~/intel/oneapi/mkl/2021.3.0
export LD_LIBRARY_PATH=${DPCPP_ROOT}/lib:${LD_LIBRARY_PATH}
export LD_LIBRARY_PATH=${MKL_DPCPP_ROOT}/lib/ia32:${MKL_DPCPP_ROOT}/lib/intel64:${LD_LIBRARY_PATH}
export PATH=${DPCPP_ROOT}/bin:$PATH
export INTELOCLSDKROOT=${DPCPP_ROOT}
export SYCLROOT=${DPCPP_ROOT}" >> env_oneapi.sh
}
runtime_test() {
echo -e "-------------------- Runtime test --------------------"
cd $WORK_SPACE
rm -rf simple-sycl-app.cpp
rm -rf simple-sycl-app.exe
source env_oneapi.sh
echo "
#include <iostream>
#include <ctime>
#include <math.h>
#include <string>
#include <CL/sycl.hpp>
using namespace std;
using namespace sycl;
clock_t start;
const size_t len = 2048*2048*100;
void output_dev_info( const device& dev, const std::string& selector_name) {
std::cout << selector_name << ": Selected device: " <<
dev.get_info<info::device::name>() << "
";
std::cout << " -> Device vendor: " <<
dev.get_info<info::device::vendor>() << "
";
}
int main() {
output_dev_info( device{ default_selector{}}, "default_selector" );
output_dev_info( device{ host_selector{}}, "host_selector" );
output_dev_info( device{ gpu_selector{}}, "gpu_selector" );
float *a = new float[len]; memset(a, 123, len*sizeof(float));
float *b = new float[len]; memset(b, 22, len*sizeof(float));
float *c = new float[len];
queue Q(gpu_selector{});
float *a_dev = malloc_device<float>(len, Q);
float *b_dev = malloc_device<float>(len, Q);
float *c_dev = malloc_device<float>(len, Q);
start=clock();
for(int i=0;i<len;i++) c[i] = sqrt(a[i]) * b[i];
long time_cpu = clock() - start;
cout << time_cpu << std::endl;
start=clock();
Q.submit([&](handler &h) {
h.parallel_for(len, [=](id<1> i) {
c_dev[i] = sqrt(a_dev[i]) * b_dev[i];
});
}).wait();
long time_xpu = clock() - start;
cout << time_xpu << std::endl;
cout << (float)time_cpu / time_xpu << std::endl;
return 0;
}
" >> simple-sycl-app.cpp
if [ $USE_CUDA ]; then
clang++ -fsycl simple-sycl-app.cpp -o simple-sycl-app.exe
else
clang++ -fsycl -fsycl-targets=nvptx64-nvidia-cuda-sycldevice simple-sycl-app.cpp -o simple-sycl-app.exe
fi
./simple-sycl-app.exe
}
install_packages
install_onemkl
install_dpcpp
create_env
runtime_test