How to efficiently allocate memory using a parfor loop
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Hello all, I have a quick optimization question.
I'm doing calculations on some very large point cloud data. The calculation I'm doing is
for n=1:size(E_mat,1)
Q_matrix(n,:,:) = sigmaE(n)/2/mass_density(n)*squeeze(E_mat(n,:,:))'*squeeze(E_mat(n,:,:));
end
where size(E_mat) ~70000000,3,24. This code should be super parallelizable but when I use parfor I get a memory issue. I have access to a good compute server with 40 cores and 512Gb of RAM. The current for loop utilizes about 300Gb of RAM but only 1.2% CPU. I'm pretty new to high performance computing but I'm pretty sure the for loop is running single threaded due to the low CPU usage. Is there a simple way to fix this?
Thanks so much for the help!!
4 Comments
Walter Roberson
on 28 Jun 2022
squeeze is fast. It is extracting the data that is slow. The memory layout is
(1,1,1) (2,1,1) (3,1,1) (4,1,1)... (70000000,1,1), (1,2,1) (2,2,1)... (70000000, 2,1) and so on. The data for (n, :, :) is all over the place in memory. If you make 70000000 the final dimension then each 3x24 is stored in consecutive memory.
Accepted Answer
Jan
on 29 Jun 2022
Edited: Jan
on 29 Jun 2022
ET = permute(E_mat, [2,3,1]);
Q = zeros(size(ET));
parfor n = 1:size(E_mat, 3)
Q(:,:,n) = sigmaE(n) / 2 / mass_density(n) * ET(:, :, n)' * ET(:, :, n);
% Or maybe this is faster:
% tmp = ET(:, :, n);
% Q(:,:,n) = sigmaE(n) / 2 / mass_density(n) * tmp' * tmp;
end
I'm curious: What do you observe?
Du you really mean ctranspose or is ET real? Then .' would be the transposition.
ET = permute(E_mat, [2,3,1]);
Q = pagetimes(ET, 'transpose', ET, 'none');
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