networkx / networkx/nx-parallel
Implement adaptive should_run policies based on graph characteristics to optimize parallel execution
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 71
- Forks
- 37
- PR merge metrics
- No merged PRs in 30d
Description
Problem
Currently, nx-parallel doesn't intelligently decide when parallelization is beneficial. This results in:
Wasted computational resources when parallel overhead exceeds any potential speedup
Suboptimal performance on small or dense graphs where sequential execution would be faster
Inconsistent user experience as speedup varies unpredictably across different graph types
Proposed Solution
Implement algorithm-specific should_run policies that dynamically determine whether to use parallelization based on graph characteristics and algorithm-specific heuristics.
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
The issue names no files, tests, or specific algorithm entry points. Start by surveying nx-parallel's algorithm entry points and its joblib-based parallel execution, then define how graph characteristics should select sequential or parallel execution. Done means algorithm-specific policies are implemented and their decisions are covered by tests across representative graph types.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100