# Machine Learning in Compiler Optimisation

arXiv: https://arxiv.org/abs/1805.03441 · HTML: https://mlsanity.com/paper/1805.03441/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 13 of 13)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [Regression Language Models for Code](https://mlsanity.com/paper/2509.26476/) (2025)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [Optimizing Sparse Matrix Multiplications for Graph Neural Networks](https://mlsanity.com/paper/2111.00352/) (2021)
- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [Automatic Cause Detection of Performance Problems in Web Applications](https://mlsanity.com/paper/2103.04954/) (2021)
- [Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework](https://mlsanity.com/paper/2201.13305/) (2022)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [BenchDirect: A Directed Language Model for Compiler Benchmarks](https://mlsanity.com/paper/2303.01557/) (2023)
- [Machine Learning-Driven Adaptive OpenMP For Portable Performance on Heterogeneous Systems](https://mlsanity.com/paper/2303.08873/) (2023)
- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)

## Abstract (arXiv)

In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity. In this article, we describe the relationship between machine learning and compiler optimisation and introduce the main concepts of features, models, training and deployment. We then provide a comprehensive survey and provide a road map for the wide variety of different research areas. We conclude with a discussion on open issues in the area and potential research directions. This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements.