LOOPer
A learned automatic code optimizer for polyhedral compilers
LOOPer: A Learned Automatic Code Optimizer for Polyhedral Compilers
A learned cost-model-driven auto-scheduler for polyhedral compilers, achieving a median 1.4× speedup over Pluto on Polybench.
Improved the existing deep learning cost model accuracy by 5% through feature engineering; expanded the optimization space via a mathematical representation of code transformations.
- Role: Research Assistant at NYU Abu Dhabi
- Methods: Polyhedral compilation, deep learning cost models, feature engineering
- Status: Published, PACT 2025
- Paper: arXiv:2403.11522