How to speed up convolution with a million data points
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I am currently doing convolution using nested for loops, for 10^6 data points in each for loop. Are there ways to speed up the following code? Thanks in advance!
% nIters = 40;
% n = 1e6;
% mzL = rand(nIters, n);
% gg = rand(1, n);
mzR_temp = zeros(nIters, 1);
for c = 1:n
mzR_temp(:) = 0;
for d = 1:c
mzR_temp(:) = mzR_temp(:) + gg(c-d+1) * mzL(:,d);
end
mzR_II(:,c) = mzR_temp;
end
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Accepted Answer
Matt J
on 3 May 2024
Edited: Matt J
on 3 May 2024
Use conv,
mzR_II=conv(gg,mzL,'same');
or FFTs,
mzR_II=ifft( fft(gg,2*n) .* fft(mzL,2*n) , 'symmetric');
mzR_II=mzR_II(1:n);
4 Comments
Matt J
on 3 May 2024
Edited: Matt J
on 3 May 2024
There is absolutely no way the computation should take more than 1 second on any computer made within the last 10 years.
n=1e6;
mzL = rand(1,n);
gg = rand(1,n-1);
tic
mzR_II = [0 fftfilt(mzL,gg)];
toc
tic;
mzR_II=ifft( fft(gg,2*n) .* fft(mzL,2*n) , 'symmetric');
mzR_II=mzR_II(1:n);
toc
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