cuda编程实例2-矩阵相加

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#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include<stdio.h>
#include<stdlib.h>
#include<malloc.h>
#include<time.h>
#include<algorithm>
//随机初始化数组
void init(float* ip, float size) {
for (int i = 0; i < size; i++) {
ip[i] = float(rand() & 0xff) / 66.6;
}
}
//打印数组
void printMatrix(float* a, float* b, float* c, const int nx, const int ny) {
float* ia = a;
float* ib = b;
float* ic = c;
printf("nMatric:(%d,%d)n", nx, ny);
for (int iy = 0; iy < ny; iy++) {
for (int ix = 0; ix < nx; ix++) {
printf("%f+%f=%f", ia[ix], ib[ix], ic[ix]);
}
ia += nx;
ib += nx;
ic += nx;
printf("n");
}
printf("n");
}
//打印矩阵之差
void printResult(float* c, float* cc, const int nx, const int ny) {
float* ic = c;
float* icc = cc;
for (int iy = 0; iy < ny; iy++) {
for (int ix = 0; ix < nx; ix++) {
printf("%f", ic[ix] - icc[ix]);
}
ic += nx;
icc += nx;
printf("n");
}
printf("n");
}
//验证结果
void checkResult(float* hostRef, float* gpuRef, const int N) {
double epsilon = 1.0E-8;
int match = 1;
for (int i = 0; i < N; i++) {
if (abs(hostRef[i] - gpuRef[i]) > epsilon) {
match = 0;
printf("Array don't match");
printf("host %5.2f gpu %5.2f at current %dn", hostRef[i], gpuRef[i], i);
break;
}
}
if (match) {
printf("Array match.nn");
return;
}
}
//CPU上两个矩阵相加
void sumMatrixOnHost(float* a, float* b, float* c, const int nx, const int ny) {
float* ia = a;
float* ib = b;
float* ic = c;
for (int iy = 0; iy < ny; iy++) {
for (int ix = 0; ix < nx; ix++) {
ic[ix] = ia[ix] + ib[ix];
}
ia += nx;
ib += nx;
ic += nx;
}
}
__global__ void sumMatrixOnGPU2D(float* a, float* b, float* c, const int nx, const int ny) {
int ix = threadIdx.x + blockDim.x * blockIdx.x;
int iy = threadIdx.y + blockDim.y * blockIdx.y;
unsigned int idx = iy * nx + ix;
if (ix < nx && iy < ny) {
c[idx] = a[idx] + b[idx];
}
}
int main() {
int dev = 0;
cudaDeviceProp deviceprop;
cudaGetDeviceProperties(&deviceprop, dev);
printf("using Device :%d %snn", dev, deviceprop.name);
//设置矩阵维度
int nx = 1 << 12;
int ny = 1 << 12;
int nxy = nx * ny;
int nBytes = nxy * sizeof(float);
//分配CPU的相关数据内存
float* h_a, *h_b, *h_c, *h_cc;
h_a = (float*)malloc(nBytes);
h_b = (float*)malloc(nBytes);
h_c = (float*)malloc(nBytes);
h_cc = (float*)malloc(nBytes);
//初始化数据
init(h_a, nxy);
init(h_b, nxy);
//开始计时
clock_t cpuStart = clock();
sumMatrixOnHost(h_a, h_b, h_c, nx, ny);
clock_t cpuEnd = clock();
float cpuTime = (float)(cpuEnd - cpuStart) / CLOCKS_PER_SEC;
printf("cpu time %fn", cpuTime);
//分配gpu内存
float* d_a, *d_b, *d_c;
cudaMalloc((void**)&d_a, nBytes);
cudaMalloc((void**)&d_b, nBytes);
cudaMalloc((void**)&d_c, nBytes);
//初始化网格以及块大小
dim3 block(128,1);
dim3 grid((nx + block.x - 1) / block.x, (ny + block.y - 1) / block.y);
//数据从cpu拷贝gpu
cudaMemcpy(d_a, h_a, nBytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, h_b, nBytes, cudaMemcpyHostToDevice);
//gpu调用核函数
clock_t gpuStart = clock();
sumMatrixOnGPU2D << <grid, block >> > (d_a, d_b, d_c, nx, ny);
cudaDeviceSynchronize();
clock_t gpuEnd = clock();
float gpuTime = (float)(gpuEnd - gpuStart) / CLOCKS_PER_SEC;
printf("GPU time:%fn", gpuTime);
// 结果从gpu再拷贝回cpu
cudaMemcpy(h_cc, d_c, nBytes, cudaMemcpyDeviceToHost);
checkResult(h_c, h_cc, nxy);
//释放内存
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
free(h_a);
free(h_b);
free(h_c);
free(h_cc);
return 0;
}