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arXiv:1805.03441cs.PL

Machine Learning in Compiler Optimisation

Zheng Wang, Michael O'Boyle

Abstract

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.

arXiv comments
Accepted to be published at Proceedings of the IEEE
Journal reference
DOI
Subjects
cs.PLProgramming Languages cs.DCDistributed, Parallel, and Cluster Computing cs.LGMachine Learning cs.SESoftware Engineering

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Jan 2020 to Jul 2020.

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  1. Building a $5k ML Workstation with Tiitan RTX and Ryzen ThreadRipper [video] 1 comment

    • People use ML for pretty much anything these days, including compiling programs [1] and encoding videos [2] [1] https://arxiv.org/abs/1805.03441 [2] https://arxiv.org/abs/1904.12462

      p1eskView on HNParent context

  2. Ask HN: Are compiler engineers still in demand, and what do they work on? 1 comment

    • This is as good an overview as any: https://arxiv.org/pdf/1805.03441.pdf In general we can afford to treat the problem as a search problem since our kernels are small enough. Here there is a lot of stuff that ML aided stochastic search is amazing at compared to normal compilers…

      deepnotderpView on HNParent context

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