# A Survey on Compiler Autotuning using Machine Learning

arXiv: https://arxiv.org/abs/1801.04405 · HTML: https://mlsanity.com/paper/1801.04405/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2018.

## Built on by (most cited first, 30 of 40)

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- [ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales](https://mlsanity.com/paper/2303.16245/) (2023)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
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- [Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition](https://mlsanity.com/paper/2410.07574/) (2024)
- [Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning](https://mlsanity.com/paper/2506.15701/) (2025)
- [Gap-Dependent Bounds for Federated Q-learning](https://mlsanity.com/paper/2502.02859/) (2025)
- [LS-CAT: A Large-Scale CUDA AutoTuning Dataset](https://mlsanity.com/paper/2103.14409/) (2021)
- [Generating GPU Compiler Heuristics using Reinforcement Learning](https://mlsanity.com/paper/2111.12055/) (2021)
- [Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines](https://mlsanity.com/paper/2208.05596/) (2022)
- [FasterPy: An LLM-based Code Execution Efficiency Optimization Framework](https://mlsanity.com/paper/2512.22827/) (2025)
- [Agentic Harness for Real-World Compilers](https://mlsanity.com/paper/2603.20075/) (2026)
- [Using Graph Neural Networks to model the performance of Deep Neural Networks](https://mlsanity.com/paper/2108.12489/) (2021)
- [Profile Guided Optimization without Profiles: A Machine Learning Approach](https://mlsanity.com/paper/2112.14679/) (2021)
- [Power Constrained Autotuning using Graph Neural Networks](https://mlsanity.com/paper/2302.11467/) (2023)
- [CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization](https://mlsanity.com/paper/2406.17811/) (2024)
- [FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs](https://mlsanity.com/paper/2412.20796/) (2024)
- [Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)](https://mlsanity.com/paper/2104.13242/) (2021)
- [Constraint-based Diversification of JOP Gadgets](https://mlsanity.com/paper/2111.09934/) (2021)
- [End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning](https://mlsanity.com/paper/2204.11981/) (2022)
- [MAGPIE: Machine Automated General Performance Improvement via Evolution of Software](https://mlsanity.com/paper/2208.02811/) (2022)
- [SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR](https://mlsanity.com/paper/2308.07654/) (2023)
- [Target-independent XLA optimization using Reinforcement Learning](https://mlsanity.com/paper/2308.14364/) (2023)
- [LoopTune: Optimizing Tensor Computations with Reinforcement Learning](https://mlsanity.com/paper/2309.01825/) (2023)
- [Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness](https://mlsanity.com/paper/2410.03210/) (2024)
- [Investigating Execution-Aware Language Models for Code Optimization](https://mlsanity.com/paper/2503.08228/) (2025)
- [Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning](https://mlsanity.com/paper/2509.26300/) (2025)

## Abstract (arXiv)

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.