c – CUDA：嵌入式循环内核

概述我有一些代码,我想进入一个cuda内核.看吧： 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 我有一些代码,我想进入一个cuda内核.看吧：

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？

解决方法 假设你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).您将无法像上面那样使用=.你可以把它全部放在一个内核中,如果你在每个块(行)中只有一个线程做总和和意思,就像这样……

__global__ both_kernel(Y,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,这意味着…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;}

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