A list of research papers, datasets, conference and talk and Projects for compilers and program optimization.
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework - Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi. ASPLOS 2024.
Large Language Models for Compiler Optimization - Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Kim Hazelwood, Gabriel Synnaeve, Hugh Leather. CC 2023.
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction - Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian. ICML 2023
TLP: A Deep Learning-Based Cost Model for Tensor Program Tuning - Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, Yanyong Zhang, ASPLOS, 2023.
Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization - L Trümper, T Ben-Nun, P Schaad, A Calotoiu, T Hoefler. ICS 2023.
SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations - Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, Scott Mahlke. CGO 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
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
Achieving High-performance the Functional Way: a Functional Pearl on Expressing High-performance Optimizations as Rewrite Strategies - Bastian Hagedorn, Johannes Lenfers, Thomas K{\oe}hler, Xueying Qin, Sergei Gorlatch, and Michel Steuwer. Proceedings of the ACM on Programming Languages 2020.
Reinforcement Learning Guided Software Debloating - Nham Le Van, Ashish Gehani, Arie Gurfinkel, Susmit Jha, and Jorge A. Navas. MLSys 2019.
Automatic Tuning of Compilers Using Machine Learning - Amir H. Ashouri, Gianluca Palermo, John Cavazos, and Cristina Silvano. Springer 2018.
- Saman Amarasinghe, Compiler 2.0: Using Machine Learning to Modernize Compiler Technology. LCTES 2020.
- Using Machine Learning to Modernize Compiler Technology, ACM SIGPLAN
- Amir Ashouri, Compiler Autotuning using Machine Learning: A State-of-the-art Review(slides). Polytechnic University of Milan 2018.
- Understanding Compiler Optimization, DevConfCZ 2019
- CompilerGym: Robust, Performant Compiler Optimization Environments for AI, CGO Conference 2022
- Jinliang Wei, Google AI Quorum: Scaling Deep Learning Training With Compiler Optimizations, the AI quorum, 2022
- CompilerGym - About Reinforcement learning environments for compiler and program optimization tasks (paper).
- CodeBert - pre-trained DNN models for programming languages provided by Microsoft. It also has impletelmentation of many compiler papers like: CodeBER,GraphCodeBERT, UniXcoder,CodeReviewer, CodeExecutor, LongCoder (paper).
- NeuroVectorizer - Using deep reinforcement learning to predict optimal vectorization compiler pragmas by Intel (paper).
- programl - ProGraML is a representation for programs as input to a machine learning model (paper).
- ONNX-MLIR - Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure (paper).
- IREE - A retargetable MLIR-based machine learning compiler and runtime toolkit.
- Supersonic - Automating reinforcement learning architecture design for code optimization. (paper).
- clgen - Deep learning program generator using LSTM (paper; slides).
- OpenTuner - An extensible framework for program autotuning (paper; slides)
- CPU® 2017 Benchmark Suite - SPEC CPU2017 Benchmark Suite.
- CodeNet - CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI technique (acceptance/error types)
- CodeXGLUE - Dataset for Code Understanding and Generation by Microsoft (paper)
- BHive - A Benchmark Suite and Measurement Framework for Validating x86-64 Basic Block Performance Models (paper).
- PolyBench - PolyBench is a collection of benchmarks containing static control parts. The purpose is to uniformize the execution and monitoring of kernels. (paper).
- devmap -End-to-end Deep Learning of Optimization Heuristics (paper; slides).
- TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers - (paper).
ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI
International Symposium on Code Generation and Optimization, CGO
International Parallel and Distributed Processing Symposium, IPDPS
Machine Learning and Programming Languages Workshop, MAPL
Conference on Machine Learning and Systems, MLSys
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