Windows7_64位 NVIDIA 卡 OpenCl环境配置

序

最近做一个项目需要用到OpenCL，由于之前没有接触过，所以在环境配置第一关就遇到了一些问题，查阅很多资料才配置完成，现在记录如下，希望给一些童鞋一些帮助。

整个步骤也很简单：

了解系统配置，选择合适的安装包

安装CUDASDK

更新驱动

VS2013下新建C++项目配置环境：

项目右键属性VC++目录，添加包含目录、库目录

项目右键属性连接器->输入，添加附加依赖项

添加测试代码，测试安装完成。

详细操作如下所示！

了解系统配置

首先，你需要了解自己电脑的硬件配置，显卡是哪个厂商出产的啊，支持不支持OpenCL等。这个方面，我们可以利用GPU-Z的工具来查看。

GPU-Z下载地址

这是我主机配置截图：



可以看到我的主机显卡是 英伟达 厂商 NVIDIA GetForce GTX 950 ，在最下面一栏 Computing中显示 支持OpenCL，CUDA。

安装CUDA SDK

查阅资料，发现对于NVIDIA的显卡，并没有单独的OpenCL SDK供安装使用，它是被CUDA SDK Tookits包含的，所以我们只需要下载安装CUDA Tookit即可，我安装的是目前的最新版本CUDA Tookit 7.5下载地址。

选择与自己系统相匹配的版本，安装即可。

安装完成后，所在目录默认路径为：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5\

更新显卡驱动

根据你的系统选择更新为最新的显卡驱动！

首先，检查当前显卡驱动版本：

控制面板—>设备管理器—>显卡—>(右键更新..)



当前我的显卡是最新版本，因此结果为：



此时，即可进行下一步，配置VS环境。

如若你的版本不是最新版，需到官网下载更新驱动！

配置VS环境

打开VS，新建普通控制台C++项目，test。

项目属性—>选择VC++目录，分别在包含目录和库目录下添加：

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5\include



库目录根据版本选择，32位系统则选择win32

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5\lib\x64



项目属性—>链接器—>输入，添加opencl.lib



确定，配置完成！

编写测试程序，验证

(1). HelloWorld.cl

__kernel void hello_kernel(__global const float *a,
__global const float *b,
__global float *result)
{
int gid = get_global_id(0);

result[gid] = a[gid] + b[gid];
}

(2). main.cpp

//
// Book:      OpenCL(R) Programming Guide
// Authors:   Aaftab Munshi, Benedict Gaster, Timothy Mattson, James Fung, Dan Ginsburg
// ISBN-10:   0-321-74964-2
// ISBN-13:   978-0-321-74964-2
// Publisher: Addison-Wesley Professional
// URLs:      http://safari.informit.com/9780132488006/ //            http://www.openclprogrammingguide.com //

// HelloWorld.cpp
//
//    This is a simple example that demonstrates basic OpenCL setup and
//    use.

#include <iostream>
#include <fstream>
#include <sstream>

#ifdef __APPLE__
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif

///
//  Constants
//
const int ARRAY_SIZE = 1000;

///
//  Create an OpenCL context on the first available platform using
//  either a GPU or CPU depending on what is available.
//
cl_context CreateContext()
{
cl_int errNum;
cl_uint numPlatforms;
cl_platform_id firstPlatformId;
cl_context context = NULL;

// First, select an OpenCL platform to run on.  For this example, we
// simply choose the first available platform.  Normally, you would
// query for all available platforms and select the most appropriate one.
errNum = clGetPlatformIDs(1, &firstPlatformId, &numPlatforms);
if (errNum != CL_SUCCESS || numPlatforms <= 0)
{
std::cerr << "Failed to find any OpenCL platforms." << std::endl;
return NULL;
}

