Performance issue with transposing in dmdexactevaluate

[nbren12]

I hope you don't mind these issues I am opening up. The exact DMD function is pretty slow for me because my data is about 15000 samples with 2500 features. I did some profiling and the lines involving transpose are the most expensive steps. I think julia arrays are memory-contiguous along the columns, so one solution would be to assume that the data is already given with time as the second index. Most datasets I have worked with are stored on disk in this way anyway.
I am not sure what the best choice is in terms of memory layout for the optimized DMD solvers though.

[askhamwhat]

No worries, glad someone's using it!

I think it's worth looking for more efficiencies in that code, especially re: larger matrices. One big thing would be to replace the SVD call in it by a randomized SVD call, a la Halko-Martinsson-Tropp.

[nbren12]

Yah. In that commit I just pushed to my fork. I switch the arpack iterative svd solver. Which was about 2x faster for my data.

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On Wed, May 16, 2018 at 12:58 PM Travis Askham ***@***.***> wrote: No worries, glad someone's using it! I think it's worth looking for more efficiencies in that code, especially re: larger matrices. One big thing would be to replace the SVD call in it by a randomized SVD call, a la Halko-Martinsson-Tropp. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#2 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABUokiFBBtnc6pDGtkOexnCtg1-qC5iYks5tzITfgaJpZM4UBvVR> .