OpenCL : Can the two GPU devices in Mali-T628 work in parallel

Hello Anthony,

Good to know that we can use the devices in parallel.

Code is given below.

I create the opencl context. Then get the platforms and then get the devices. For each device I call a function initOpenCL which creates the opencl program and kernel and ties the commandqueue to the device. I use slightly modified versions of the functions given in Mali SDK.

--Kiran

bool createCommandQueue(cl_context context, cl_command_queue* commandQueue, cl_device_id* device)

{

    cl_int errorNumber = 0;

    /* Set up the command queue with the selected device. */

    *commandQueue = clCreateCommandQueue(context, *device, CL_QUEUE_PROFILING_ENABLE, &errorNumber);

    if (!checkSuccess(errorNumber))

    {  

        cerr << "Failed to create the OpenCL command queue. " << __FILE__ << ":"<< __LINE__ << endl;

        return false;

    }  

    return true;

}

int initOpenCL(cl_context& context, cl_device_id& device, cl_command_queue& commandQueue, cl_program& program, cl_kernel& kernel, cl_mem* memoryObjects)

{

    const unsigned int numberOfMemoryObjects = 3;

    cl_int errorNumber;

    if (!createCommandQueue(context, &commandQueue, &device))

    {

        cleanUpOpenCL(context, commandQueue, program, kernel, memoryObjects, numberOfMemoryObjects);

        cerr << "Failed to create the OpenCL command queue. " << __FILE__ << ":"<< __LINE__ << endl;

        return 1;

    } else {

        cout<<"Successfully created commandQ"<<endl ;

    }

    if (!createProgram(context, device, "assets/matmul.cl", &program))

    {

        cleanUpOpenCL(context, commandQueue, program, kernel, memoryObjects, numberOfMemoryObjects);

        cerr << "Failed to create OpenCL program." << __FILE__ << ":"<< __LINE__ << endl;

        return 1;

    } else {

        cout<<"Successfully created program"<<endl ;

    }

    kernel = clCreateKernel(program, "matmul_simple_loop_switched", &errorNumber);

    if (!checkSuccess(errorNumber))

    {

        cleanUpOpenCL(context, commandQueue, program, kernel, memoryObjects, numberOfMemoryObjects);

        cerr << "Failed to create OpenCL kernel. " << __FILE__ << ":"<< __LINE__ << endl;

        return 1;

    } else {

        cout<<"Successfully created kernel"<<endl ;

    }

    return 0 ;

}

int main()

{

    cl_uint num_platforms;       

    cl_platform_id platforms[32];

    cl_uint num_devices;            

    cl_device_id devices[32];        

    cl_command_queue commandQueue[32] ;

    cl_program program[32] ;

    cl_kernel kernel[32] ;

    cl_context context = 0;

    createContext(&context)

    clGetPlatformIDs (32, platforms, &num_platforms);

    int i, j ;

    for(i = 0; i < num_platforms; i++) {

            clGetDeviceIDs (platforms[i], CL_DEVICE_TYPE_ALL, sizeof(devices), devices, &num_devices);

            for(j = 0; j < num_devices; j++)

            {

                    initOpenCL(context,devices[j],commandQueue[j],program[j],kernel[j],memoryObjects) ;

            }

    }

}

--Kiran

Hello Anthony,

So are you suggesting that it is likely to be an error in my code. Have you seen examples of opencl kernels running in parallel on the two devices on the Mali-T628 GPU ?

--Kiran

Hi,

I can't see anything wrong with the part you pasted, could you please paste he rest of the host code?

I'll try to run it tomorrow.

Thanks

Hi Kiran,

Your code is roughly doing:

foreach device in devices:

  enqueue( queue[device], kernel )

foreach device in devices:

   finish(queue[device])

The problem is you're not flushing the queues therefore the kernels only get sent to the GPU when clFinish is called which is not what you want.

If you add a clFlush after the enqueue then the jobs will get executed in parallel:

foreach device in devices:

  enqueue( queue[device], kernel )

  clFlush( queue[device] )

foreach device in devices:

   finish(queue[device])

You should then see something like:

Profiling information:

Queued time:    0.315959ms

Wait time:      14.0343ms

Run time:       15394.6ms

Profiling information:

Queued time:    14.0195ms

Wait time:      0.079417ms

Run time:       15331.2ms

Hope this helps,

Thanks a ton Anthony. Works fine with the flush.