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OpenCL之矩阵乘法实现

栏目：框架设计时间：2015-05-27 08:07:17

kernel
配置
效果

kernel

在opencl中，1般最优价值的就是kernel，前面写的配置文件基本没有很大的差别，主要是kernel写法上。其中矩阵运算又是最能体现opencl价值的地方。先上写的kernel：

__kernel void matrix_mult(
    const int Ndim,
    const int Mdim,
    const int Pdim,
    __global const float* A, 
    __global const float* B, 
    __global float* C)
{
    int i = get_global_id(0);
    int j = get_global_id(1);

    int k;
    float tmp;

    if ((i < Ndim) && (j < Mdim)) {
        tmp = 0.0;
        for (k = 0; k < Pdim; k++)
            tmp += A[i*Pdim + k] * B[k*Mdim + j];
        C[i*Mdim + j] = tmp;
    }
}

上面的配置文件看起来简单其实已包括了两方面的并行，首先是里面的乘法，这里是对所有的乘法可以进行并行。如果是M×P，P×N的矩阵，那末最多可以进行：M×N×P次乘法，如果没有超过GPU里面流媒体的处理器个数的话那末就能够同时履行，否者也只能满负荷运行。接着计算完这个以后就是加法的并行操作。用if是避免越界。

配置

在这里要特别说明的就是我们在传数据给从机的时候我们是传的1维数组，再通过传矩阵的维度来还原回2维数组。

配置文件的说明可以参考我之前的博客：请点击！
直接贴代码：

#include <CL/cl.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <iostream>
#include <fstream>

using namespace std;

#define NWITEMS 6

#pragma comment (lib,"OpenCL.lib")

//把文本文件读入1个 string 中
int convertToString(const char *filename, std::string& s)
{
    size_t size;
    char* str;
    std::fstream f(filename, (std::fstream::in | std::fstream::binary));
    if (f.is_open())
    {
        size_t fileSize;
        f.seekg(0, std::fstream::end);
        size = fileSize = (size_t)f.tellg();
        f.seekg(0, std::fstream::beg);
        str = new char[size + 1];
        if (!str)
        {
            f.close();
            return NULL;
        }
        f.read(str, fileSize);
        f.close();
        str[size] = '';
        s = str;
        delete[] str;
        return 0;
    }
    printf("Error: Failed to open file %s
", filename);
    return 1;
}

int main()
{
    cl_uint status;
    cl_platform_id platform;

    //创建平台对象
    status = clGetPlatformIDs(1, &platform, NULL);
    cl_device_id device;
    //创建 GPU 装备
    clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU,
        1,
        &device,
        NULL);
    //创建context
    cl_context context = clCreateContext(NULL,
        1,
        &device,
        NULL, NULL, NULL);
    //创建命令队列
    cl_command_queue commandQueue = clCreateCommandQueue(context,
        device,
        CL_QUEUE_PROFILING_ENABLE, NULL);

    if (commandQueue == NULL) 
            perror("Failed to create commandQueue for device 0.");

    //建立要传入从机的数据
    /********  创建内核和内存对象 ********/

    const int Ndim = 20;
    const int Mdim = 20;
    const int Pdim = 20;
    int szA = Ndim * Pdim;
    int szB = Pdim * Mdim;
    int szC = Ndim * Mdim;

    float *A;
    float *B;
    float *C;

    A = (float *)malloc(szA * sizeof(float));
    B = (float *)malloc(szB * sizeof(float));
    C = (float *)malloc(szC * sizeof(float));
    int i, j;
    for (i = 0; i < szA; i++)
        A[i] = (float)((float)i + 1.0);
    for (i = 0; i < szB; i++)
        B[i] = (float)((float)i + 1.0);

