VecTrans: LLM Transformation Framework for Better Auto-vectorization on High-performance CPU

no code implementations • 25 Mar 2025 • Zhongchun Zheng, Long Cheng, Lu Li, Rodrigo C. O. Rocha, Tianyi Liu, Wei Wei, Xianwei Zhang, Yaoqing Gao

Large language models (LLMs) have demonstrated great capabilities in code generation, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-specific reasoning.

Code Generation

ACPO: AI-Enabled Compiler Framework

no code implementations • 15 Dec 2023 • Amir H. Ashouri, Muhammad Asif Manzoor, Duc Minh Vu, Raymond Zhang, Colin Toft, Ziwen Wang, Angel Zhang, Bryan Chan, Tomasz S. Czajkowski, Yaoqing Gao

Furthermore, by including both Function Inlining and Loop Unroll models, ACPO can provide a combined speedup of 4. 5% on Polybench and 2. 4% on Cbench when compared with LLVM's O3, respectively.

MLGOPerf: An ML Guided Inliner to Optimize Performance

no code implementations • 18 Jul 2022 • Amir H. Ashouri, Mostafa Elhoushi, Yuzhe Hua, Xiang Wang, Muhammad Asif Manzoor, Bryan Chan, Yaoqing Gao

This paper presents MLGOPerf; the first end-to-end framework capable of optimizing performance using LLVM's ML-Inliner.

reinforcement-learning Reinforcement Learning +1