[FEA] Hierarchical KMeans: Abstract Away Common Implementation Details Into Helpers
Nobody has claimed this yet.
- Dominant language
- Cuda
- Stars
- 854
- Forks
- 236
- Avg merge
- 3d 3h
- Merged PRs (30d)
- 62
Description
kmeans_balanced.cuh and kmeans.cuh have some commonalities that can be abstracted away into helpers and reused. For example, the minClusterAndDistanceCompute function could likely be reused rather than calling the fused NN + Reduction from predict_core in kmeans_balanced.cuh
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 comparing kmeans_balanced.cuh and kmeans.cuh, focusing on minClusterAndDistanceCompute and the fused nearest-neighbor and reduction path in predict_core. Identify which implementation details are shared and can be moved into helpers without changing either algorithm's behavior. Done means the common logic is reused by both implementations and existing behavior remains intact.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100