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GPU backslash performance much slower than CPU

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Meme Young
Meme Young on 27 Dec 2020
Edited: Joss Knight on 10 Jan 2021
I am doing numerical power flow caclulation by modifying the functions of matpower, an open source toolbox. By modifying its function newtonpf.m, GPU computation can be implemented. However, I found that GPU performance is much much slower than CPU. When calculating the built-in case3012wp of matpower, the matrix in newtonpf.m will be :
A: 5725 * 5725 sparse double, b: 5725 * 1 double.
The process of A \ b in the 1st iteration of newtonpf() will generally take around 0.01 sec on my i7-10750H + RTX 2070super MSI-GL65.
But if A and b are changed into GPU arrays, the process of A \ b will take the following time if A is the following types:
full double, 0.8 sec
sparse double, 4 sec
full single, 0.1 sec
(sparse single is not supported)
So why is the diference in performance? I thought GPU could do things much faster than CPU.
Files are attached as follows. Atest is sparse and Agpu is a sparse gpu array. All are doubles.
Matt J
Matt J on 27 Dec 2020
Please attach all the variables in a single .mat file, to make the download more convenient.

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Answers (1)

Matt J
Matt J on 27 Dec 2020
This thread looks relevant. It appears that sparse mldivide on the GPU is not expected to be faster.
Joss Knight
Joss Knight on 10 Jan 2021
Yes, PCG, GMRES, CGS, LSQR, QMR, TFQMR, BICG, BICGSTAB. Try them all, play with tolerance, iterations and preconditioning - something is likely to work. I'm not an expert in this field but this is what the sparse community tend to do.

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