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Machine Learning in Compiler Optimization

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

Published

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Machine Learning in Compiler Optimization. / Wang, Zheng; O'Boyle, Michael.
In: Proceedings of the IEEE , Vol. 106, No. 11, 11.2018, p. 1879-1901.

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

Harvard

Wang, Z & O'Boyle, M 2018, 'Machine Learning in Compiler Optimization', Proceedings of the IEEE , vol. 106, no. 11, pp. 1879-1901. https://doi.org/10.1109/JPROC.2018.2817118

APA

Wang, Z., & O'Boyle, M. (2018). Machine Learning in Compiler Optimization. Proceedings of the IEEE , 106(11), 1879-1901. https://doi.org/10.1109/JPROC.2018.2817118

Vancouver

Wang Z, O'Boyle M. Machine Learning in Compiler Optimization. Proceedings of the IEEE . 2018 Nov;106(11):1879-1901. Epub 2018 May 10. doi: 10.1109/JPROC.2018.2817118

Author

Wang, Zheng ; O'Boyle, Michael. / Machine Learning in Compiler Optimization. In: Proceedings of the IEEE . 2018 ; Vol. 106, No. 11. pp. 1879-1901.

Bibtex

@article{fa8f5cbce7da4126833b6626f17c7094,
title = "Machine Learning in Compiler Optimization",
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, trainingand 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.",
author = "Zheng Wang and Michael O'Boyle",
note = "{\textcopyright}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.",
year = "2018",
month = nov,
doi = "10.1109/JPROC.2018.2817118",
language = "English",
volume = "106",
pages = "1879--1901",
journal = "Proceedings of the IEEE ",
issn = "0018-9219",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - Machine Learning in Compiler Optimization

AU - Wang, Zheng

AU - O'Boyle, Michael

N1 - ©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.

PY - 2018/11

Y1 - 2018/11

N2 - 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, trainingand 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.

AB - 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, trainingand 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.

U2 - 10.1109/JPROC.2018.2817118

DO - 10.1109/JPROC.2018.2817118

M3 - Journal article

VL - 106

SP - 1879

EP - 1901

JO - Proceedings of the IEEE

JF - Proceedings of the IEEE

SN - 0018-9219

IS - 11

ER -