GPU backslash performance much slower than CPU
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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.
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Matt J
2020-12-27
This thread looks relevant. It appears that sparse mldivide on the GPU is not expected to be faster.
13 个评论
Joss Knight
2021-1-10
编辑:Joss Knight
2021-1-10
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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