How to speedup mean and std calculation on GPU?
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Hello everyone, I am looking a way to speed up mean and std calculation on GPU. I run this code and it does take quite some time to complete, compared to the one if I do not use gpuArray. Maybe somebody would have any idea?
g_p is gpuArray with matrix of (1000000,5)
for q=1:n1-d
x2=g_p(d-w+q-1:d+q-2,:);
mean_x=mean(x2);
std_x=std(x2);
R = bsxfun(@minus,x2,mean_x);
x3=bsxfun(@rdivide,R,std_x)
end
///////////
or x3=arrayfun(@norm,x2)?
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Jan
2018 年 6 月 17 日
To calculate the standard deviation, the mean must be calculated again. Try to combine this:
x2 = g_p(d-w+q-1:d+q-2,:);
mean_x = sum(x2, 1) / w;
xc = x2 - mean_x; % Auto-expand: >= R2016b
% xc = bsxfun(@minus, x2, mean_x);
std_x = vecnorm(xc) / sqrt(s - 1); % vecnorm: >= R2017b
% std_x = sqrt(sum(xc .* xc, 1)) / sqrt(s - 1);
for the mean only the first and the last element changed between the iterations. Use this detail:
mean_x = sum(g_p(d-w:d-1, :) / w; % For q=1
for q = 1:n1-d
...
mean_x = mean_x - (g_p(d-w+q-1, :) + g_p(d+q-1, :)) / w;
end
3 件のコメント
Jan
2018 年 6 月 18 日
Without vecnorm you can use the line posted afterwards:
std_x = sqrt(sum(xc .* xc, 1)) / sqrt(s - 1);
I cannot test the code on a GPU. Maybe my suggestion give you at least an impression, of what could be tried to reduce the overhead.
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