OpenCL - GPU 总和和 CPU 总和不同

我是OpenCL的新手。

我写了一个程序，应该对 100 万个元素数组进行并行归约。在代码的最后一部分，我比较了 CPU总和和 GPU总和 ，它们不一样，这就是问题所在。我的本地尺码是 64。从索引"90"开始，GPU 中的总和开始变大。

编辑：如果我对较小的数字求和(现在我和 0 - 1m(，假设 1 的最终总和是正确的。

内核：

__kernel void gpuSumfunc(  __global float *vec ,__global float* sum, int n)          
{
    __local float tempSum[64];
    const int i;                                                        
    const int globalID = get_global_id(0); //BLOCK_DIM*BLOCK_IND+THREAD_ID
    const int tid = get_local_id(0);         //THREAD_ID
    const int BlockDIM = get_local_size(0);//BLOCK_DIM=64
    if (globalID < n)
    {
        tempSum[tid] = vec[globalID];    //Inserting global data to local data
    }
    else
    {
        tempSum[tid] = 0;
    }
        barrier(CLK_LOCAL_MEM_FENCE);    //Wating for all the threads to copy their data
        for (i = BlockDIM / 2; i > 0; i /= 2)
        {
            if (tid < i)
            {
                tempSum[tid] += tempSum[tid + i];
            }
            barrier(CLK_LOCAL_MEM_FENCE);
        }
        if (tid == 0)
        {
            sum[get_group_id(0)] = tempSum[0];
        }
    }

主要：

//HOST-cpu
    float *h_a;//input
    float *h_b;//output
    float *h_s;
    //DEVICE-gpu
    cl_mem d_a;//input buffer
    cl_mem d_b;//Output
               //Kernel File
    FILE* fileKernel;
    //Memory allocation - cpu input 
    vector = (float*)malloc(n * sizeof(float));
    h_a = (float*)malloc(n * sizeof(float));
    h_b = (float*)malloc(n * sizeof(float));
    h_s = (float*)malloc(n * sizeof(float));
    *vector = { 0 };
    *h_a = { 0 };
    *h_b = { 0 };
    *h_s = { 0 };

    //Initializing Data for gpu
    for (i = 0; i < n; i++) {
        h_a[i] = i;//(float)i;
    }

    //Initializing Data for cpu
    for (i = 0; i < n; i++) {
        vector[i] = i;//(float)i;
    }
    fileKernel = fopen("KernelCode.cl", "r");
    if (!fileKernel)
    {
        printf("Cannot open kernel file!n");
        exit(1);
    }
    // Read kernel code
    kernelSource = (char*)malloc(MAX_SOURCE_SIZE);
    source_size = fread(kernelSource, 1, MAX_SOURCE_SIZE, fileKernel);
    fclose(fileKernel);

    error = clGetPlatformIDs(2, cp_Platform, NULL); //array with two devices
    error = clGetDeviceIDs(cp_Platform[1], CL_DEVICE_TYPE_GPU, 1, &Device_ID, NULL); // cp_platform[1] = Nvidia GPU
    context = clCreateContext(NULL, 1, &Device_ID, NULL, NULL, &error); // creating openCL context 
    queue = clCreateCommandQueue(context, Device_ID, 0, &error); // creating command queue, executing openCL context on device cp_Platform[1] 

    globalSize = ceil(n / (float)localSize)*localSize;
    d_a = clCreateBuffer(context, CL_MEM_READ_ONLY, n * sizeof(float), NULL, NULL);
    d_b = clCreateBuffer(context, CL_MEM_READ_ONLY, n * sizeof(float), NULL, NULL);
    error = clEnqueueWriteBuffer(queue, d_a, CL_TRUE, 0, n * sizeof(float), h_a, 0, NULL, NULL); //Enqueue commands to write to a buffer object from host memory.
    error |= clEnqueueWriteBuffer(queue, d_b, CL_TRUE, 0,n * sizeof(float), h_s, 0, NULL, NULL); //Enqueue commands to write to a buffer object from host memory.

    program = clCreateProgramWithSource(context, 1, (const char **)& kernelSource, (const size_t *)&source_size, &error); //this function creates a program object for this specific openCL context
    error = clBuildProgram(program, 0, NULL, NULL, NULL, NULL); //compiles and links a program executable from the program source

    kernel = clCreateKernel(program, "gpuSumfunc", &error); //creating kernel object 
    error = clGetKernelWorkGroupInfo(kernel, Device_ID, CL_KERNEL_WORK_GROUP_SIZE, sizeof(size_t), (void*)&workGroupSize, NULL);
    error = clGetKernelWorkGroupInfo(kernel, Device_ID, CL_KERNEL_PREFERRED_WORK_GROUP_SIZE_MULTIPLE, sizeof(size_t), (void*)&pWorkGroupSize, NULL);
    error = clGetDeviceInfo(Device_ID, CL_DEVICE_MAX_COMPUTE_UNITS, sizeof(NumOfCU), &NumOfCU, NULL);
    error |= clSetKernelArg(kernel, 0, sizeof(cl_mem), &d_a); //Used to set the argument value for a specific argument of a kernel.
    error |= clSetKernelArg(kernel, 1, sizeof(cl_mem), &d_b);
    error |= clSetKernelArg(kernel, 2, sizeof(int), &n);
    error |= clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &globalSize, &localSize, 0, NULL, NULL); // Enqueues a command to execute a kernel on a device.
    clFinish(queue);
    clEnqueueReadBuffer(queue, d_b, CL_TRUE, 0, n*sizeof(float) , h_b, 0, NULL, NULL); ////writing data from the device (d_b) to host(h_b)
    clock_t end = clock();
    for (i = 0; i < (n+localSize-1)/localSize; i++)
    {
        gpuSum += h_b[i];
        cpuSum = cpuSumfunc(vector, 64*(i+1));
    if ((gpuSum - cpuSum) > Tolerance)
        {
            printf("nfailed! for index:%d",i);
            printf("nCPU sum = %f", cpuSum);
            printf("nGPU sum = %fn", gpuSum);
        }
        else
        {
            printf("nPassed! for index:%d",i);
            printf("nCPU sum: %.2f", cpuSum);
            printf("nGPU sum: %.2fn", gpuSum);
        }
    }

    // cpu

    time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
    //printf("nTotal program's running time is: %.2fn", time_spent);
    free(h_a);
    free(h_b);
    free(h_s);
    free(vector);
    //free(kernelSource);
    clReleaseProgram(program);
    clReleaseContext(context);
    clReleaseKernel(kernel);
    clReleaseCommandQueue(queue);
}
float cpuSumfunc(float * vec, int n)
{
    float sum = 0;
    int i;
    for (i = 0; i < n; i++)
    {
        sum += vec[i];
    }
    return sum;
}