Pableins | ME

Pableins | ME

OpenCL

From October 02, 2019

A simple example of parallel execution using OpenCL, which is an API that allows the execution of tasks on the CPU and GPU.

In this example we cover the square function in the kernel while validating the results in the main function as well.

OpenCL

A basic example of OpenCL with a simple kernel.

Kernel

// Kernel block
kernel void square(global float* input, global float* output)
{
    size_t i = get_global_id(0);
    output[i] = input[i] * input[i];
}

Main

#include <chrono>
#include <stdio.h>
#include <stdlib.h>
#include <OpenCL/opencl.h>
#include "mykernel.cl.h"

#define NUM_VALUES 2048000

static int validate(cl_float* input, cl_float* output)
{
    for (int i = 0; i < NUM_VALUES; i++)
    {
        if ( output[i] != (input[i] * input[i]) )
        {
            fprintf(stdout, "Error: Element %d did not match expected output.\n", i);

            fprintf(stdout, "       Got %1.4f, EXPECTED %1.4f\n", output[i], input[i] * input[i]);

            fflush(stdout);
            return 0;
        }
    }
    return 1;
}

int main (int argc, const char * argv[])
{
    char name[128];
    dispatch_queue_t queue = gcl_create_dispatch_queue(CL_DEVICE_TYPE_GPU, NULL);

    if (queue == NULL)
    {
        queue = gcl_create_dispatch_queue(CL_DEVICE_TYPE_CPU, NULL);
    }

    cl_device_id gpu = gcl_get_device_id_with_dispatch_queue(queue);
    clGetDeviceInfo(gpu, CL_DEVICE_NAME, 128, name, NULL);
    fprintf(stdout, "Created a dispatch queue using the %s\n", name);

    float* test_in = (float*) malloc(sizeof(cl_float) * NUM_VALUES);

    for (int i = 0; i < NUM_VALUES; i++)
    {
        test_in[i] = (cl_float) i;
    }

    float* test_out = (float*)malloc(sizeof(cl_float) * NUM_VALUES);

    // Kernel space
    void* mem_in  = gcl_malloc(sizeof(cl_float) * NUM_VALUES, test_in, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR);
    void* mem_out = gcl_malloc(sizeof(cl_float) * NUM_VALUES, NULL, CL_MEM_WRITE_ONLY);

    dispatch_sync(queue, ^{
        size_t wgs;
        gcl_get_kernel_block_workgroup_info(square_kernel, CL_KERNEL_WORK_GROUP_SIZE, sizeof(wgs), &wgs, NULL);

        cl_ndrange range = {
            1,                     // The number of dimensions to use.
            {0, 0, 0},             // The offset in each dimension.
            {NUM_VALUES, 0, 0},    // The global range —how many items in each dimension
            {wgs, 0, 0}            // The local size of each workgroup.
        };

        auto t1 = std::chrono::high_resolution_clock::now();
        square_kernel(&range,(cl_float*)mem_in, (cl_float*)mem_out);
        auto t2 = std::chrono::high_resolution_clock::now();

        auto duration = std::chrono::duration_cast<std::chrono::milliseconds>( t2 - t1 ).count();
        printf("%lld milliseconds\n", duration);

        gcl_memcpy(test_out, mem_out, sizeof(cl_float) * NUM_VALUES);
    });

    // Check to see if the kernel did what it was supposed to:
    if ( validate(test_in, test_out))
    {
        fprintf(stdout, "All values were properly squared.\n");
    }

    // Don't forget to free up the CL device's memory when you're done.
    gcl_free(mem_in);
    gcl_free(mem_out);

    // And the same goes for system memory, as usual.
    free(test_in);
    free(test_out);

    // Finally, release your queue just as you would any GCD queue.
    dispatch_release(queue);
}
 
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