Fastest large SVD computation in multithreaded machine?
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I have a waveform optimization problem that uses a large SVD in an interative loop. For instance, with my current settings, the matrix is 18800x18937, or roughly 356M elements (complex, either double or single, been experimenting). I have a machine with enough RAM (128 GB) to hold the entire thing; for other portions of the code I get some help using parpool('threads') and a parfor. Given the matrix sizes, I don't see a process pool as an option for other portions of the code.
Watching the utilization, it appears it uses about 6-10 threads for the native SVD, so it's partly parallel; I don't know if this is a Linux processor counting problem because the parpool only generated 10 workers despite it having 20 CPU cores. SPMD and distributed arrays seem to be only for process pools; with threads I don't need to distribute the array in memory.
Any tips on what to try, or is it going as fast as possible already? RdTildeRoot is 18800x18800 and computed once, Z is updated each iteration. From profiling, the SVD on this one line is 90% of the execution time.
[Ubar, ~, Utilde] = svd(RdTildeRoot * Z, 'econ');
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Benjamin Thompson
2022년 1월 25일
If you have the parallel computing toolbox and a good NVIDIA GPU, try using gpu Arrays. Try svds if you don't need all the singular values.
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