Hongxu Xu

Zhenyang Xu, Hongxu Xu, Yongqiang Tian et al.

Peephole optimization is an essential class of compiler optimizations that targets small, inefficient instruction sequences within programs. By replacing such suboptimal instructions with refined and more optimal sequences, these optimizations not only directly optimize code size and performance, but also enable more transformations in the subsequent optimization pipeline. Despite their importance, discovering new and effective peephole optimizations remains challenging due to the complexity and breadth of instruction sets. Prior approaches either lack scalability or have significant restrictions on the peephole optimizations that they can find. This paper introduces LPO, a novel automated framework to discover missed peephole optimizations. Our key insight is that, Large Language Models (LLMs) are effective at creative exploration but susceptible to hallucinations; conversely, formal verification techniques provide rigorous guarantees but struggle with creative discovery. By synergistically combining the strengths of LLMs and formal verifiers in a closed-loop feedback mechanism, LPO can effectively discover verified peephole optimizations that were previously missed. We comprehensively evaluated LPO within LLVM ecosystems. Our evaluation shows that LPO can successfully identify up to 22 out of 25 previously reported missed optimizations in LLVM. In contrast, the recently proposed superoptimizers for LLVM, Souper and Minotaur detected 15 and 3 of them, respectively. More importantly, within eleven months of development and intermittent testing, LPO found 62 missed peephole optimizations, of which 28 were confirmed and an additional 13 had already been fixed in LLVM. These results demonstrate LPO's strong potential to continuously uncover new optimizations as LLMs' reasoning improves.

Chunhao Liao, Hongxu Xu, Xintong Zhou et al.

Peephole optimizations are a core component of modern optimizing compilers. It rewrites specific instruction into semantically equivalent but more efficient forms. In practice, creating a new peephole optimization often starts from a concrete optimization instance and requires lifting it into a more general rewrite rule that matches a wider range of instruction patterns. This generalization step is critical to optimization effectiveness, but it is also difficult: producing rules that are both correct and sufficiently general typically demands substantial manual effort and domain expertise. Existing approaches such as Hydra attempt to automate this task with program synthesis, but their generalization capability is often limited by search-space explosion, under-generalization, and restricted support for diverse instruction domains. We present LPG, large language model aided peephole optimization generalization, a framework that uses large language models (LLMs) to generalize peephole optimizations. The design of LPG is motivated by the observation that LLMs are effective at semantic abstraction and exploratory reasoning, while formal analyses are necessary to ensure that generated rules are sound and profitable. Based on this observation, LPG adopts a closed-loop workflow that integrates LLM-driven symbolic constant generalization, structural generalization, constraint relaxation, and bitwidth/precision generalization with feedback from syntactic validation, semantic verification, and profitability checking. We evaluate LPG on real-world peephole optimization issues drawn from the LLVM ecosystem. Overall, LPG successfully generalizes 90 out of 102 optimizations. On the integer-focused subset that is directly comparable to Hydra, LPG generalizes 74 out of 81 optimizations, whereas Hydra generalizes 35.