#include <stdio.h>

// Include OpenCL/opencl.h to include everything you need for OpenCL

// development on OS X v10.7 or later.

#include <OpenCL/opencl.h>

// In this example, mykernel.cl.h is the header file that contains

// the kernel block declaration.  The name of this header file would

// be different if the name of the file containing the kernel source

// were different.

// This header file is generated by Xcode.

#include "mykernel.cl.h"

static void print_device_info(cl_device_id device) {

    char name[128];

    char vendor[128];

    clGetDeviceInfo(device, CL_DEVICE_NAME, 128, name, NULL);

    clGetDeviceInfo(device, CL_DEVICE_VENDOR, 128, vendor, NULL);

    fprintf(stdout, "%s : %s\n", vendor, name);

}

// Demonstrates how to get the global OpenCL context, and how to ask that

// context about the devices it contains.  It also shows how

// to create a dispatch queue by asking for a device type (CPU or GPU) and

// by specifying the queue's OpenCL device directly.

static void hello_world_sample1 ()

{

    int i;

    // Ask for the global OpenCL context:

    // Note: If you will not be enqueing to a specific device, you do not need

    // to retrieve the context.

    cl_context context = gcl_get_context();

    // Query this context to see what kinds of devices are available.

    size_t length;

    cl_device_id devices[8];

    clGetContextInfo(

         context, CL_CONTEXT_DEVICES, sizeof(devices), devices, &length);

    // Walk over these devices, printing out some basic information.  You could

    // query any of the information available about the device here.

    fprintf(stdout, "The following devices are available for use:\n");

    int num_devices = (int)(length / sizeof(cl_device_id));

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

        print_device_info(devices[i]);

    }

    // To do any work, you need to create a dispatch queue associated

    // with some OpenCL device.  You can either let the system give you

    // a GPU—perhaps the only GPU—or the CPU device.  Or, you can

    // create a dispatch queue with a cl_device_id you specify.  This

    // device id comes from the OpenCL context, as above.  Below are three

    // examples.

    // 1. Ask for a GPU-based dispatch queue; notice that here we do not provide

    // a device id.  Instead, we let the system tell us the most capable GPU.

    dispatch_queue_t gpu_queue =

       gcl_create_dispatch_queue(CL_DEVICE_TYPE_GPU, NULL);

    // Get the device from the queue, so we can ask OpenCL questions about it.

    // Note that we check to make sure there WAS an OpenCL-capable GPU in the

    // system by checking against a NULL return value.

    if (gpu_queue != NULL) {

        cl_device_id gpu_device =

          gcl_get_device_id_with_dispatch_queue(gpu_queue);

        fprintf(stdout, "\nAsking for CL_DEVICE_TYPE_GPU gives us:\n");

        print_device_info(gpu_device);

    } else {

        fprintf(stdout, "\nYour system does not contain an OpenCL-compatible "

                "GPU\n.");

    }

    // 2. Try the same thing for CL_DEVICE_TYPE_CPU.  All Mac

    // systems have a CPU OpenCL device, so you don't have to

    // check for NULL, as you have to do in the case of a GPU.

    dispatch_queue_t cpu_queue =

        gcl_create_dispatch_queue(CL_DEVICE_TYPE_CPU, NULL);

    cl_device_id cpu_device = gcl_get_device_id_with_dispatch_queue(cpu_queue);

    fprintf(stdout, "\nAsking for CL_DEVICE_TYPE_CPU gives us:\n");

    print_device_info(cpu_device);

    // 3. Or perhaps you are in a situation where you want a specific device

    // from the list of devices you found on the context.

    // Notice the difference here:

    // Pass CL_DEVICE_TYPE_USE_ID and a device_id. This example just uses the

    // first device on the context from above, whatever that might be.

    dispatch_queue_t custom_queue =

        gcl_create_dispatch_queue(CL_DEVICE_TYPE_USE_ID, devices[0]);

    cl_device_id custom_device =

        gcl_get_device_id_with_dispatch_queue(custom_queue);

    fprintf(stdout,

       "\nAsking for CL_DEVICE_TYPE_USE_ID and our own device gives us:\n");

    print_device_info(custom_device);

    // Now you can use any of these 3 dispatch queues to run some kernels.

    …                                              // Run your kernels here.

