Question

我有一些代码要进入cuda内核。看哪：

    for (r = Y; r < Y + H; r+=2)
    {
        ch1RowSum = ch2RowSum = ch3RowSum = 0;
        for (c = X; c < X + W; c+=2)
        {
            chan1Value = //some calc'd value
                            chan3Value = //some calc'd value
            chan2Value = //some calc'd value
            ch2RowSum  += chan2Value;
            ch3RowSum  += chan3Value;
            ch1RowSum  += chan1Value;
        }
        ch1Mean += ch1RowSum / W;
        ch2Mean += ch2RowSum / W;
        ch3Mean += ch3RowSum / W;
    }

这应该分成两个内核，一个用于计算RowSums，另一个用于计算均值，我应该如何处理我的循环索引从零开始并在N结束的事实？

Answer 1

假设您有一个计算三个值的内核。配置中的每个线程将计算每个（r，c）对的三个值。

__global__ value_kernel(Y, H, X, W)
{
    r = blockIdx.x + Y;
    c = threadIdx.x + W;

    chan1value = ...
    chan2value = ...
    chan3value = ...
}

我不相信你可以在上面的内核中计算总和（至少完全平行）。你将不能像上面那样使用+ =。你可以把它全部放在一个内核中，如果你在每个块（行）中只有一个线程做和，就像这样......

__global__ both_kernel(Y, H, X, W)
{
    r = blockIdx.x + Y;
    c = threadIdx.x + W;

    chan1value = ...
    chan2value = ...
    chan3value = ...

    if(threadIdx.x == 0)
    {
        ch1RowSum = 0;
        ch2RowSum = 0;
        ch3RowSum = 0;

        for(i=0; i<blockDim.x; i++)
        {
            ch1RowSum += chan1value;
            ch2RowSum += chan2value;
            ch3RowSum += chan3value;
        }

        ch1Mean = ch1RowSum / blockDim.x;
        ch2Mean = ch2RowSum / blockDim.x;
        ch3Mean = ch3RowSum / blockDim.x;
    }
}

但是最好使用第一个值内核，然后使用第二个内核来获得总和和平均值...可以进一步并行化下面的内核，如果它是独立的，你可以在准备好时专注于它。

__global__ sum_kernel(Y,W)
{
    r = blockIdx.x + Y;

    ch1RowSum = 0;
    ch2RowSum = 0;
    ch3RowSum = 0;

    for(i=0; i<W; i++)
    {
        ch1RowSum += chan1value;
        ch2RowSum += chan2value;
        ch3RowSum += chan3value;
    }

    ch1Mean = ch1RowSum / W;
    ch2Mean = ch2RowSum / W;
    ch3Mean = ch3RowSum / W;
}

CUDA：嵌入式循环内核

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