本篇博文转载于https://www.cnblogs.com/1024incn/tag/CUDA/,仅用于学习。
线程的组织形式对程序的性能影响是至关重要的,本篇博文主要以下面一种情况来介绍线程组织形式:
矩阵在memory中是row-major线性存储的:
在kernel里,线程的唯一索引非常有用,为了确定一个线程的索引,我们以2D为例:
首先可以将thread和block索引映射到矩阵坐标:
ix = threadIdx.x + blockIdx.x * blockDim.x
iy = threadIdx.y + blockIdx.y * blockDim.y
之后可以利用上述变量计算线性地址:
idx = iy * nx + ix
上图展示了block和thread索引,矩阵坐标以及线性地址之间的关系,谨记,相邻的thread拥有连续的threadIdx.x,也就是索引为(0,0)(1,0)(2,0)(3,0)...的thread连续,而不是(0,0)(0,1)(0,2)(0,3)...连续,跟我们线代里玩矩阵的时候不一样。
现在可以验证出下面的关系:
thread_id(2,1)block_id(1,0) coordinate(6,1) global index 14 ival 14
下图显示了三者之间的关系:
int main(int argc, char **argv) {
printf("%s Starting...\n", argv[0]);
// set up device
int dev = 0;
cudaDeviceProp deviceProp;
CHECK(cudaGetDeviceProperties(&deviceProp, dev));
printf("Using Device %d: %s\n", dev, deviceProp.name);
CHECK(cudaSetDevice(dev)); // set up date size of matrix
int nx = 1<<14;
int ny = 1<<14;
int nxy = nx*ny;
int nBytes = nxy * sizeof(float);
printf("Matrix size: nx %d ny %d\n",nx, ny);
// malloc host memory
float *h_A, *h_B, *hostRef, *gpuRef;
h_A = (float *)malloc(nBytes);
h_B = (float *)malloc(nBytes);
hostRef = (float *)malloc(nBytes);
gpuRef = (float *)malloc(nBytes);
// initialize data at host side
double iStart = cpuSecond();
initialData (h_A, nxy);
initialData (h_B, nxy);
double iElaps = cpuSecond() - iStart;
memset(hostRef, 0, nBytes);
memset(gpuRef, 0, nBytes);
// add matrix at host side for result checks
iStart = cpuSecond();
sumMatrixOnHost (h_A, h_B, hostRef, nx,ny);
iElaps = cpuSecond() - iStart;
// malloc device global memory
float *d_MatA, *d_MatB, *d_MatC;
cudaMalloc((void **)&d_MatA, nBytes);
cudaMalloc((void **)&d_MatB, nBytes);
cudaMalloc((void **)&d_MatC, nBytes);
// transfer data from host to device
cudaMemcpy(d_MatA, h_A, nBytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_MatB, h_B, nBytes, cudaMemcpyHostToDevice);
// invoke kernel at host side
int dimx = 32;
int dimy = 32;
dim3 block(dimx, dimy);
dim3 grid((nx+block.x-1)/block.x, (ny+block.y-1)/block.y);
iStart = cpuSecond();
sumMatrixOnGPU2D <<< grid, block >>>(d_MatA, d_MatB, d_MatC, nx, ny);
cudaDeviceSynchronize();
iElaps = cpuSecond() - iStart;
printf("sumMatrixOnGPU2D <<<(%d,%d), (%d,%d)>>> elapsed %f sec\n", grid.x,
grid.y, block.x, block.y, iElaps);
// copy kernel result back to host side
cudaMemcpy(gpuRef, d_MatC, nBytes, cudaMemcpyDeviceToHost);
// check device results
checkResult(hostRef, gpuRef, nxy);
// free device global memory
cudaFree(d_MatA);
cudaFree(d_MatB);
cudaFree(d_MatC);
// free host memory
free(h_A);
free(h_B);
free(hostRef);
free(gpuRef);
// reset device
cudaDeviceReset();
return (0);
}
编译运行:
$ nvcc -arch=sm_20 sumMatrixOnGPU-2D-grid-2D-block.cu -o matrix2D $ ./matrix2D
输出:
./a.out Starting... Using Device 0: Tesla M2070 Matrix size: nx 16384 ny 16384 sumMatrixOnGPU2D <<<(512,512), (32,32)>>> elapsed 0.060323 sec Arrays match.
接下来,我们更改block配置为32x16,重新编译,输出为:
sumMatrixOnGPU2D <<<(512,1024), (32,16)>>> elapsed 0.038041 sec
可以看到,性能提升了一倍,直观的来看,我们会认为第二个配置比第一个多了一倍的block所以性能提升一倍,实际上也确实是因为block增加了。但是,如果你继续增加block的数量,则性能又会降低:
sumMatrixOnGPU2D <<< (1024,1024), (16,16) >>> elapsed 0.045535 sec
下图展示了不同配置的性能;
关于性能的分析将在之后的博文中总结,现在只是了解下,本文在于掌握线程组织的方法。