Investigate discrepancy between non-BLAS and BLAS versions of `linfa-linear`

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#277 9 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Bug
Clarity
Needs clarification
Activity status
Stale
Tech stack
rust
Domain
machine-learning, performance

Research direction

Start with the benchmark results linked in the issue comment and the BLAS and non-BLAS implementations used by linfa-linear. Profile OLS and GLM across the reported feature counts, then compare the measurements to identify and document the source of the performance discrepancy.

Written by the indexing model from the issue text.

Description

enhancement help wanted

According to the benchmark results from this comment, linfa-linear is faster with BLAS than without. The OLS algorithm isn't too different with 5 features and is only slightly slower with 10 features, but GLM is significantly slower without BLAS. We should profile and investigate the difference between the BLAS and non-BLAS performance.

Dominant language
Rust
Stars
4.7k
Forks
334
Avg merge
39m
Merged PRs (30d)
1

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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