mlsanityData to 14 Oct 2026

The Next 700 ML-Enabled Compiler Optimizations

arXiv 2311.10800 ↗First posted 17 Nov 2023cs.PL cs.LG cs.PFcited by 0

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Abstract (arXiv)

There is a growing interest in enhancing compiler optimizations with ML models, yet interactions between compilers and ML frameworks remain challenging. Some optimizations require tightly coupled models and compiler internals,raising issues with modularity, performance and framework independence. Practical deployment and transparency for the end-user are also important concerns. We propose ML-Compiler-Bridge to enable ML model development within a traditional Python framework while making end-to-end integration with an optimizing compiler possible and efficient. We evaluate it on both research and production use cases, for training and inference, over several optimization problems, multiple compilers and its versions, and gym infrastructures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 56 references that are in the map. First posted on arXiv 17 Nov 2023.

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15 of 56 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  5. 2015Gated Graph Sequence Neural Networkscited by 796
  6. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  7. 2018code2vec: Learning Distributed Representations of Codecited by 250
  8. 2017A Survey of Machine Learning for Big Code and Naturalnesscited by 221
  9. 2018Neural Code Comprehension: A Learnable Representation of Code Semanticscited by 59
  10. 2021CompilerGym: Robust, Performant Compiler Optimization Environments for AI Researchcited by 41
  11. 2021MLGO: a Machine Learning Guided Compiler Optimizations Frameworkcited by 40
  12. 2019NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learningcited by 37
3 more
  1. 2019AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learningcited by 12
  2. 2022RL4ReAl: Reinforcement Learning for Register Allocationcited by 4
  3. 2022POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learningcited by 2

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