// Next, create an OpenCL context on the platform.  Attempt to
// create a GPU-based context, and if that fails, try to create
// a CPU-based context.
cl_context_properties contextProperties[] =
{
CL_CONTEXT_PLATFORM,
(cl_context_properties)firstPlatformId,
0
};
context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_GPU,
NULL, NULL, &errNum);
if (errNum != CL_SUCCESS)
{
std::cout << "Could not create GPU context, trying CPU..." << std::endl;
context = clCreateContextFromType(contextProperties, CL_DEVICE_TYPE_CPU,
NULL, NULL, &errNum);
if (errNum != CL_SUCCESS)
{
std::cerr << "Failed to create an OpenCL GPU or CPU context." << std::endl;
return NULL;
}
}

return context;
}

///
//  Create a command queue on the first device available on the
//  context
//
cl_command_queue CreateCommandQueue(cl_context context, cl_device_id *device)
{
cl_int errNum;
cl_device_id *devices;
cl_command_queue commandQueue = NULL;
size_t deviceBufferSize = -1;

// First get the size of the devices buffer
errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, 0, NULL, &deviceBufferSize);
if (errNum != CL_SUCCESS)
{
std::cerr << "Failed call to clGetContextInfo(...,GL_CONTEXT_DEVICES,...)";
return NULL;
}

if (deviceBufferSize <= 0)
{
std::cerr << "No devices available.";
return NULL;
}

// Allocate memory for the devices buffer
devices = new cl_device_id[deviceBufferSize / sizeof(cl_device_id)];
errNum = clGetContextInfo(context, CL_CONTEXT_DEVICES, deviceBufferSize, devices, NULL);
if (errNum != CL_SUCCESS)
{
delete[] devices;
std::cerr << "Failed to get device IDs";
return NULL;
}

// In this example, we just choose the first available device.  In a
// real program, you would likely use all available devices or choose
// the highest performance device based on OpenCL device queries
commandQueue = clCreateCommandQueue(context, devices[0], 0, NULL);
if (commandQueue == NULL)
{
delete[] devices;
std::cerr << "Failed to create commandQueue for device 0";
return NULL;
}

*device = devices[0];
delete[] devices;
return commandQueue;
}

///
//  Create an OpenCL program from the kernel source file
//
cl_program CreateProgram(cl_context context, cl_device_id device, const char* fileName)
{
cl_int errNum;
cl_program program;

std::ifstream kernelFile(fileName, std::ios::in);
if (!kernelFile.is_open())
{
std::cerr << "Failed to open file for reading: " << fileName << std::endl;
return NULL;
}

std::ostringstream oss;
oss << kernelFile.rdbuf();

std::string srcStdStr = oss.str();
const char *srcStr = srcStdStr.c_str();
program = clCreateProgramWithSource(context, 1,
(const char**)&srcStr,
NULL, NULL);
if (program == NULL)
{
std::cerr << "Failed to create CL program from source." << std::endl;
return NULL;
}

errNum = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
if (errNum != CL_SUCCESS)
{
// Determine the reason for the error
char buildLog[16384];
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG,
sizeof(buildLog), buildLog, NULL);

std::cerr << "Error in kernel: " << std::endl;
std::cerr << buildLog;
clReleaseProgram(program);
return NULL;
}

return program;
}

///
//  Create memory objects used as the arguments to the kernel
//  The kernel takes three arguments: result (output), a (input),
//  and b (input)
//
bool CreateMemObjects(cl_context context, cl_mem memObjects[3],
float *a, float *b)
{
memObjects[0] = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(float)* ARRAY_SIZE, a, NULL);
memObjects[1] = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(float)* ARRAY_SIZE, b, NULL);
memObjects[2] = clCreateBuffer(context, CL_MEM_READ_WRITE,
sizeof(float)* ARRAY_SIZE, NULL, NULL);

if (memObjects[0] == NULL || memObjects[1] == NULL || memObjects[2] == NULL)
{
std::cerr << "Error creating memory objects." << std::endl;
return false;
}

return true;
}

///
//  Cleanup any created OpenCL resources
//
void Cleanup(cl_context context, cl_command_queue commandQueue,
cl_program program, cl_kernel kernel, cl_mem memObjects[3])
{
for (int i = 0; i < 3; i++)
{
if (memObjects[i] != 0)
clReleaseMemObject(memObjects[i]);
}
if (commandQueue != 0)
clReleaseCommandQueue(commandQueue);

if (kernel != 0)
clReleaseKernel(kernel);

if (program != 0)
clReleaseProgram(program);

if (context != 0)
clReleaseContext(context);