    //创建3个 OpenCL 内存对象，并把buf1 的内容通过隐式拷贝的方式
    //拷贝到clbuf1, buf2 的内容通过显示拷贝的方式拷贝到clbuf2
    cl_mem memObjects[3] = { 0, 0, 0 };
    memObjects[0] = clCreateBuffer(context, CL_MEM_READ_ONLY |  CL_MEM_COPY_HOST_PTR,
        sizeof(float)* szA, A, NULL);
    memObjects[1] = clCreateBuffer(context, CL_MEM_READ_ONLY |  CL_MEM_COPY_HOST_PTR,
        sizeof(float)* szB, B, NULL);
    memObjects[2] = clCreateBuffer(context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR,
        sizeof(float)* szC, C, NULL);
    if (memObjects[0] == NULL || memObjects[1] == NULL ||memObjects[2] == NULL) 
        perror("Error in clCreateBuffer.
");

    const char * filename = "Vadd.cl";
    std::string sourceStr;
    status = convertToString(filename, sourceStr);
    if (status)
        cout << status << "  !!!!!!!!" << endl;
    const char * source = sourceStr.c_str();
    size_t sourceSize[] = { strlen(source) };
    //创建程序对象
    cl_program program = clCreateProgramWithSource(
        context,
        1,
        &source,
        sourceSize,
        NULL);
    //编译程序对象
    status = clBuildProgram(program, 1, &device, NULL, NULL, NULL);
    if (status)
        cout << status << "  !!!!!!!!" <<endl;
    if (status != 0)
    {
        printf("clBuild failed:%d
", status);
        char tbuf[0x10000];
        clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 0x10000, tbuf,
            NULL);
        printf("
%s
", tbuf);
        //return ?1;
    }

    //创建 Kernel 对象
    cl_kernel kernel = clCreateKernel(program, "matrix_mult", NULL);

    //设置 Kernel 参数
    cl_int clnum = NWITEMS;
    status = clSetKernelArg(kernel, 0, sizeof(int), &Ndim);
    status = clSetKernelArg(kernel, 1, sizeof(int), &Mdim);
    status = clSetKernelArg(kernel, 2, sizeof(int), &Pdim);
    status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &memObjects[0]);
    status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &memObjects[1]);
    status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &memObjects[2]);
    if (status)
        cout << "参数设置毛病" << endl;

    //履行 kernel
    size_t global[2];
    cl_event prof_event;
    cl_ulong ev_start_time = (cl_ulong)0;
    cl_ulong ev_end_time = (cl_ulong)0;
    double rum_time;
    global[0] = (size_t)Ndim;
    global[1] = (size_t)Mdim;
    status = clEnqueueNDRangeKernel(commandQueue, kernel, 2, NULL,
             global, NULL, 0, NULL, &prof_event);
    if (status)
        cout << "履行内核时毛病" << endl;
    clFinish(commandQueue);

    //读取时间
    status = clGetEventProfilingInfo(prof_event,CL_PROFILING_COMMAND_QUEUED,
        sizeof(cl_ulong),&ev_start_time,NULL);
    status = clGetEventProfilingInfo(prof_event,CL_PROFILING_COMMAND_END,
        sizeof(cl_ulong),&ev_end_time,NULL);
    if (status) 
        perror("读取时间的时候产生毛病
");
    rum_time = (double)(ev_end_time - ev_start_time);
    cout << "履行时间为:" << rum_time << endl;

    //数据拷回 host 内存
    status = clEnqueueReadBuffer(commandQueue, memObjects[2],CL_TRUE, 0,
            sizeof(float)* szC, C,0, NULL, NULL);
    if (status) 
        perror("读回数据的时候产生毛病
");

    //结果显示
    printf("
Array A:
");
    for (i = 0; i < Ndim; i++) {
        for (j = 0; j < Pdim; j++)
             printf("%.3f	", A[i*Pdim + j]);
        printf("
");
    }
    printf("
Array B:
");
    for (i = 0; i < Pdim; i++) {
        for (j = 0; j < Mdim; j++)
             printf("%.3f	", B[i*Mdim + j]);
        printf("
");
    }
    printf("
Array C:
");
    for (i = 0; i < Ndim; i++) {
        for (j = 0; j < Mdim; j++)
             printf("%.3f	", C[i*Mdim + j]);
        printf("
");
    }

    cout << endl;

    if (A)
        free(A);
    if (B)
        free(B);
    if (C)
        free(C);

    //删除 OpenCL 资源对象
    clReleaseMemObject(memObjects[2]);
    clReleaseMemObject(memObjects[1]);
    clReleaseMemObject(memObjects[0]);
    clReleaseProgram(program);
    clReleaseCommandQueue(commandQueue);
    clReleaseContext(context);
    system("pause");

    return 0;
}

效果

我演示1个4×5与5×6的矩阵的乘法：

这里写图片描述

请点击：参考文档

另外可以避免积分下载AMD OpenCL教程：点击进入下载

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