Awesome machine learning for compilers and program optimisation

A curated list of awesome research papers, datasets, and tools for applying machine learning techniques to compilers and program optimisation.

Contents

• Papers
  • Survey
  • Iterative Compilation and Compiler Option Tuning
  • Instruction-level Optimisation
  • Parallelism Mapping and Task Scheduling
  • Languages and Compilation
  • Auto-tuning and Design Space Exploration
  • Code Size Reduction
  • Cost and Performance Models
  • Domain-specific Optimisation
  • Learning Program Representation
  • Enabling ML in Compilers and Systems Optimisation
  • Memory/Cache Modeling/Analysis
• Books
• Talks and Tutorials
• Software
• Benchmarks and Datasets
• Conferences
• Journals
• How to Contribute

Papers

Survey

• Machine Learning in Compiler Optimisation - Zheng Wang and Michael O'Boyle, Proceedings of the IEEE, 2018
• A survey on compiler autotuning using machine learning - Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, and Cristina Silvano, ACM Computing Surveys (CSUR), 2018
• A survey of machine learning for big code and naturalness - Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton, ACM Computing Surveys (CSUR), 2018
• A Taxonomy of ML for Systems Problems - Martin Maas, IEEE Micro, 2020
• The Deep Learning Compiler: A Comprehensive Survey - Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian, IEEE Transactions on Parallel and Distributed Systems, 2021

Iterative Compilation and Compiler Option Tuning

• SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations - Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, Scott Mahlke. CGO 2022.
• Iterative Compilation Optimization Based on Metric Learning and Collaborative Filtering - Hongzhi Liu, Jie Luo, Ying Li, Zhonghai Wu. ACM TACO 2022.
• Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 - Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon. arXiv 2021.
• Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models - RB Roy, T Patel, V Gadepally, D Tiwari. PLDI 2021.
• Efficient Compiler Autotuning via Bayesian Optimization - Junjie Chen, Ningxin Xu, Peiqi Chen, Hongyu Zhang. ICSE 2021.
• Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations - Jaehoon Koo, Prasanna Balaprakash, Michael Kruse, Xingfu Wu, Paul Hovland, Mary Hall. Arxiv.org, 2021.
• Improved basic block reordering - Andy Newell and Sergey Pupyrev. IEEE Transactions on Computers, 2020.
• Static Neural Compiler Optimization via Deep Reinforcement Learning - Rahim Mammadli, Ali Jannesari, Felix Wolf. LLVM HPC Workshop, 2020.
• Autotuning Search Space for Loop Transformations - Michael Kruse, Hal Finkel, Xingfu Wu. LLVM HPC Workshop, 2020.
• A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations - Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, and Stefano Doni, LCTES 2020.
• Autophase: Compiler phase-ordering for hls with deep reinforcement learning. Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek. MLSys 2020.
• FuncyTuner: Auto-tuning Scientific Applications With Per-loop Compilation - Tao Wang, Nikhil Jain, David Beckingsale, David Böhme, Frank Mueller, Todd Gamblin. ICPP 2019.
• Micomp: Mitigating the compiler phase-ordering problem using optimization sub-sequences and machine learning - Amir H. Ashouri, Andrea Bignoli, Gianluca Palermo, Cristina Silvano, Sameer Kulkarni, and John Cavazos. ACM Transactions on Architecture and Code Optimization (TACO) 2017.
• Iterative Schedule Optimization for Parallelization in the Polyhedron Model - Stefan Ganser, Armin Grösslinger, Norbert Siegmund, Sven Apel, and Christian Lengauer. ACM Transactions on Architecture and Code Optimization (TACO), 2017.
• Learning to superoptimize programs - Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H.S. Torr, Pushmeet Kohlim. ICLR 2017
• Continuous learning of compiler heuristics - Michele Tartara and Stefano Crespi Reghizzi. ACM Transactions on Architecture and Code Optimization (TACO), 2013.
• Mitigating the compiler optimization phase-ordering problem using machine learning - Sameer Kulkarni and John Cavazos. OOPSLA 2012.
• An evaluation of different modeling techniques for iterative compilation - Eunjung Park, Sameer Kulkarni, and John Cavazos. CASES 2011.
• Evaluating iterative optimization across 1000 datasets - Yang Chen, Yuanjie Huang, Lieven Eeckhout, Grigori Fursin, Liang Peng, Olivier Temam, and Chengyong Wu. PLDI 2010
• Iterative optimization in the polyhedral model: Part II, multidimensional time - Louis-Noël Pouchet, Cédric Bastoul, Albert Cohen, and John Cavazos. PLDI 2008.
• Cole: compiler optimization level exploration - Kenneth Hoste and Lieven Eeckhout. CGO 2008.
• MILEPOST GCC: machine learning based research compiler - Grigori Fursin, Cupertino Miranda, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Ayal Zaks, Bilha Mendelson et al., 2008
• Evaluating heuristic optimization phase order search algorithms - J. W. Davidson, Gary S. Tyson, D. B. Whalley, and P. A. Kulkarni. CGO 2007.
• Rapidly selecting good compiler optimizations using performance counters - John Cavazos, Grigori Fursin, Felix Agakov, Edwin Bonilla, Michael FP O'Boyle, and Olivier Temam. CGO 2007.
• Using machine learning to focus iterative optimization - Felix Agakov, Edwin Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael FP O'Boyle, John Thomson, Marc Toussaint, and Christopher KI Williams. CGO 2006.
• Method-specific dynamic compilation using logistic regression - John Cavazos and Michael FP O'boyle. OOPSLA 2005.
• Predicting unroll factors using supervised classification - Mark Stephenson and Saman Amarasinghe. CGO 2005.
• Fast searches for effective optimization phase sequences - Prasad Kulkarni, Stephen Hines, Jason Hiser, David Whalley, Jack Davidson, and Douglas Jones. PLDI 2004.

Instruction-level Optimisation

• RL4ReAl: Reinforcement Learning for Register Allocation - S. VenkataKeerthy, Siddharth Jain, Anilava Kundu, Rohit Aggarwal, Albert Cohen, Ramakrishna Upadrasta. CC 2023.
• Reinforcement Learning assisted Loop Distribution for Locality and Vectorization - Shalini Jain, S. VenkataKeerthy, Rohit Aggarwal, Tharun Kumar Dangeti, Dibyendu Das, Ramakrishna Upadrasta. LLVM HPC Workshop 2022.
• Discovering faster matrix multiplication algorithms with reinforcement learning - Fawzi, Alhussein, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov et al. Nature 2022
• A Reinforcement Learning Environment for Polyhedral Optimizations - Alexander Brauckmann, Andrés Goens, Jeronimo