說明

A矩陣為M * N，B矩陣為N * M

程式碼

#include "device_functions.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>

typedef struct {
    int width;
    int height;
    int stride;
    float* elements;
} Matrix;

#define BLOCK_SIZE  2
#define N           4
 

#define M           8

__device__ float GetElement(const Matrix A, int row, int col) {
    return A.elements[row * A.stride + col];
}

__device__ void SetElement(Matrix A, int row, int col, float value) {
    A.elements[row * A.stride + col] = value;
}

__device__ Matrix GetSubMatrix(Matrix A, int row, int
 
 col) {
    Matrix Asub;
    Asub.width = BLOCK_SIZE;
    Asub.height = BLOCK_SIZE;
    Asub.stride = A.stride;
    Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row+ BLOCK_SIZE * col];
    return Asub;
}

__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C) {
    int blockRow = blockIdx.y;
    int
 
 blockCol = blockIdx.x;
    Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
    float Cvalue = 0;
    int row = threadIdx.y;
    int col = threadIdx.x;
    for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) {
        Matrix Asub = GetSubMatrix(A, blockRow, m);
        Matrix Bsub = GetSubMatrix(B, m, blockCol);
        __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
        __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
        As[row][col] = GetElement(Asub, row, col);
        Bs[row][col] = GetElement(Bsub, row, col);
        __syncthreads();
        for (int e = 0; e < BLOCK_SIZE; ++e)
            Cvalue += As[row][e] * Bs[e][col];
        __syncthreads();
    }
    SetElement(Csub, row, col, Cvalue);
}

void MatMul(const Matrix A, const Matrix B, Matrix C) {
    Matrix d_A;
    d_A.width = d_A.stride = A.width; 
    d_A.height = A.height;
    size_t size = A.width * A.height * sizeof(float);
    cudaMalloc((void**)&d_A.elements, size);
    cudaMemcpy(d_A.elements, A.elements, size,
    cudaMemcpyHostToDevice);

    Matrix d_B;
    d_B.width = d_B.stride = B.width; 
    d_B.height = B.height;
    size = B.width * B.height * sizeof(float);
    cudaMalloc((void**)&d_B.elements, size);
    cudaMemcpy(d_B.elements, B.elements, size,
    cudaMemcpyHostToDevice);

    Matrix d_C;
    d_C.width = d_C.stride = C.width; 
    d_C.height = C.height;
    size = C.width * C.height * sizeof(float);
    cudaMalloc((void**)&d_C.elements, size);

    dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
    dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
    MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
    cudaMemcpy(C.elements, d_C.elements, size,
    cudaMemcpyDeviceToHost);

    cudaFree(d_A.elements);
    cudaFree(d_B.elements);
    cudaFree(d_C.elements);
}

void init(Matrix *A,int row, int col) {
    A->width=col;
    A->height=row;
    A->stride=col;
    A->elements=(float*) calloc(row*col, sizeof(float));
    for(int i=0; i<row*col; i++) A->elements[i]=i;

}



int main() {
    //定義四個矩陣，C=A*B為分割成塊使用sharedmemory;C_ref=A*B直接計算；
    Matrix A, B, C;
    //初始化四個矩陣，賦初值。
    init(&A,M,N);
    init(&B,N,M);
    init(&C,M,M);

    int i, j;
    printf("A\n");
    for(i=0; i<M*N; i++){
        printf("%f \t",A.elements[i]);
    }
    printf("\n");
    printf("A\n");
    for(i=0; i<M; i++){
        for(j=0; j<N; j++) {
            printf("%f \t",A.elements[i*N+j]);
        }
        printf("\n");
    }
    printf("B\n");
    for(i=0; i<N; i++){
        for(j=0; j<M; j++) {
            printf("%f \t",A.elements[i*M+j]);
        }
        printf("\n");
    }

    MatMul(A, B, C);
    printf("C\n");
    for(i=0; i<M; i++){
        for(j=0; j<M; j++) {
            printf("%f \t",C.elements[j*M+i]);
        }
        printf("\n");
    }

}