我也得到错误的结果，而且每次执行的结果都是随机的。这意味着 C 数组中的 values 永远不会被覆盖，并且会打印分配的内存位置中未初始化的随机 values。

调试的第一步是打印 ret 并查看哪里出了问题：

• 在 clGetDeviceIDs 之后出现错误 -1（未找到设备）。 CL_DEVICE_TYPE_ALL 清除；然后选择的设备是我的 Nvidia GPU。
• 在 clCreateProgramWithSource 之后出现错误 -6（主机内存不足）。要解决此问题，请使用 cl_program program = clCreateProgramWithSource(context, 1, (const char**)&source_str, NULL, &ret);。
• 在 clEnqueueNDRangeKernel 之后出现错误 -54（无效的工作组大小）。要解决此问题，请将 global_item_size 设为 local_item_size 的倍数：size_t global_item_size = ((LIST_SIZE+local_item_size-1)/local_item_size)*local_item_size;。由于您预期的全局大小（或者更确切地说分配缓冲区的大小）不是工作组大小的倍数，因此您还应该在内核中放置一个保护子句 if(i>=10) return;；否则内核可能会在未定义的内存空间中写入 values ，这可能会导致崩溃。

然后，一切都按预期工作。这是整个固定代码：

#define CL_USE_DEPRECATED_OPENCL_1_2APIS
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <CL/cl.h>

#define MAX_SOURCE_SIZE (0x100000)

int main(void) {
    // Create the two input vectors
    int i;
    const int LIST_SIZE = 10;
    int* A = (int*)malloc(sizeof(int)*LIST_SIZE);
    int* B = (int*)malloc(sizeof(int)*LIST_SIZE);
    int* C = (int*)malloc(sizeof(int)*LIST_SIZE);
    for(i = 0; i < LIST_SIZE; i++) {
        A[i] = i;
        B[i] = LIST_SIZE - i;
        C[i] = 0;
    }

    // Load the kernel source code into the array source_str
    size_t source_size;

    const char* source_str =
        "__kernel void vector_add(__global int *A, __global int *B, __global int *C) {\n"
        "    int i = get_global_id(0);\n"
        "    if(i>=10) return;\n"
        "    C[i] = A[i]+B[i];\n"
        "}"
    ;

    // 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_ALL, 1,
        &device_id, &ret_num_devices);

    // Create an OpenCL context
    cl_context context = clCreateContext(NULL, 1, &device_id, NULL, NULL, &ret);
    std::cout << "context " << ret << std::endl;

    // Create a command queue
    cl_command_queue command_queue = clCreateCommandQueue(context, device_id, 0, &ret);
    std::cout << "queue " << ret << std::endl;

    // 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);
    std::cout << "buffer " << ret << std::endl;

    // 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);
    std::cout << "write " << ret << std::endl;

    // Create a program from the kernel source
    cl_program program = clCreateProgramWithSource(context, 1, (const char**)&source_str, NULL, &ret);
    //cl_program program = clCreateProgramWithSource(context, 1, (const char**)&source_str, (const size_t *)&source_size, &ret);
    std::cout << "program " << ret << std::endl;

    // Build the program
    ret = clBuildProgram(program, 1, &device_id, NULL, NULL, NULL);

    // Create the OpenCL kernel
    cl_kernel kernel = clCreateKernel(program, "vector_add", &ret);
    std::cout << "kernel " << ret << std::endl;

    // 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);
    std::cout << "args " << ret << std::endl;

    // Execute the OpenCL kernel on the list
    size_t local_item_size = 64; // Divide work items into groups of 64
    size_t global_item_size = ((LIST_SIZE+local_item_size-1)/local_item_size)*local_item_size; // make global range a multiple of local range
    ret = clEnqueueNDRangeKernel(command_queue, kernel, 1, NULL, &global_item_size, &local_item_size, 0, NULL, NULL);
    std::cout << "run " << ret << std::endl;
    clFinish(command_queue);

    // Read the memory buffer C on the device to the local variable C
    ret = clEnqueueReadBuffer(command_queue, c_mem_obj, CL_TRUE, 0,
        LIST_SIZE * sizeof(int), C, 0, NULL, NULL);
    std::cout << "read " << ret << std::endl;

    // 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;
}

调试很麻烦。帮自己一个忙，将这个 https://github.com/ProjectPhysX/OpenCL-Wrapper 与 C++ 一起使用。这消除了所有代码开销以及随之而来的无数错误可能性。