[FEA] Improve perf of batched all_neighbors
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- Dominant language
- Cuda
- Stars
- 854
- Forks
- 236
- Avg merge
- 3d 3h
- Merged PRs (30d)
- 62
Description
For large datasets, batched all_neighbors takes a long time to preprocess the data into clusters.
Currently we are running a brute-force search to assign data points to its nearest cluster centroids.
This process can be optimized by using a non brute-force approach.
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
Read cpp/src/neighbors/all_neighbors/all_neighbors_batched.cuh around lines 101-148, where batched all_neighbors preprocesses data into clusters and assigns points to their nearest centroids. First understand the current brute-force assignment and measure preprocessing on a large dataset. Done means the assignment uses a non-brute-force approach and batched all_neighbors preprocessing is measurably faster.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance, search
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100