Machine Learning in Compiler Optimization

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.

# Parallel Computing and Optimization Techniques # Computer Science # Software Engineering Research # Logic, programming, and type systems

Zheng Wang, Michael F. P. O’boyle (2018). "Machine Learning in Compiler Optimization". Proceedings of the IEEE. https://doi.org/10.1109/jproc.2018.2817118