}

///
//  main() for HelloWorld example
//
int main(int argc, char** argv)
{
cl_context context = 0;
cl_command_queue commandQueue = 0;
cl_program program = 0;
cl_device_id device = 0;
cl_kernel kernel = 0;
cl_mem memObjects[3] = { 0, 0, 0 };
cl_int errNum;

// Create an OpenCL context on first available platform
context = CreateContext();
if (context == NULL)
{
std::cerr << "Failed to create OpenCL context." << std::endl;
return 1;
}

// Create a command-queue on the first device available
// on the created context
commandQueue = CreateCommandQueue(context, &device);
if (commandQueue == NULL)
{
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Create OpenCL program from HelloWorld.cl kernel source
program = CreateProgram(context, device, "HelloWorld.cl");
if (program == NULL)
{
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Create OpenCL kernel
kernel = clCreateKernel(program, "hello_kernel", NULL);
if (kernel == NULL)
{
std::cerr << "Failed to create kernel" << std::endl;
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Create memory objects that will be used as arguments to
// kernel.  First create host memory arrays that will be
// used to store the arguments to the kernel
float result[ARRAY_SIZE];
float a[ARRAY_SIZE];
float b[ARRAY_SIZE];
for (int i = 0; i < ARRAY_SIZE; i++)
{
a[i] = (float)i;
b[i] = (float)(i * 2);
}

if (!CreateMemObjects(context, memObjects, a, b))
{
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Set the kernel arguments (result, a, b)
errNum = clSetKernelArg(kernel, 0, sizeof(cl_mem), &memObjects[0]);
errNum |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &memObjects[1]);
errNum |= clSetKernelArg(kernel, 2, sizeof(cl_mem), &memObjects[2]);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error setting kernel arguments." << std::endl;
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

size_t globalWorkSize[1] = { ARRAY_SIZE };
size_t localWorkSize[1] = { 1 };

// Queue the kernel up for execution across the array
errNum = clEnqueueNDRangeKernel(commandQueue, kernel, 1, NULL,
globalWorkSize, localWorkSize,
0, NULL, NULL);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error queuing kernel for execution." << std::endl;
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Read the output buffer back to the Host
errNum = clEnqueueReadBuffer(commandQueue, memObjects[2], CL_TRUE,
0, ARRAY_SIZE * sizeof(float), result,
0, NULL, NULL);
if (errNum != CL_SUCCESS)
{
std::cerr << "Error reading result buffer." << std::endl;
Cleanup(context, commandQueue, program, kernel, memObjects);
return 1;
}

// Output the result buffer
for (int i = 0; i < ARRAY_SIZE; i++)
{
std::cout << result[i] << " ";
}
std::cout << std::endl;
std::cout << "Executed program succesfully." << std::endl;
Cleanup(context, commandQueue, program, kernel, memObjects);

return 0;
}

运行结果

tips

在MacOS X 10.6中，OpenCL的头文件是存在OpenCL目录中，也就是

#include <OpenCL/opencl.h>

但是在Windows下（以及可能所有其它的OS下），都是

#include <CL/cl.h>

因此，如果想要让同一个程序，可以同时在各种OS下都能编译的话，在include头文件时，建议写成：

#ifdef __APPLE__
#include <OpenCL/opencl.h>
#else
#include <CL/cl.h>
#endif

这样就可以同时在MacOS X 10.6下，以及其它的OS下使用。