Question

现在我编程了几个算法在一个GPU上并行运行，但是当我尝试在几个GPU上执行它们时（例如，3），它们都有同样的问题。问题是在一个GPU上执行的代码在3个GPU上执行完全相同的时间（不是更快）。我尝试用更多数据执行，尝试执行不同的任务，没有任何帮助。最后，我最终尝试运行最简单的任务，如元素总和，仍然有这个可怕的错误。这就是为什么我不相信这是一个特定算法的问题，我觉得我的代码中存在错误（甚至在我的方法中并行处理几个GPU上的代码）。

这是我的Parallel.cpp类的头文件：

#ifndef PARALLEL_H
#define PARALLEL_H

#define __NO_STD_VECTOR // Use cl::vector and cl::string and
#define __NO_STD_STRING // not STL versions, more on this later
#include <CL/cl.h>

class Parallel
{
    public:
        Parallel();
        int executeAttachVectorsKernel(int*, int*, int*, int);
        static void getMaxWorkGroupSize(int*, int*, int*);
        virtual ~Parallel();
    protected:
    private:
        char* file_contents(const char*, int*);
        void getShortInfo(cl_device_id);
        int init(void);
        cl_platform_id platform;
        cl_device_id* devices;
        cl_uint num_devices;
        cl_command_queue* queues;
        int* WGSizes;
        int* WGNumbers;
        cl_context context;
        cl_program program;
        cl_kernel kernel;
        cl_mem input1;
        cl_mem input2;
        cl_mem output;
};

#endif // PARALLEL_H

这是初始化方法init：

int Parallel::init() {
cl_int err;

//Connect to the first platfrom
err = clGetPlatformIDs(1, &platform, NULL);
if (err != CL_SUCCESS) {
    cerr << "Error occured while executing clGetPlatformIDs" << endl;
    return EXIT_FAILURE;
}

//Get devices number
err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 0, NULL, &num_devices);
if (err != CL_SUCCESS) {
    cerr << "Error: Failed to create a device group:" << endl;
    return EXIT_FAILURE;
}

cout << "NUM DEVICES =" << num_devices << endl;

devices = new cl_device_id[num_devices];
//Get all the GPU devices
err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, num_devices, devices, NULL);

//Create one context for all the devices
context = clCreateContext(NULL, num_devices, devices, NULL, NULL, &err);
if (!context) {
    cerr << "Error: Failed to create a compute context!" << endl;
    return EXIT_FAILURE;
}

queues = new cl_command_queue[num_devices];
WGNumbers = new int[num_devices];
WGSizes = new int[num_devices];


for(int i = 0; i < num_devices; i++) {
    //Create a command queue for every device
    queues[i] = clCreateCommandQueue(context, devices[i], 0, &err);
    if (!queues[i]) {
        cerr << "Error: Failed to create a command commands!" << endl;
        return EXIT_FAILURE;
    }

    cl_ulong temp;
    clGetDeviceInfo(devices[i], CL_DEVICE_MAX_WORK_GROUP_SIZE, sizeof(temp), &temp, NULL);
    WGSizes[i] = (int)temp;

    clGetDeviceInfo(devices[i], CL_DEVICE_MAX_WORK_ITEM_SIZES, sizeof(temp), &temp, NULL);
    WGNumbers[i] = (int)temp;
}

//Translate kernel code into chars
int pl;
size_t program_length;
string path = "./kernel/kernel_av.cl";

char* cSourceCL = file_contents(path.c_str(), &pl);
program_length = (size_t)pl;

//Create a program
program = clCreateProgramWithSource(context, 1,
                  (const char **) &cSourceCL, &program_length, &err);

if (!program) {
    cerr << "Error: Failed to create compute program!" << endl;
    return EXIT_FAILURE;
}

//Create an executable
err = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
if (err != CL_SUCCESS)
{
    size_t len;
    char buffer[2048];

    cerr << "Error: Failed to build program executable!" << endl;
    exit(1);
}

// Create the compute kernel in the program
kernel = clCreateKernel(program, "calculate2dim", &err);
if (err != CL_SUCCESS)
{
    cerr << "Error: Failed to create compute kernel!" << endl;
    exit(1);
}
}

