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cs.LG2026
ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
Haolin Pan, Lianghong Huang, Jinyuan Dong +2
Compiler auto-tuning faces a dichotomy between traditional black-box search methods, which lack semantic guidance, and recent Large Language Model (LLM) approaches, which often suf…
cs.LG2025
Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
Haolin Pan, Jinyuan Dong, Hongbin Zhang +3
Learning effective numerical representations, or embeddings, of programs is a fundamental prerequisite for applying machine learning to automate and enhance compiler optimization.…
cs.LG2025
Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
Haolin Pan, Hongyu Lin, Haoran Luo +5
Compiler auto-tuning optimizes pass sequences to improve performance metrics such as Intermediate Representation (IR) instruction count. Although recent advances leveraging Large L…