A Survey on Compiler Autotuning using Machine Learning

TL;DR

This survey reviews recent machine learning techniques applied to compiler autotuning, focusing on optimization selection and phase-ordering, highlighting advances, classifications, and influential works in the field.

Contribution

It provides a comprehensive classification and summary of recent machine learning approaches for compiler optimization selection and phase-ordering.

Findings

Machine learning improves compiler optimization results.

Recent approaches effectively address optimization selection.

Phase-ordering techniques have advanced significantly.

Abstract

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.