Home > Research > Publications & Outputs > Machine Learning in Compiler Optimization

Research

Associated organisational units

Electronic data

  • main

    Rights statement: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 3.17 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Machine Learning in Compiler Optimization

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
Close
More...
<mark>Journal publication date</mark>11/2018
<mark>Journal</mark>Proceedings of the IEEE
Issue number11
Volume106
Number of pages23
Pages (from-to)1879-1901
Publication StatusPublished
Early online date10/05/18
<mark>Original language</mark>English

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.

Bibliographic note

©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.