microsoft / microsoft/foldingdiff
How to use multithreading to speed up KL calculations？
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- Dominant language
- Jupyter Notebook
- Stars
- 568
- Forks
- 74
- Avg merge
- 20h 2m
- Merged PRs (30d)
- 1
Description
I found that when I was doing KL calculation, only one thread was calculating, which is currently taking up a lot of my time. How can I solve this problem?
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 identifies KL calculation as the slow entry point but names no file, test, or implementation location. Start by locating the KL calculation and profiling its threading behavior; done would require a concrete, validated way to use more than one thread and evidence that the calculation is faster.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100