Machine Learning in Compiler Optimisation
arXiv:1805.03441
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.
arXiv commentAccepted to be published at Proceedings of the IEEE
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- Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures
- Automated Parallel Kernel Extraction from Dynamic Application Traces
- AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
- Learning C to x86 Translation: An Experiment in Neural Compilation
- A Reinforcement Learning Environment for Polyhedral Optimizations
- Deep Data Flow Analysis
- Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Multi-cores
- Smart, Adaptive Energy Optimization for Mobile Web Interactions