个人的小说网站如何做,微信公众号优惠和网站绑定怎么做,wordpress流量赚钱,怎样做的英文网站在上一篇文章#xff1a;第一个Cuda程序#xff0c;矩阵相乘代码#xff0c;我们设计了一种并行的矩阵乘法程序#xff0c;效果和使用CPU计算的一样#xff0c;但时间有了很大的降低#xff0c;然而#xff0c;这只是最基本的一种方法#xff0c;事实上我们完全可以让程…在上一篇文章第一个Cuda程序矩阵相乘代码我们设计了一种并行的矩阵乘法程序效果和使用CPU计算的一样但时间有了很大的降低然而这只是最基本的一种方法事实上我们完全可以让程序变得更快
仔细看看会发现我们使用的是global memory而share memory的访问速度要远远大于global memory所以我们将使用share memory优化矩阵乘法让程序更快
#include stdio.h#includestdlib.h#include cuda.h#include cuda_runtime.h#include device_launch_parameters.h#include device_functions.h
# define BLOCK_SIZE 8# define M 6# define N 8# define K 6__managed__ float a[M * N];__managed__ float b[N*K];__managed__ float c_gpu[M * K];__managed__ float c_cpu[M * K];__global__ void gpu_matrix(float* a, float* b, float* c, const int m, const int n, const int k){ __shared__ float sub_a[BLOCK_SIZE][BLOCK_SIZE]; __shared__ float sub_b[BLOCK_SIZE][BLOCK_SIZE]; int x threadIdx.x blockIdx.x * blockDim.x; int y threadIdx.y blockIdx.y * blockDim.y; float temp 0.0; int step, i; for (step 0; step N / BLOCK_SIZE; step) { if ((step * BLOCK_SIZE threadIdx.x) N || y M) { sub_a[threadIdx.y][threadIdx.x] 0.0; } else { sub_a[threadIdx.y][threadIdx.x] a[y * N (step * BLOCK_SIZE threadIdx.x)]; }if ((step * BLOCK_SIZE threadIdx.y) N || x K) { sub_b[threadIdx.y][threadIdx.x] 0.0;} else { sub_b[threadIdx.y][threadIdx.x] b[(step * BLOCK_SIZE threadIdx.y) * K x]; } __syncthreads(); for (i 0; i BLOCK_SIZE; i) { temp temp sub_a[threadIdx.y][i] * sub_b[i][threadIdx.x]; } __syncthreads();if (x K y M) { c[y * K x] temp; } }}
void cpu_matrix(float* a, float* b, float* c, const int m, const int n, const int k){ int y, x, step; float temp; for (y 0; y M; y) { for (x 0; x K; x) { temp 0; for (step 0; step N; step) { temp a[y * N step] * b[step * K x]; } c[y * K x] temp; } }}
int main(){ int x,y; float item1; //初始化矩阵 for (y 0; y M; y) { for (x 0; x N; x) { item1 x y; a[y * N x] item1;}} for (y 0; y N; y) { for (x 0; x K; x) { item1 x y; b[y * K x] item1;} } printf(-----------------两个矩阵-#--------------\n); for (y 0; y M; y) {for (x 0; x N; x) { printf(%f , a[y * N x]); } printf(\n); } for (y 0; y N; y) { for (x 0; x K; x) { printf(%f , b[y * K x]); } printf(\n); } unsigned int grid_rows (M BLOCK_SIZE - 1) / BLOCK_SIZE; unsigned int grid_cols (K BLOCK_SIZE - 1) / BLOCK_SIZE;dim3 dimGrid(grid_rows, grid_cols); dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);gpu_matrix dimGrid, dimBlock (a, b, c_gpu, M, N, K);cudaDeviceSynchronize();cpu_matrix(a, b, c_cpu, M, N, K); // //打印cpu计算结果 printf(---------------cpu计算结果---------#------------------\n);for (y 0; y M; y) { for (x 0; x K; x) { printf(%f , c_cpu[y * K x]); } printf(\n);}printf(------------------gpu计算结果------#------------------\n); //打印GPU计算结果 for (y 0; y M; y) { for (x 0; x K; x) { printf(%f , c_gpu[y * K x]); } printf(\n); } return 0;}
-----------------两个矩阵-#--------------0.000000 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.0000001.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.0000002.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.0000003.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.0000004.000000 5.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.0000005.000000 6.000000 7.000000 8.000000 9.000000 10.000000 11.000000 12.0000000.000000 1.000000 2.000000 3.000000 4.000000 5.0000001.000000 2.000000 3.000000 4.000000 5.000000 6.0000002.000000 3.000000 4.000000 5.000000 6.000000 7.0000003.000000 4.000000 5.000000 6.000000 7.000000 8.0000004.000000 5.000000 6.000000 7.000000 8.000000 9.0000005.000000 6.000000 7.000000 8.000000 9.000000 10.0000006.000000 7.000000 8.000000 9.000000 10.000000 11.0000007.000000 8.000000 9.000000 10.000000 11.000000 12.000000---------------cpu计算结果---------#------------------140.000000 168.000000 196.000000 224.000000 252.000000 280.000000168.000000 204.000000 240.000000 276.000000 312.000000 348.000000196.000000 240.000000 284.000000 328.000000 372.000000 416.000000224.000000 276.000000 328.000000 380.000000 432.000000 484.000000252.000000 312.000000 372.000000 432.000000 492.000000 552.000000280.000000 348.000000 416.000000 484.000000 552.000000 620.000000------------------gpu计算结果------#------------------140.000000 168.000000 196.000000 224.000000 252.000000 280.000000168.000000 204.000000 240.000000 276.000000 312.000000 348.000000196.000000 240.000000 284.000000 328.000000 372.000000 416.000000224.000000 276.000000 328.000000 380.000000 432.000000 484.000000252.000000 312.000000 372.000000 432.000000 492.000000 552.000000280.000000 348.000000 416.000000 484.000000 552.000000 620.000000 到了这里我们能够使得矩阵乘法变得相当快与仅使用CPU计算相比这在实际应用中非常重要尤其是数据计算量非常大的情况。
也许到了这里这两个程序你并没有完全了解但不要担心先把这些代码运行一下体会使用GPU计算的魅力为以后的学习打下基础。
