Accelerate eig with GPUs

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Niklas
Niklas 2018-10-16
评论： Matt J 2019-7-16
Hi all, I need to diagonalize a lot of matrices. The problem is similar to:
A = rand(5000, 5000, 500); %this snipped is just a demo. It is real logic in the program
EVs = zeros(5000, 500);
for idx = 1:500
EVs(:, idx) = eig(A(:,:,idx));
end
This is fine on CPUs and easily scalable with parfor and MDCS. As eig is faster on GPUs I tried this
A = rand(5000, 5000, 500); %this snipped is just a demo. It is real logic in the program
EVs = zeros(5000, 500, 'gpuArray');
for idx = 1:500
B = gpuArray(A(:, :, idx));
EVs(:, idx) = eig(B);
end
EVs = gather(EVs);
This does not lead to a much better performance. Is there a way to get around the gpuArray statement in each loop? Some kind of pagefun with eig would be the solution I guess. (unfortunately, eig is not supported by pagefun)
Best wishes Niklas
  1 个评论
Matt J
Matt J 2019-7-16
Birk Andreas's comment moved here:
Please Mathworks, implement eig for use with pagefun as soon as possible!!!

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Matt J
Matt J 2018-10-16
编辑：Matt J 2018-10-16
You need to build A directly on the GPU, for example,
EVs = zeros(5000, 500, 'gpuArray');
A=gpuArray.rand(5000,5000,500);
for idx = 1:500
B = A(:, :, idx);
EVs(:, idx) = eig(B);
end
EVs = gather(EVs);
For the case of your real A, you have to examine what operations you are currently using to build A on the host, and which of those operations would not also be available on the GPU.
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Niklas
Niklas 2018-10-16
编辑：Niklas 2018-10-16
Using bigger matrices the speedup is higher.
Elapsed time is 1006.431223 seconds. <- CPU
Elapsed time is 295.168202 seconds. <- GPU
Unfortunately, nvidia-smi shows a low usage of the GPU. Maybe I will write a CUDA snipped to deal with it.
Matt J
Matt J 2018-10-16
Yeah, I can't see that there would be a lot of parallelism in eigenvalue computation.

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