scverse / scverse/fast-array-utils
benchmarks: benchmark dask
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component: benchmark
type: dask
- Dominant language
- Python
- Stars
- 15
- Forks
- 5
- Avg merge
- 10h 33m
- Merged PRs (30d)
- 9
Description
See https://github.com/scverse/fast-array-utils/pull/30#discussion_r1965881913
We could create random dask arrays something like this:
import dask.array as da
arr = da.zeros(shape, dtype=dtype, chunks=_half_chunk_size(shape))
return arr.map_blocks(
lambda x: self.random(x.shape, dtype=x.dtype, gen=gen), dtype=dtype
)
cc @ilan-gold
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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 with the linked pull request discussion to understand the requested benchmark and inspect the repository for its existing benchmark entry point. Add coverage for randomly generated Dask arrays using the shape, dtype, chunks, and mapping approach shown in the issue; done means the Dask benchmark runs alongside the existing benchmarks.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, performance
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100