Survey on Automatic Tuning of Compilers by Machine Learning

Abstract

Modern compilers offer many optimization options.It is a complex problem to choose which parameter values,which combination of options and in which order to apply these options.Among them,the optimization phase ordering is the most dif-ficult one.With the improvement of traditional methods (iterative compilation combined with heuristic optimization search) and the emergence of new technologies (machine learning),it is possible to build a relatively efficient and intelligent compiler automatic tuning framework.This paper summarizes the research ideas and application methods of predecessors by investigating the related research in the past decades.Firstly,the development of compiler automatic tuning is introduced,including early manual methods,cost function driven methods,iterative compilation and machine learning-based prediction methods.Then,this paper focuses on the direct prediction based on machine learning and the automatic optimization method of iterative compilation driven by machine learning.Last but not least,several successful frameworks and some recent research results are listed.Current challenges and some key future research directions are also pointed out.

Keywords

• compiler|automatic tuning|machine learning|iterative compilation|optimization options selection|optimization phase ordering selection