Improve indexing performance benchmarks
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topic-performance
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- Python
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Description
As discussed in #3375 - FYI @jhamman
asv_bench/benchmarks/indexing.py is currently missing some key use cases:
- All tests in the above module use arrays with 2~6 million points.
While this is important to spot any case where the numpy underlying functions start being unnecessarily called more than once, it also means any performance improvement or degradation in any of the pure-Python code will be completely drowned out.
All tests should be run twice, once with the currentnx = 3000; ny = 2000; nt = 1000and again withnx = 15; ny = 10; nt = 5. - DataArray slicing (sel, isel, and square brackets)
- Slicing when there are no IndexVariables (verify that we're not creating dummy variables, doing a full scan on them, and then discarding them)
- other?
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 with asv_bench/benchmarks/indexing.py and inspect the existing cases and their nx, ny, and nt sizes. Run the current indexing benchmarks, then cover both large and small arrays, DataArray sel/isel/bracket slicing, and slicing without IndexVariables. Done means these use cases are represented without losing the existing large-array coverage.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python
- Domain
- performance, testing-qa
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100