Lucene: Improve CAGRA_HNSW search latency via better graph quality
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- Dominant language
- Cuda
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Description
Current logic only allows 1 to 3 graph layers when converting from CAGRA to HNSW which results in less "navigable" HNSW graphs that take longer to search.
PR: https://github.com/rapidsai/cuvs-lucene/pull/140
In the attached plot, we can see that some datasets (such as the one seen here) appear to demand higher hnsw-layers in order to achieve the natural search-latency as seen in the natural HNSW benchmark runs
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 PR #140 and the CAGRA-to-HNSW conversion logic described in the issue, then review the attached latency plot and compare the affected search behavior with the natural HNSW benchmarks. Done means the converted graphs provide better navigability and improved search latency on datasets that require more HNSW layers.
Written by the indexing model from the issue text.
Assessment
- Domain
- performance, search
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100