    // Use the GCD API to free your queues.

    dispatch_release(custom_queue);

    dispatch_release(cpu_queue);

    if (gpu_queue != NULL) dispatch_release(gpu_queue);

}

// This listing shows how to obtain workgroup info –

// useful for obtaining peak performance—from the kernel block.

static void hello_world_sample2() {

    // Get a queue backed by a GPU for running our squaring kernel.

    dispatch_queue_t queue =

       gcl_create_dispatch_queue(CL_DEVICE_TYPE_GPU, NULL);

    // Did we get a GPU?  If not, fall back to the CPU device.

    if (queue == NULL) {

        gcl_create_dispatch_queue(CL_DEVICE_TYPE_GPU, NULL);

    }

    // In any case, print out the device you're using:

    fprintf(stdout, "\nExamining workgroup info for square_kernel on device ");

    print_device_info(gcl_get_device_id_with_dispatch_queue(queue));

    // Now find out what OpenCL thinks is the best workgroup size for

    // executing this kernel on this particular device.  Notice that this

    // method is executed in a block, on a dispatch queue you've created

    // with OpenCL.

    dispatch_sync(queue,

                  ^{

                      size_t wgs, preferred_wgs_multiple;

                      cl_ulong local_memsize, private_memsize;

                      // The next two calls give you information about how much

                      // memory, local and private, is used by the kernel on this

                      // particular device.

                      gcl_get_kernel_block_workgroup_info(square_kernel,

                                CL_KERNEL_LOCAL_MEM_SIZE,

                                sizeof(local_memsize),

                                &local_memsize, NULL);

                      fprintf(stdout, "Local memory size: %lld\n", local_memsize);

                      gcl_get_kernel_block_workgroup_info(square_kernel,

                                CL_KERNEL_PRIVATE_MEM_SIZE,

                                sizeof(private_memsize),

                                &private_memsize, NULL);

                      fprintf(stdout,

                          "Private memory size: %lld\n", private_memsize);

                       // Ask OpenCL to suggest the optimal workgroup

                       // size for this kernel on this device.

                       gcl_get_kernel_block_workgroup_info(square_kernel,

                               CL_KERNEL_WORK_GROUP_SIZE,

                               sizeof(wgs), &wgs, NULL);

                       fprintf(stdout, "Workgroup size: %ld\n", wgs);

                       // Finally, you can ask OpenCL for a workgroup size multiple.

                       // This is a performance hint.

                       gcl_get_kernel_block_workgroup_info(square_kernel,

                               CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE,

                               sizeof(preferred_wgs_multiple),

                               &preferred_wgs_multiple, NULL);

                       fprintf(stdout, "Preferred workgroup size multiple: %ld\n",

                               preferred_wgs_multiple);

                      // You can now use these workgroup values to craft an

                      // appropriate cl_ndrange structure for use in launching

                      // your kernel.

                });

    dispatch_release(queue);

}

int main(int argc, const char* argv[]) {

    hello_world_sample1();

    hello_world_sample2();

}

gcl_get_kernel_block_workgroup_info(

                             square_kernel,

                             CL_KERNEL_LOCAL_MEM_SIZE,

                             sizeof(local_memsize),

                             &local_memsize, NULL);

gcl_get_kernel_block_workgroup_info(

                             square_kernel,

                             CL_KERNEL_WORK_GROUP_SIZE,

                             sizeof(workgroup_size), &workgroup_size, NULL);

                             fprintf(stdout, "Workgroup size: %ld\n",

                                     workgroup_size);

gcl_get_kernel_block_workgroup_info(

                             square_kernel,

                             CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE,

                             sizeof(preferred_workgroup_size_multiple),

                             &preferred_workgroup_size_multiple, NULL);

cl_ndrange range = {

        1,                  // The number of dimensions to use.

        {0, 0, 0},          // The offset in each dimension.  Want to process

                                // ALL of the data, so all three offsets are 0.

                                // Always pass an offset for each of the

                                // three dimensions even though the workgroup

                                // may have fewer than three dimensions.

        {NUM_VALUES, 0, 0},  // The global range—this is how many items

                                // total in each dimension you want to

                                // process.

                                // Always pass the global range for each of the

                                // three dimensions even though the workgroup

                                // may have fewer than three dimensions.

        {workgroup_size, 0, 0 } // The local size of each workgroup.  This

                                // determines the number of work items per

                                // workgroup.  It indirectly affects the

                                // number of workgroups, since the global

                                // size / local size yields the number of

                                // workgroups.  So in this test case,

                                // have NUM_VALUE/workgroup_size workgroups.

                                // Always pass the workgroup size for each of the

                                // three dimensions even though the workgroup

                                // may have fewer than three dimensions.

};