kernel

在opencl中，一般最優價值的就是kernel，前面寫的配置檔案基本沒有很大的差別，主要是kernel寫法上。其中矩陣運算又是最能體現opencl價值的地方。先上寫的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;
    }
}

上面的配置檔案看起來簡單其實已經包含了兩方面的並行，首先是裡面的乘法，這裡是對所有的乘法可以進行並行。如果是M×P，P×N的矩陣，那麼最多可以進行：M×N×P次乘法，如果沒有超過GPU裡面流媒體的處理器個數的話那麼就可以同時執行，否者也只能滿負荷執行。接著計算完這個之後就是加法的並行操作。用if是防止越界。

配置

在這裡要特別說明的就是我們在傳資料給從機的時候我們是傳的一維陣列，再通過傳矩陣的維度來還原回二維陣列。

配置檔案的說明可以參考我之前的部落格：請點選！
直接貼程式碼：

#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")
 


//把文字檔案讀入一個 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] = '\0';
        s = str;
        delete[] str;
        return 0;
    }
    printf("Error: Failed to open file %s\n", filename);
    return 1;
}

int main()
{
    cl_uint status;
    cl_platform_id platform;

    //建立平臺物件
    status = clGetPlatformIDs(1, &platform, NULL);
    cl_device_id device;
    //建立 GPU 裝置
    clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU,
        1,
        &device,
        NULL);
    //建立context
    cl_context context = clCreateContext(NULL,
        1,
        &device,
        NULL, NULL, NULL);
    //建立命令佇列
    cl_command_queue commandQueue = clCreateCommandQueue(context,
        device,
        CL_QUEUE_PROFILING_ENABLE, NULL);

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

    //建立要傳入從機的資料
    /********  建立核心和記憶體物件 ********/

    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);

    //建立三個 OpenCL 記憶體物件，並把buf1 的內容通過隱式拷貝的方式
    //拷貝到clbuf1, buf2 的內容通過顯示拷貝的方式拷貝到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.\n");

    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) };
    //建立程式物件
    cl_program program = clCreateProgramWithSource(
        context,
        1,
        &source,
        sourceSize,
        NULL);
    //編譯程式物件
    status = clBuildProgram(program, 1, &device, NULL, NULL, NULL);
    if (status)
        cout << status << "  !!!!!!!!" <<endl;
    if (status != 0)
    {
        printf("clBuild failed:%d\n", status);
        char tbuf[0x10000];
        clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 0x10000, tbuf,
            NULL);
        printf("\n%s\n", tbuf);
        //return −1;
    }

    //建立 Kernel 物件
    cl_kernel kernel = clCreateKernel(program, "matrix_mult", NULL);

    //設定 Kernel 引數
    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 << "引數設定錯誤" << endl;

    //執行 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 << "執行核心時錯誤" << endl;
    clFinish(commandQueue);

    //讀取時間
    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("讀取時間的時候發生錯誤\n");
    rum_time = (double)(ev_end_time - ev_start_time);
    cout << "執行時間為:" << rum_time << endl;

    //資料拷回 host 記憶體
    status = clEnqueueReadBuffer(commandQueue, memObjects[2],CL_TRUE, 0,
            sizeof(float)* szC, C,0, NULL, NULL);
    if (status) 
        perror("讀回資料的時候發生錯誤\n");

    //結果顯示
    printf("\nArray A:\n");
    for (i = 0; i < Ndim; i++) {
        for (j = 0; j < Pdim; j++)
             printf("%.3f\t", A[i*Pdim + j]);
        printf("\n");
    }
    printf("\nArray B:\n");
    for (i = 0; i < Pdim; i++) {
        for (j = 0; j < Mdim; j++)
             printf("%.3f\t", B[i*Mdim + j]);
        printf("\n");
    }
    printf("\nArray C:\n");
    for (i = 0; i < Ndim; i++) {
        for (j = 0; j < Mdim; j++)
             printf("%.3f\t", C[i*Mdim + j]);
        printf("\n");
    }

    cout << endl;

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

    //刪除 OpenCL 資源物件
    clReleaseMemObject(memObjects[2]);
    clReleaseMemObject(memObjects[1]);
    clReleaseMemObject(memObjects[0]);
    clReleaseProgram(program);
    clReleaseCommandQueue(commandQueue);
    clReleaseContext(context);
    system("pause");

    return 0;
}

效果

我演示一個4×5與5×6的矩陣的乘法：

請點選：參考文件

另外可以免積分下載AMD OpenCL教程：點選進入下載