Machine Learning in Compiler Optimisation
It serves as an accessible guide and bibliography for researchers and practitioners in the fast-moving field of machine learning-based compilation, but is incremental as it surveys existing work rather than proposing novel methods.
This paper provides a comprehensive survey and introduction to machine learning in compiler optimization, covering concepts, research areas, and open issues, without presenting new experimental results or specific numerical improvements.
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.