Work

Machine Learning in Compiler Optimization

Year: 2018

Type: article

Abstract: In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between machine learning and compiler optimization 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.

Source: Proceedings of the IEEE

DOI: 10.1109/jproc.2018.2817118

Authors: Zheng Wang, Michael O'Boyle

Institutions: Lancaster University, University of Edinburgh

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Awards: Heterogeneous Thinking, Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres, SANDeRS: Smart, Adaptive Compilation for Dark Silicon