Performance: Investigate `pp.scale` with sparse matrices
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Area – Performance 🐌
- Dominant language
- Python
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- Avg merge
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- Merged PRs (30d)
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Description
With the new numba kernel for sparse scaling, we need to figure out how and when the numba code becomes faster than the array code. This might lead to numba completly replaceing the array operations for sparse matrices.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the pp.scale implementation and inspect the new numba kernel for sparse scaling alongside the existing array operations. Benchmark both paths across representative sparse-matrix sizes and identify when numba becomes faster. Done means documenting the performance crossover and whether numba should replace the array operations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100