无法使用beignet在opencl中找到设备

时间:2015-10-02 04:38:29

标签: opencl gpu

我正在尝试使用beignet运行opencl

https://askubuntu.com/questions/412009/open-cl-in-intel

我的系统配置是
英特尔高清显卡5500 NVIDIA GeForce 830M(专用2 GB DDR3)

当我运行以下代码时:

    // 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;
}

我总是得到输出:

 Number of available platforms: 1
 Platform names:
     [0] Experiment Intel Gen OCL Driver [Selected]
 Number of devices available for each type:
     CL_DEVICE_TYPE_CPU: 0
     CL_DEVICE_TYPE_GPU: 0
     CL_DEVICE_TYPE_ACCELERATOR: 0

    *** Detailed information for each device ***

我尝试了各种opencl代码,但没有一个正常工作。为什么没有找到设备,解决方案是什么?

1 个答案:

答案 0 :(得分:0)

运行clinfo实用程序会发生什么?

你可以为你的linux获得clinfo然后运行它。它提供了找到的每个平台和设备的列表。如果您无法通过clinfo列出您的设备,则您的计划无法列出您的设备。

看起来你有一台Nvidia Optimus计算机,这非常糟糕,因为Nvidia没有为Linux上的Optimus提供官方支持。但是,至少应该识别您的Intel CPU。

如果您无法列出,则可能缺少供应商的驱动程序(nvidia)。