Open
Conversation
I have added a few sparse benchmarks to NPBench: - Banded Matrix-Matrix-Transposed multiplication (A * B * A^T). NumPy already had an implementation for banded matrix multiplication, but it required the data to be in dense format (2D numpy arrays). - Conjugate Gradient Method for CSR matrices. - BiConjugate Gradient Method for CSR matrices. - BiConjugate Stabilized Gradient Method for CSR matrices. - Minimum Residual Method for CSR matrices. - Generalized Minimum Residual Method for CSR matrices. - CSR Matrix-Matrix multiplication.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
I have added a few sparse benchmarks to NPBench: