Machine Learning in Compiler Optimisation

TL;DR

This paper provides an accessible overview of how machine learning techniques are transforming compiler optimization, covering key concepts, research areas, and future challenges in the field.

Contribution

It offers a comprehensive survey and roadmap of machine learning applications in compiler optimization, highlighting main concepts, achievements, and open issues.

Findings

Machine learning has become central to modern compiler optimization.

The paper maps out research areas and key achievements in the field.

Open issues and future research directions are discussed.

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.