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。
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;
}
我演示1个4×5与5×6的矩阵的乘法:
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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