CUDA笔记3：代码实践



    本文用CUDA实现两矩阵相乘。下图为C=A*B的直观解释，C的每个元素为A对应行向量与B对应列向量的点积。可见，为了计算元素C(i,j)，需要访问全局内存A.width + B.height次。为了提升计算效率，矩阵乘法还可以用共享内存实现。首先按照Block大小将A,B,C拆分成子矩阵形式，那么C中一个Block内的所有Thread可以同步访问A,B对应的子区域（从全局内存拷贝到共享内存，共享内存设置成Block区域大小），然后进行子区域的乘积计算。实际上利用共享内存可以将全局访问次数降低为A.width/BLOCK_SIZE + B.height/BLOCK_SIZE.

                                      矩阵相乘原理图

                                            利用共享内存可以提高计算效率

以下Code参照SDK VectorAdd代码示例改下，用来计算两个1024*1024方阵的乘机，并且添加了CPU和GPU计时功能，可以方便对比运算时间。

/** * 两矩阵（方阵）相乘示例， 使用共享内存 比 不使用共享内存 效率高出一倍 */#include <stdio.h>#include <time.h>// For the CUDA runtime routines (prefixed with "cuda_")#include <cuda_runtime.h>/** * CUDA Kernel Device code *  * Computes the matrix multiplication of A and B into C.  * without using shared memory */__global__ void matrixMul_noSharedM(const float *A, const float *B, float *C, int N){int col = blockDim.x * blockIdx.x + threadIdx.x;int row = blockDim.y * blockIdx.y + threadIdx.y;if (col < N && row < N){C[row*N + col] = 0;for (int i=0; i<N; i++){C[row*N + col] += A[row*N + i] * B[i*N + col];}}}//using shared memory__global__ void matrixMul_SharedM(const float *A, const float *B, float *C, int N){__shared__ float Mds[32][32];__shared__ float Nds[32][32];int col = blockDim.x * blockIdx.x + threadIdx.x;int row = blockDim.y * blockIdx.y + threadIdx.y;if (col < N && row < N){float pValue = 0.0;for (int m=0; m<32; m++){Mds[threadIdx.y][threadIdx.x] = A[row*N + m*32 + threadIdx.x];Nds[threadIdx.y][threadIdx.x] = B[(threadIdx.y+m*32)*N + col];__syncthreads(); //同步同一个block的所有threadfor(int k = 0; k < 32; k++){pValue += Mds[threadIdx.y][k] * Nds[k][threadIdx.x];}__syncthreads();}C[row*N + col] = pValue;}}/** * Host main routine */int main(void){    // Error code to check return values for CUDA calls    cudaError_t err = cudaSuccess;    // Define the squared matrix, and compute its size    int numElements = 32*32;    int size = numElements * numElements * sizeof(float);int NN = numElements * numElements;        // Allocate the host input vector A    float *h_A = new float[size];//(float *)malloc(size);    // Allocate the host input vector B    float *h_B = new float[size];//(float *)malloc(size);    // Allocate the host output vector C    float *h_C = new float[size];//(float *)malloc(size);    // Verify that allocations succeeded    if (h_A == NULL || h_B == NULL || h_C == NULL)    {        fprintf(stderr, "Failed to allocate host matrix!\n");        exit(EXIT_FAILURE);    }    // Initialize the host input matrix    for (int i = 0; i < NN; i++)    {        h_A[i] = rand()/(float)RAND_MAX * 10.0;        h_B[i] = rand()/(float)RAND_MAX * 10.0;     }    // Allocate the device input matrix A    float *d_A = NULL;    err = cudaMalloc((void **)&d_A, size);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to allocate device matrix A (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Allocate the device input matrix B    float *d_B = NULL;    err = cudaMalloc((void **)&d_B, size);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to allocate device matrix B (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Allocate the device output matrix C    float *d_C = NULL;    err = cudaMalloc((void **)&d_C, size);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to allocate device matrix C (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Copy the host input matrix A and B in host memory to the device input matrix in    // device memory   // printf("Copy input data from the host memory to the CUDA device\n");    err = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to copy matrix A from host to device (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    err = cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to copy matrix B from host to device (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Launch the Matrix multiplication CUDA Kernel    dim3 dimGrid;dim3 dimBlock(32, 32);dimGrid.x  = numElements / dimBlock.x ;dimGrid.y  = numElements / dimBlock.y ;cudaEvent_t start, stop; // measure GPU timefloat time;cudaEventCreate(&start);cudaEventCreate(&stop);cudaEventRecord(start, 0);    //matrixMul_noSharedM<<<dimGrid, dimBlock>>>(d_A, d_B, d_C, numElements);matrixMul_SharedM<<<dimGrid, dimBlock>>>(d_A, d_B, d_C, numElements);cudaEventRecord(stop, 0);cudaEventSynchronize(stop);cudaEventElapsedTime(&time, start, stop); printf("GPU time: %f \n", time);cudaEventDestroy(start);cudaEventDestroy(stop);    err = cudaGetLastError();    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Copy the device result matrix in device memory to the host result matrix    // in host memory.   // printf("Copy output data from the CUDA device to the host memory\n");    err = cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to copy matrix C from device to host (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }// Compute the groundtruethclock_t start2, finish;   // measure CPU time    double  duration = 0.0;   start2 = clock();   float* GT_C = new float[NN];for (int r=0; r<numElements; r++){for (int c=0; c<numElements; c++){GT_C[r*numElements + c] = 0;for (int i=0; i<numElements; i++){GT_C[r*numElements + c] += h_A[r*numElements + i] * h_B[i*numElements + c];}}}finish = clock();   duration = (double)(finish - start2); //输出单位ms  printf("CPU time: %f\n", duration);// Verify that the result vector is correctfor (int i = 0; i < NN; ++i){//printf("%d  %f  %f\n", i,  GT_C[i], h_C[i]);if (fabs(GT_C[i] - h_C[i]) > 1e-5){fprintf(stderr, "Result verification failed at element %d!\n", i);exit(EXIT_FAILURE);}}printf("Test PASSED\n");    // Free device global memory    err = cudaFree(d_A);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to free device matrix A (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    err = cudaFree(d_B);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to free device matrix B (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    err = cudaFree(d_C);    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to free device matrix C (error code %s)!\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    // Free host memory    delete []h_A;    delete []h_B;    delete []h_C;    // Reset the device and exit    err = cudaDeviceReset();    if (err != cudaSuccess)    {        fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));        exit(EXIT_FAILURE);    }    printf("Done\n");    return 0;}

参考：http://blog.csdn.net/csgxy123/article/details/10018531