[CUDA]CUDA編程實戰三——矩陣加法的實現

前面我們實現了向量的加法，今天我們實現復雜一些的運算，矩陣的加法，即將矩陣對應位置上的元素進行相加，相當於向量加法的升級版本。不過需要注意的是，malloc時需要分配二維矩陣，這樣才能使用A[i][j];

CPU實現

CPP實現起來的注意點在於二維數組的開辟，通過給二維數組的每一個指針賦值實現二維數據的訪問，具體算法兩層循環即可。

#include <stdlib.h>
#include <iostream>
#include <sys/time.h>
#include <math.h>

const int ROWS=1024;
const int 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一維數組初始化
    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];
        }
        
    //檢查結果
    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;
}

運行結果

運行時間為19ms，對於1024*1024的矩陣，這已經足夠快。

CUDA版本

CUDA版本與CPU版本基本類似，在核函數中則基本與向量的加法基本類似，只不過一維數據變成了二維。

#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;
    }
    //將主機指針A指向設備數據位置，目的是讓設備二級指針能夠指向設備數據一級指針
    //A 和  dataA 都傳到了設備上，但是二者還沒有建立對應關系
    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);
    //拷貝計算數據-一級數據指針
    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]);
    }

    //釋放內存
    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;
}

這里需要注意的是，dim3 threadPerBlock(16, 16)這里采用了二維的線程，那么對應的threadIdx也為二維的。
dim3則為英偉達內置的三維數據類型，即英偉達認為每個grid，或者是thread都應當是三維的，盡管有些維度還未實現。

運行結果

運行結果依然比CPU版本慢，原因還是核函數過於簡單，以至於線程調度占據了更多的時間。