为了充分利用SM，Block数尽可能多，>100。
#include "cuda_runtime.h" #include <stdlib.h> #include <iostream> #include <sys/time.h> using namespace std; __global__ void Plus(float A[], float B[], float C[], int n) { int i = blockDim.x * blockIdx.x + threadIdx.x; C[i] = A[i] + B[i]; } int main() { struct timeval start, end; gettimeofday( &start, NULL ); float*A, *Ad, *B, *Bd, *C, *Cd; int n = 1024 * 1024; int size = n * sizeof(float); // CPU端分配内存 A = (float*)malloc(size); B = (float*)malloc(size); C = (float*)malloc(size); // 初始化数组 for(int i=0;i<n;i++) { A[i] = 90.0; B[i] = 10.0; } // GPU端分配内存 cudaMalloc((void**)&Ad, size); cudaMalloc((void**)&Bd, size); cudaMalloc((void**)&Cd, size); // CPU的数据拷贝到GPU端 cudaMemcpy(Ad, A, size, cudaMemcpyHostToDevice); cudaMemcpy(Bd, B, size, cudaMemcpyHostToDevice); cudaMemcpy(Bd, B, size, cudaMemcpyHostToDevice); // 定义kernel执行配置，（1024*1024/512）个block，每个block里面有512个线程 dim3 dimBlock(512); dim3 dimGrid(n/512); // 执行kernel Plus<<<dimGrid, dimBlock>>>(Ad, Bd, Cd, n); // 将在GPU端计算好的结果拷贝回CPU端 cudaMemcpy(C, Cd, size, cudaMemcpyDeviceToHost); // 校验误差 float max_error = 0.0; for(int i=0;i<n;i++) { max_error += fabs(100.0 - C[i]); } cout << "max error is " << max_error << endl; // 释放CPU端、GPU端的内存 free(A); free(B); free(C); cudaFree(Ad); cudaFree(Bd); cudaFree(Cd); gettimeofday( &end, NULL ); int timeuse = 1000000 * ( end.tv_sec - start.tv_sec ) + end.tv_usec - start.tv_usec; cout << "total time is " << timeuse/1000 << "ms" <<endl; return 0; }GPU方式输出结果
max error is 0 total time is 1278ms由上面的例子看出，使用CUDA编程时我们看不到for循环了，因为CPU编程的循环已经被分散到各个thread上做了，所以我们也就看到不到for一类的语句。从结果上看，CPU的循环计算的速度比GPU计算快多了，原因就在于CUDA中有大量的内存拷贝操作（数据传输花费了大量时间，而计算时间却非常少），如果计算量比较小的话，CPU计算会更合适一些。
下面计算一个稍微复杂的例子，矩阵加法，即对两个矩阵对应坐标的元素相加后的结果存储在第三个的对应位置的元素上。
值得注意的是，这个计算任务我采用了二维数组的计算方式，注意一下二维数组在CUDA编程中的写法。
CPU版本
#include <stdlib.h> #include <iostream> #include <sys/time.h> #include <math.h> #define ROWS 1024 #define COLS 1024 using namespace std; int main() { struct timeval start, end; gettimeofday( &start, NULL ); int *A, **A_ptr, *B, **B_ptr, *C, **C_ptr; int total_size = ROWS*COLS*sizeof(int); A = (int*)malloc(total_size); B = (int*)malloc(total_size); C = (int*)malloc(total_size); A_ptr = (int**)malloc(ROWS*sizeof(int*)); B_ptr = (int**)malloc(ROWS*sizeof(int*)); C_ptr = (int**)malloc(ROWS*sizeof(int*)); //CPU一维数组初始化 for(int i=0;i<ROWS*COLS;i++) { A[i] = 80; B[i] = 20; } for(int i=0;i<ROWS;i++) { A_ptr[i] = A + COLS*i; B_ptr[i] = B + COLS*i; C_ptr[i] = C + COLS*i; } for(int i=0;i<ROWS;i++) for(int j=0;j<COLS;j++) { C_ptr[i][j] = A_ptr[i][j] + B_ptr[i][j]; } //检查结果 int max_error = 0; for(int i=0;i<ROWS*COLS;i++) { //cout << C[i] << endl; max_error += abs(100-C[i]); } cout << "max_error is " << max_error <<endl; gettimeofday( &end, NULL ); int timeuse = 1000000 * ( end.tv_sec - start.tv_sec ) + end.tv_usec - start.tv_usec; cout << "total time is " << timeuse/1000 << "ms" <<endl; return 0; }CPU方式输出
