为了编写cuda程序,本文使用Qt作为IDE,使用cmake构建程序
pro文件如下:
project(CudaWithQt)
cmake_minimum_required(VERSION 2.8)
#packages
#找到cuda的库 可以通过cmake --help-module-list查看可以看到FINDCUDA
find_package(CUDA REQUIRED)
#nvcc flags
set(CUDA_NVCC_FLAGS -gencode arch=compute_20,code=sm_20;-G;-g)
#将要编译的文件,赋值给CURRENT_XX变量
file(GLOB_RECURSE CURRENT_HEADERS *.h *.hpp *.cuh)
file(GLOB CURRENT_SOURCES *.cpp *.cu)
#构建程序
CUDA_ADD_EXECUTABLE(test_cuda_project ${CURRENT_HEADERS} ${CURRENT_SOURCES})
kernel.cu
// CUDA-C includes
#include
#include
#include
extern "C" void runCudaPart();
__global__ void addAry( int * ary1, int * ary2 )
{
int indx = threadIdx.x;
ary1[ indx ] += ary2[ indx ];
}
// Main cuda function
void runCudaPart() {
int ary1[32];
int ary2[32];
int res[32];
for( int i=0 ; i<32 ; i++ )
{
ary1[i] = i;
ary2[i] = 2*i;
res[i]=0;
}
int * d_ary1, *d_ary2;
cudaMalloc((void**)&d_ary1, 32*sizeof(int));
cudaMalloc((void**)&d_ary2, 32*sizeof(int));
cudaMemcpy((void*)d_ary1, (void*)ary1, 32*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy((void*)d_ary2, (void*)ary2, 32*sizeof(int), cudaMemcpyHostToDevice);
addAry<<<1,32>>>(d_ary1,d_ary2);
cudaMemcpy((void*)res, (void*)d_ary1, 32*sizeof(int), cudaMemcpyDeviceToHost);
for( int i=0 ; i<32 ; i++ )
printf( "result[%d] = %d\n", i, res[i]);
cudaFree(d_ary1);
cudaFree(d_ary2);
}
main.cpp
#include
extern "C"
void runCudaPart();
int main(int argc, char *argv[])
{
runCudaPart();
}