reliance on LLM judgment for optimization prioritization without clear metrics
Open
Nobody has claimed this yet.
- Dominant language
- C
- Stars
- 19
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
how well can LLMs prioritize issues in the optimization remarks?
Attempt a simple brute-force or search-based optimization (or use an existing autotuning tool) to see if CompilerGPT finds better solutions faster.
Contributor guide
No contributing guide indexed for this repository
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 does not name any files, tests, or entry points. First define clear optimization-quality and speed metrics, then compare CompilerGPT's prioritization with a brute-force, search-based, or existing autotuning approach; the work is done when the comparison shows whether it finds better solutions faster.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- c, machine-learning
- Domain
- ai, compilers, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100