max_error is 0 total time is 29msGPU版本
#include "cuda_runtime.h" #include "device_launch_parameters.h" #include <sys/time.h> #include <stdio.h> #include <math.h> #define Row 1024 #define Col 1024 __global__ void addKernel(int **C, int **A, int ** B) { int idx = threadIdx.x + blockDim.x * blockIdx.x; int idy = threadIdx.y + blockDim.y * blockIdx.y; if (idx < Col && idy < Row) { C[idy][idx] = A[idy][idx] + B[idy][idx]; } } int main() { struct timeval start, end; gettimeofday( &start, NULL ); int **A = (int **)malloc(sizeof(int*) * Row); int **B = (int **)malloc(sizeof(int*) * Row); int **C = (int **)malloc(sizeof(int*) * Row); int *dataA = (int *)malloc(sizeof(int) * Row * Col); int *dataB = (int *)malloc(sizeof(int) * Row * Col); int *dataC = (int *)malloc(sizeof(int) * Row * Col); int **d_A; int **d_B; int **d_C; int *d_dataA; int *d_dataB; int *d_dataC; //malloc device memory cudaMalloc((void**)&d_A, sizeof(int **) * Row); cudaMalloc((void**)&d_B, sizeof(int **) * Row); cudaMalloc((void**)&d_C, sizeof(int **) * Row); cudaMalloc((void**)&d_dataA, sizeof(int) *Row*Col); cudaMalloc((void**)&d_dataB, sizeof(int) *Row*Col); cudaMalloc((void**)&d_dataC, sizeof(int) *Row*Col); //set value for (int i = 0; i < Row*Col; i++) { dataA[i] = 90; dataB[i] = 10; } //将主机指针A指向设备数据位置，目的是让设备二级指针能够指向设备数据一级指针 //A 和 dataA 都传到了设备上，但是二者还没有建立对应关系 for (int i = 0; i < Row; i++) { A[i] = d_dataA + Col * i; B[i] = d_dataB + Col * i; C[i] = d_dataC + Col * i; } cudaMemcpy(d_A, A, sizeof(int*) * Row, cudaMemcpyHostToDevice); cudaMemcpy(d_B, B, sizeof(int*) * Row, cudaMemcpyHostToDevice); cudaMemcpy(d_C, C, sizeof(int*) * Row, cudaMemcpyHostToDevice); cudaMemcpy(d_dataA, dataA, sizeof(int) * Row * Col, cudaMemcpyHostToDevice); cudaMemcpy(d_dataB, dataB, sizeof(int) * Row * Col, cudaMemcpyHostToDevice); dim3 threadPerBlock(16, 16); dim3 blockNumber( (Col + threadPerBlock.x - 1)/ threadPerBlock.x, (Row + threadPerBlock.y - 1) / threadPerBlock.y ); printf("Block(%d,%d) Grid(%d,%d).\n", threadPerBlock.x, threadPerBlock.y, blockNumber.x, blockNumber.y); addKernel << <blockNumber, threadPerBlock >> > (d_C, d_A, d_B); //拷贝计算数据-一级数据指针 cudaMemcpy(dataC, d_dataC, sizeof(int) * Row * Col, cudaMemcpyDeviceToHost); int max_error = 0; for(int i=0;i<Row*Col;i++) { //printf("%d\n", dataC[i]); max_error += abs(100-dataC[i]); } //释放内存 free(A); free(B); free(C); free(dataA); free(dataB); free(dataC); cudaFree(d_A); cudaFree(d_B); cudaFree(d_C); cudaFree(d_dataA); cudaFree(d_dataB); cudaFree(d_dataC); printf("max_error is %d\n", max_error); gettimeofday( &end, NULL ); int timeuse = 1000000 * ( end.tv_sec - start.tv_sec ) + end.tv_usec - start.tv_usec; printf("total time is %d ms\n", timeuse/1000); return 0; }GPU输出
Block(16,16) Grid(64,64). max_error is 0 total time is 442 ms