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CompilerGym: robust, performant compiler optimization environments for AI research, (CGO2022)
- Abstract: Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast iteration and development of ideas, and getting started requires a significant engineering investment. What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can ser...
- Labels: static analysis, program optimization, benchmark
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Language Models for Code Optimization: Survey, Challenges and Future Directions, (arXiv2025)
- Abstract: Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion, and repair. This has paved the way for the emergence of LM-based code optimization techniques, which are crucial for enhancing the performance of existing programs, such as accelerating program execution time. However, a comprehensive survey dedicated to this specific application has been lacking. To fill this gap, w...
- Labels: static analysis, program optimization, survey
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Meta large language model compiler: Foundation models of compiler optimization, (Meta2024)
- Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of software engineering and coding tasks. However, their application in the domain of code and compiler optimization remains underexplored. Training LLMs is resource-intensive, requiring substantial GPU hours and extensive data collection, which can be prohibitive. To address this gap, we introduce Meta Large Language Model Compiler (LLM Compiler), a suite of robust, openly available, pre-trained models speci...
- Labels: static analysis, program optimization, code model, code model training, IR code model
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Programl: A graph-based program representation for data flow analysis and compiler optimizations, (ICML2021)
- Abstract: Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of the data flow analyses that are critical to making good optimization decisions. We posit that if ML cannot do that, then it is insufficiently able to reason about programs. We formulate data flow analyses as supervised learning tasks and introduce a large open dataset of programs and their corresponding labels from several analys...
- Labels: static analysis, data-flow analysis, program optimization, code model, code model training, IR code model