OpenCL快速入门教程

参考链接

一、OpenCL中的一些函数

OpenCL的Kernel相当于CUDA的device
OpenCL的Work-item相当于CUDA的thread
OpenCL的Work-group相当于CUDA的block
OpenCL的ND-Range相当于CUDA的grid

get_global_id(dim) ：CUDA中需要计算线程的id，而在opencl中线程id直接通过这个函数直接获取
get_global_size(dim)：线程总数量

get_group_id(dim)：dim可以为0，1，2，分别代表CUDA中的blockIdx.x、blockIdx.y、blockIdx.z
get_num_groups(dim)：
get_local_id(dim)：dim可以为0，1，2，分别代表CUDA中的threadIdx.x、threadIdx.y、threadIdx.z
get_local_size(dim)

二、内存模型：

箭头表示可以访问。所有的workItem都可以读Global Memory的数据。我们在利用opencl写并行函数的时候，可以看到函数的形参是类似这样定义的：__global int *C，这就代表C所指向的地方是Global Memory。

三、opencl编程实践

opencl需要包含头文件：#include<CL/cl.h>
这里新建一个Vadd.cl文件，用于保存__kernel函数的相关代码。然后在testOpenCL.cpp中建立环境和调用Vadd.cl文件中的__kernel函数。
实现矩阵乘法（参考：矩阵乘法）：
Vadd.cl中代码如下：

__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;
	}
}

testOpenCL.cpp中代码如下：

#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")

//把文本文件读入一个 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] = '\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;

	//创建平台对象
	status = clGetPlatformIDs(1, &platform, NULL);
	cl_device_id device;
	//创建 GPU 设备
	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 = 4;
	const int Mdim = 3;
	const int Pdim = 4;
	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);

	//创建三个 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.\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) };
	//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\n", status);
		char tbuf[0x10000];
		clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 0x10000, tbuf,
			NULL);
		printf("\n%s\n", 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 << "参数设置错误" << endl;

	//执行 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 << "执行内核时错误" << 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("读取时间的时候发生错误\n");
	rum_time = (double)(ev_end_time - ev_start_time);
	cout << "执行时间为:" << 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("读回数据的时候发生错误\n");

	//结果显示
	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);

	//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;
}

标签：status,OpenCL,入门教程,cl,int,Mdim,float,NULL,快速
来源： https://blog.csdn.net/qq_42775938/article/details/121707888