Performance regression in 0.2
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Description
I was pip updating from **0.1.3** to **0.2**. Two sample workloads of us took a significant hit in performance: Reducing `480x13500` to `80x13500` ran `2:24` instead of `1:14` and reducing `480x6700` to `80x6700` took `1:49` instead of `0:28`.
Alongside updating umap-learn, other libraries got a bump (llvmlite 0.2 to 0.21, numba 0.35.0 to 0.36.2). Neither of those affected running times. After downgrading to **0.1.3**, I got the former numbers.
I saw that [this commit](https://github.com/lmcinnes/umap/commit/a8617e8f9bf37e9027cc8093a42844b0680c1c5d#diff-4b3afb0d9643c1cae4537938d420e927) disabled jitting for `fuzzy_simplical_set`. Could this or anything else cause this regression?
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 by inspecting the linked commit, especially the change involving fuzzy_simplical_set, then compare the 0.1.3 and 0.2 implementations and reproduce the two workloads described in the issue. Use the reported timings as the baseline; done means identifying the regression and restoring comparable performance without breaking the affected computation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100