OpenCl入门getting-started-with-opencl-and-gpu-computing

原文来自于：getting-started-with-opencl-and-gpu-computing/

对整个程序的注释：http://www.kimicat.com/opencl-1/opencl-jiao-xue-yi

但是对CUDA比较熟悉的用户来说，应该不需要看注释就能理解全部的程序

main.cpp

 #include <stdio.h>
 #include <stdlib.h>
 #include <CL/cl.h>
 #define MAX_SOURCE_SIZE (0x100000)
 int main(void)
 {
         // Create the two input vectors
         int i;
         const int LIST_SIZE = ;
         int *A = (int*) malloc(sizeof(int) * LIST_SIZE);
         int *B = (int*) malloc(sizeof(int) * LIST_SIZE);
         for (i = ; i < LIST_SIZE; i++)
         {
                 A[i] = i;
                 B[i] = LIST_SIZE - i;
         }
 
         // Load the kernel source code into the array source_str
         FILE *fp;
         char *source_str;
         size_t source_size;
         fp = fopen("vector_add_kernel.cl", "r");
 
         if (!fp)
         {
                 fprintf(stderr, "Failed to load kernel.\n");
                 exit();
         }
         source_str = (char*) malloc(MAX_SOURCE_SIZE);
         source_size = fread(source_str, , MAX_SOURCE_SIZE, fp);
         fclose(fp);
 
         // 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(, &platform_id, &ret_num_platforms);
         ret = clGetDeviceIDs(platform_id, CL_DEVICE_TYPE_DEFAULT, , &device_id,&ret_num_devices);
 
         // Create an OpenCL context
         cl_context context = clCreateContext(NULL, , &device_id, NULL, NULL, &ret);
         // Create a command queue
         cl_command_queue command_queue = clCreateCommandQueue(context, device_id, , &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, ,LIST_SIZE * sizeof(int), A, , NULL, NULL);
         ret = clEnqueueWriteBuffer(command_queue, b_mem_obj, CL_TRUE, ,LIST_SIZE * sizeof(int), B, , NULL, NULL);
 
         // Create a program from the kernel source
         cl_program program = clCreateProgramWithSource(context, ,(const char **) &source_str, (const size_t *) &source_size, &ret);
 
         // Build the program
         ret = clBuildProgram(program, , &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, , sizeof(cl_mem), (void *) &a_mem_obj);
         ret = clSetKernelArg(kernel, , sizeof(cl_mem), (void *) &b_mem_obj);
         ret = clSetKernelArg(kernel, , 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 = ; // Process one item at a time
         ret = clEnqueueNDRangeKernel(command_queue, kernel, , NULL,&global_item_size, &local_item_size, , 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, ,LIST_SIZE * sizeof(int), C, , NULL, NULL);
 
         // Display the result to the screen
         for (i = ; 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 ;
 
 }

vector_add_kernel.cl

__kernel void vector_add(__global const int *A, __global const int *B, __global int *C)
{
    // Get the index of the current element to be processed
    int i = get_global_id();
    // Do the operation
    C[i] = A[i] + B[i];
}

之前已经安装好了CUDA的运行环境，这里作者说使用g++ -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lOpenCL main.cpp -o openclApp命令来执行，结果提示

'clGetPlatformIDs' undefined reference，但是我的include和lib都是正常的，因此，调整编译命令为：

g++  main.cpp -o openclApp -I/usr/local/cuda/include -L/usr/local/cuda/lib64 -lOpenCL

编译通过并运行通过，因此gcc编译选项的顺序也对程序有一定影响（理论上不应该有这个问题）。但是，这个问题使用clang编译就没有任何影响。