gpt4 book ai didi

opencl - 在多个 GPU 上运行 OpenCL 内核?

转载 作者:行者123 更新时间:2023-12-02 00:09:09 30 4
gpt4 key购买 nike

现在我编写了几个算法,在一个 GPU 上并行运行,但是当我尝试在多个 GPU(例如 3)上执行它们时,它们都有相同的问题。问题在于,在一个 GPU 上执行的代码在 3 个 GPU 上执行的时间完全相同(不是更快)。我尝试使用更多数据执行,尝试执行不同的任务,但没有任何帮助。最后,我最终尝试运行最简单的任务,如 elements sum,但仍然犯了这个可怕的错误。这就是为什么我不认为这是特定算法的问题,而且我觉得我的代码中存在错误(甚至是我在多个 GPU 上并行化代码的方法)。

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

这里是初始化方法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);
}
}

执行内核的方法在这里:

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;
}

在将这个问题发布到 stackoverflow 之前,我已经与这个问题抗争了 2-3 周,现在我真的需要有人的帮助,所以我将非常感谢任何想法和答案!

最佳答案

这是我认为正在发生的事情。您为每个参与的 opencl 设备调用一次 clEnqueueNDRangeKernel。此时,没有一个内核开始执行,因为还没有调用 clFlush。接下来,您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。它还等待它完成。第一个工作组完成后,clFinish 会将控制权返回给您的应用程序。然后,您的应用会为下一个队列调用 clFinish。这会触发第二个工作灌浆运行,并等待它完成。所以工作是按顺序进行的。解决方案可能与在每次调用 clEnqueueNDRangeKernel 后立即调用 clFush 一样简单。这就是我的 AMD 系统的行为方式。我将很快发布一个工作示例。

关于opencl - 在多个 GPU 上运行 OpenCL 内核?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/16478968/

30 4 0
Copyright 2021 - 2024 cfsdn All Rights Reserved 蜀ICP备2022000587号
广告合作:1813099741@qq.com 6ren.com