main_add_1024_ints.cpp

/*
 This is a modified version of:
 https://www.eriksmistad.no/getting-started-with-opencl-and-gpu-computing/
 
 It allows to start doing GPGPU in a minimalistic way,
 with a very simple algorithm.
 */

constexpr auto kernel_file = "vector_add_kernel.cl";

int main(void) {
  // Create the two input vectors
  int i;
  const int LIST_SIZE = 1024;
  int *A = (int*)malloc(sizeof(int)*LIST_SIZE);
  int *B = (int*)malloc(sizeof(int)*LIST_SIZE);
  for(i = 0; i < LIST_SIZE; i++) {
    A[i] = i;
    B[i] = LIST_SIZE - i;
  }
  
  // Get platform and device information
  cl_platform_id platform_id = NULL;
  cl_device_id device_id = NULL;
  cl_uint ret_num_devices;
  cl_uint ret_num_platforms;
  cl_int ret = clGetPlatformIDs(1, &platform_id, &ret_num_platforms);
  ret = clGetDeviceIDs( platform_id, CL_DEVICE_TYPE_DEFAULT, 1,
                       &device_id, &ret_num_devices);
  
  // Create an OpenCL context
  cl_context context = clCreateContext( NULL, 1, &device_id, NULL, NULL, &ret);
  
  // Create a command queue
  cl_command_queue command_queue = clCreateCommandQueue(context, device_id, 0, &ret);
  
  // Create memory buffers on the device for each vector
  cl_mem a_mem_obj = clCreateBuffer(context, CL_MEM_READ_ONLY,
                                    LIST_SIZE * sizeof(int), NULL, &ret);
  cl_mem b_mem_obj = clCreateBuffer(context, CL_MEM_READ_ONLY,
                                    LIST_SIZE * sizeof(int), NULL, &ret);
  cl_mem c_mem_obj = clCreateBuffer(context, CL_MEM_WRITE_ONLY,
                                    LIST_SIZE * sizeof(int), NULL, &ret);
  
  // Copy the lists A and B to their respective memory buffers
  ret = clEnqueueWriteBuffer(command_queue, a_mem_obj, CL_TRUE, 0,
                             LIST_SIZE * sizeof(int), A, 0, NULL, NULL);
  ret = clEnqueueWriteBuffer(command_queue, b_mem_obj, CL_TRUE, 0,
                             LIST_SIZE * sizeof(int), B, 0, NULL, NULL);
  
  // Create a program from the kernel source
  auto kernel_src = read_kernel(kernel_file);
  auto kernel_c_src = kernel_src.c_str();
  auto source_size = kernel_src.size();
  cl_program program = clCreateProgramWithSource(context, 1,
                                                 (const char **)&kernel_c_src, (const size_t *)&source_size, &ret);

  // Build the program
  ret = clBuildProgram(program, 1, &device_id, NULL, NULL, NULL);
  
  // Create the OpenCL kernel
  cl_kernel kernel = clCreateKernel(program, "vector_add", &ret);
  
  // Set the arguments of the kernel
  ret = clSetKernelArg(kernel, 0, sizeof(cl_mem), (void *)&a_mem_obj);
  ret = clSetKernelArg(kernel, 1, sizeof(cl_mem), (void *)&b_mem_obj);
  ret = clSetKernelArg(kernel, 2, sizeof(cl_mem), (void *)&c_mem_obj);
  
  // Execute the OpenCL kernel on the list
  size_t global_item_size = LIST_SIZE; // Process the entire lists
  size_t local_item_size = 64; // Divide work items into groups of 64
  ret = clEnqueueNDRangeKernel(command_queue, kernel, 1, NULL,
                               &global_item_size, &local_item_size, 0, NULL, NULL);
  
  // Read the memory buffer C on the device to the local variable C
  int *C = (int*)malloc(sizeof(int)*LIST_SIZE);
  ret = clEnqueueReadBuffer(command_queue, c_mem_obj, CL_TRUE, 0,
                            LIST_SIZE * sizeof(int), C, 0, NULL, NULL);
  
  // Display the result to the screen
  for(i = 0; i < LIST_SIZE; i++)
    printf("%d + %d = %d\n", A[i], B[i], C[i]);
  
  // Clean up
  ret = clFlush(command_queue);
  ret = clFinish(command_queue);
  ret = clReleaseKernel(kernel);
  ret = clReleaseProgram(program);
  ret = clReleaseMemObject(a_mem_obj);
  ret = clReleaseMemObject(b_mem_obj);
  ret = clReleaseMemObject(c_mem_obj);
  ret = clReleaseCommandQueue(command_queue);
  ret = clReleaseContext(context);
  free(A);
  free(B);
  free(C);
  return 0;
}