SVDDC
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
Research direction
Start by reproducing the reported SVD and matrix-multiplication timings for the [13824,273] matrix, comparing the svd and svddc routines and the ndarray dot operation with NumPy. Read the svddc API to determine valid uvt_flag values. Done means explaining the performance gap and clarifying whether the timings are expected or indicate a problem.
Written by the indexing model from the issue text.
Description
Hi,
I am trying to compute the SVD of a rather large matrix [13824,273] , the svd routine takes about 260s to perform the decomposition , compare to 7s with the svd routine from numpy linalg module.
And I cannot figure out how to use the svddc routine instead of the svd routine, it is not clear what values uvt_flag should be given.
Also I have noticed that matrix multiplication is very slow with ndarray dot routine compared again to numpy, for example , after the SVD if I do the matrix multiplications USVT with ndarray it takes 160s and with numpy it takes 25ms.
Is these timings expected for ndarray-linalg at this stage of development or did I do something wrong and I should expect the same speed than numpy?
Thanks
- Dominant language
- Rust
- Stars
- 452
- Forks
- 95
- PR merge metrics
- No merged PRs in 30d
Contributor guide
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First steps
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