ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866910006536830976 |
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| author | Pan, Haolin Huang, Lianghong Dong, Jinyuan Xing, Mingjie Wu, Yanjun |
| author_facet | Pan, Haolin Huang, Lianghong Dong, Jinyuan Xing, Mingjie Wu, Yanjun |
| contents | 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 suffer from superficial pattern matching and causal opacity. In this paper, we introduce ECCO, a framework that bridges interpretable reasoning with combinatorial search. We first propose a reverse engineering methodology to construct a Chain-of-Thought dataset, explicitly mapping static code features to verifiable performance evidence. This enables the model to learn the causal logic governing optimization decisions rather than merely imitating sequences. Leveraging this interpretable prior, we design a collaborative inference mechanism where the LLM functions as a strategist, defining optimization intents that dynamically guide the mutation operations of a genetic algorithm. Experimental results on seven datasets demonstrate that ECCO significantly outperforms the LLVM opt -O3 baseline, achieving an average 24.44% reduction in cycles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00087 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization Pan, Haolin Huang, Lianghong Dong, Jinyuan Xing, Mingjie Wu, Yanjun Machine Learning Artificial Intelligence Performance Programming Languages 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 suffer from superficial pattern matching and causal opacity. In this paper, we introduce ECCO, a framework that bridges interpretable reasoning with combinatorial search. We first propose a reverse engineering methodology to construct a Chain-of-Thought dataset, explicitly mapping static code features to verifiable performance evidence. This enables the model to learn the causal logic governing optimization decisions rather than merely imitating sequences. Leveraging this interpretable prior, we design a collaborative inference mechanism where the LLM functions as a strategist, defining optimization intents that dynamically guide the mutation operations of a genetic algorithm. Experimental results on seven datasets demonstrate that ECCO significantly outperforms the LLVM opt -O3 baseline, achieving an average 24.44% reduction in cycles. |
| title | ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization |
| topic | Machine Learning Artificial Intelligence Performance Programming Languages |
| url | https://arxiv.org/abs/2602.00087 |