YACOS

Abstract

The growing popularity of machine learning frameworks and algorithms has greatly contributed to the design and exploration of good code optimization sequences. Yet, in spite of this progress, mainstream compilers still provide users with only a handful of fixed optimization sequences. Finding optimization sequences that are good in general is challenging because the universe of possible sequences is potentially infinite. This paper describes a infrastructure that provides developers with the means to explore this space. Said infrastructure, henceforth called YaCoS, consists of benchmarks, search algorithms, metrics to estimate the distance between programs, and compilation strategies. YaCoS's features let users build learning models that predict, for unknown programs, optimization sequences that are likely to yield good results for them. In this paper, as a case study, we have used YaCoS to find good optimization sequences for LLVM, using code size as the objective function. Such study lets us evaluate three feature sets: two variations of the feature vectors proposed by Namolaru at al in 2010, plus the optimization statistics produced by LLVM. Our results show that YaCoS is able to find sequences that improve onto clang -Oz by 3.75% on average. Our experiments do not indicate a dominant feature set out of the three approaches that we have investigated---it is possible to find programs in which one of them is strictly better than the others.