执行内核的方法是：

int Parallel::executeAttachVectorsKernel(int* data1, int* data2, int* results, int vectors_num) {

cl_int err;
size_t global;  // global domain size for our calculation
size_t local;   // local domain size for our calculation

int partition = vectors_num/num_devices;
unsigned int count = partition;
input1 = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(int) * count, NULL, NULL);
input2 = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(int) * count, NULL, NULL);
output = clCreateBuffer(context, CL_MEM_WRITE_ONLY, sizeof(int) * count, NULL, NULL);
if (!input1 || !input2 || !output) {
    cerr << "Error: Failed to allocate device memory!" << endl;
    exit(1);
}

int** data1_apart = new int*[num_devices];
int** data2_apart = new int*[num_devices];
int** results_apart = new int*[num_devices];

for(int i = 0; i < num_devices; i++) {
    cout << "Executing parallel part on GPU " << i + 1 << endl;
    cout << "Partition size = " << partition << endl;
    data1_apart[i] = new int[partition];
    data2_apart[i] = new int[partition];
    results_apart[i] = new int[partition];

    for(int j = i*partition, k = 0; k < partition; j++, k++) {
        data1_apart[i][k] = data1[j];
        data2_apart[i][k] = data2[j];
    }

    //Transfer the input vector into device memory
    err = clEnqueueWriteBuffer(queues[i], input1,
                               CL_TRUE, 0, sizeof(int) * count,
                               data1_apart[i], 0, NULL, NULL);

    err = clEnqueueWriteBuffer(queues[i], input2,
                               CL_TRUE, 0, sizeof(int) * count,
                               data2_apart[i], 0, NULL, NULL);

    if (err != CL_SUCCESS)
    {
        cerr << "Error: Failed to write to source array!" << endl;
        exit(1);
    }

    int parameter4 = count/WGNumbers[i];

     //Set the arguments to the compute kernel
    err = 0;
    err  = clSetKernelArg(kernel, 0, sizeof(cl_mem), &input1);
    err |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &input2);
    err |= clSetKernelArg(kernel, 2, sizeof(cl_mem), &output);
    err |= clSetKernelArg(kernel, 3, sizeof(int), &parameter4);
    if (err != CL_SUCCESS)
    {
        cerr << "Error: Failed to set kernel arguments! " << err << endl;
        exit(1);
    }

    global = WGNumbers[i];
    local = WGSizes[i];

    if(local > global) {
        local = global;
    }
    cout << "global = " << global << " local = " << local << endl;

    err = clEnqueueNDRangeKernel(queues[i], kernel,
                                 1, NULL, &global, &local,
                                 0, NULL, NULL);
    if (err)
    {
        cerr << "Error: Failed to execute kernel!" << endl;
        return EXIT_FAILURE;
    }
}

for(int i = 0; i < num_devices; i++) {
    //Wait for all commands to complete
    clFinish(queues[i]);

    //Read back the results from the device to verify the output

    err = clEnqueueReadBuffer(queues[i], output,
                               CL_TRUE, 0, sizeof(int) * count,
                               results_apart[i], 0, NULL, NULL );
    if (err != CL_SUCCESS)
    {
        cerr << "Error: Failed to read output array! " <<  err << endl;
        exit(1);
    }

    for(int j = 0; j < partition; j++) {
        results[i*partition + j] = results_apart[i][j];
    }

    delete [] data1_apart[i];
    delete [] data2_apart[i];
    delete [] results_apart[i];
}

clReleaseMemObject(input1);
clReleaseMemObject(input2);
clReleaseMemObject(output);
delete [] data1_apart;
delete [] data2_apart;
}

在将此问题发布到stackoverflow之前，我正在解决这个问题2-3周，现在我真的需要别人的帮助，所以我将非常感谢任何想法和答案！

Answer 1

这是我认为正在发生的事情。您为每个参与的opencl设备调用clEnqueueNDRangeKernel一次。此时，没有内核已经开始执行，因为尚未调用clFlush。接下来，为每个队列创建一个clFinish。第一个clFinish调用导致第一个排队的工作组运行。它也等待它完成。第一个工作组完成后，clFinish会将控制权返回给您的应用。然后你的应用程序调用clFinish作为下一个队列。这会触发第二个工作灌浆运行，并等待它完成。所以工作顺序进行。解决方案可能就像每次调用clEnqueueNDRangeKernel后立即调用clFush一样简单。这就是我的AMD系统的行为方式。我将很快发布一个有效的例子。

Answer 2

所有设备都在相同的缓冲区上运行。执行内核时，数据将在设备之间移动。如果没有正确的同步，结果将是未定义的。

如果可能，请考虑为每个设备分配一组不同的缓冲区。

Answer 3

您使用的是哪些GPU？我有一台GTX590，它出现在两个GPU设备上。当我试图在两个设备上运行时似乎等待每个设备完成它之前它移动到下一个设备（即使它不是假设）。我不知道Nvidia是否解决了这个问题。

当我读到有关Nvidia的内容时，我想在Nvidia网站上阅读一些消息，建议为每个设备创建单独的上下文并在不同的线程中运行它们。这就是我所做的，而且效果很好。我为此使用了pthreads（或SDL_threads）。这很容易设置。

在多个GPU上运行OpenCL内核？

3 个答案: