Support cusolverDn<t>gesvdj and cusolverDn<t>gesvdaStridedBatched #3192
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anaruse
changed the title
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@toslunar Could you take a look? |
toslunar
reviewed
Mar 17, 2020
| status = cusolverDnXgesvdjGetResidual(<Handle>handle, <GesvdjInfo>info, | ||
| &residual) | ||
| check_status(status) | ||
| return residual |
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Fair to leave it untested because the functionality (to compute residual of the decomposition) does not seem available in NumPy or SciPy.
| cuda_version = runtime.runtimeGetVersion() | ||
| if version_added is not None and cuda_version < version_added: | ||
| return False | ||
| if version_removed is not None and cuda_version >= version_removed: |
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Agree to have version_removed to align with cupy/cusparse.py.
Lines 82 to 93 in 2d10549
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Thanks for your review. I fixed typos. |
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Jenkins, test this please. |
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Successfully created a job for commit f1df4a9: |
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Jenkins CI test (for commit f1df4a9, target branch master) succeeded! |
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This PR makes following two types of cuSolver SVD implementations available in CuPy.
gesvdj... https://docs.nvidia.com/cuda/cusolver/index.html#cuds-lt-t-gt-gesvdjgesvda... https://docs.nvidia.com/cuda/cusolver/index.html#cuds-lt-t-gt-gesvdagesvdjuses Jacobi method and can give better performance compared with standard gesvd using QR factorization when matrix size is not large.gesvdais approximate SVD solver and better in performance when a tall skinny matrix.This is related to #3174