9 papers
Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search
Haolin Pan, Lianghong Huang, Xvlin Zhou +2
Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurement cost with learned cost m…
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…
A Hybrid, Knowledge-Guided Evolutionary Framework for Personalized Compiler Auto-Tuning
Haolin Pan, Hongbin Zhang, Mingjie Xing +1
Compiler pass auto-tuning is critical for enhancing software performance, yet finding the optimal pass sequence for a specific program is an NP-hard problem. Traditional, general-p…
Synergy-Guided Compiler Auto-Tuning of Nested LLVM Pass Pipelines
Haolin Pan, Jinyuan Dong, Mingjie Xing +1
Compiler optimization relies on sequences of passes to improve program performance. Selecting and ordering these passes automatically, known as compiler auto-tuning, is challenging…
GRACE: Cluster-Specific Sequence Reuse for Compiler Auto-Tuning
Haolin Pan, Chao Zha, Jinyuan Dong +2
Compiler auto-tuning aims to improve optimization quality beyond fixed compiler heuristics, but existing approaches often face a trade-off between effectiveness and deployability.…
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.…