Реализация функции gpuIncreaseOne в CUDA

Я пытаюсь выполнить основные операции + с CUDA для вычислений на GPU. Функция vectorIncreaseOne это экземпляр для деталей операции и gpuIncreaseOne функция — это структура для применения операции к каждому элементу параметра. data_for_calculation.

Экспериментальная реализация

Экспериментальная реализация gpuIncreaseOne функция как показано ниже.

#include <stdio.h>
#include <cuda_runtime.h>
#include <cuda.h>
#include <helper_cuda.h>
#include <math.h>

__global__ void CUDACalculation::vectorIncreaseOne(const long double* input, long double* output, int numElements)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < numElements)
    {
        if (input[i] < 255)
        {
            output[i] = input[i] + 1;
        }
    }
}

int CUDACalculation::gpuIncreaseOne(float* data_for_calculation, int size)
{
    // Error code to check return values for CUDA calls
    cudaError_t err = cudaSuccess;

    // Print the vector length to be used, and compute its size
    int numElements = size;
    size_t DataSize = numElements * sizeof(float);

    // Allocate the device input vector A
    float *d_A = NULL;
    err = cudaMalloc((void **)&d_A, DataSize);
    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector A (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Allocate the device input vector B
    float *d_B = NULL;
    err = cudaMalloc((void **)&d_B, DataSize);
    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector B (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Allocate the device output vector C
    float *d_C = NULL;
    err = cudaMalloc((void **)&d_C, DataSize);
    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to allocate device vector C (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Copy the host input vectors A and B in host memory to the device input vectors in
    // device memory
    err = cudaMemcpy(d_A, data_for_calculation, DataSize, cudaMemcpyHostToDevice);

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to copy vector A from host to device (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Launch the Vector Add CUDA Kernel
    int threadsPerBlock = 256;
    int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;
    printf("CUDA kernel launch with %d blocks of %d threadsn", blocksPerGrid, threadsPerBlock);
    vectorIncreaseOne <<<blocksPerGrid, threadsPerBlock>>>(d_A, d_C, numElements);

    err = cudaGetLastError();

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Copy the device result vector in device memory to the host result vector
    // in host memory.
    err = cudaMemcpy(data_for_calculation, d_C, DataSize, cudaMemcpyDeviceToHost);

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Free device global memory
    err = cudaFree(d_A);

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector A (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    err = cudaFree(d_B);

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector B (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    err = cudaFree(d_C);

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to free device vector C (error code %s)!n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }
    return 0;
}

Тестовые кейсы

Тестовый пример для gpuIncreaseOne функция как показано ниже.

auto data_pointer = (float*)malloc(100 * sizeof(float));
for (int i = 0; i < 100; i++)
{
    data_pointer[i] = static_cast<float>(1);
}
CUDACalculation::gpuIncreaseOne(data_pointer, 100);


free(data_pointer);

Все предложения приветствуются.

Если есть какие-либо улучшения по поводу:

• Возможный недостаток или ненужные накладные расходы
• Обработка ошибок

пожалуйста, дайте мне знать.