@inproceedings{154e583877fd41bb8e5c3d8ab19f3b2e,
title = "Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework",
abstract = "In this paper we explore the novel application of a linear genetic programming framework, Shackleton, to optimizing sequences of LLVM optimization passes. The algorithm underpinning Shackleton is discussed, with an emphasis on the effects of different features unique to the framework when applied to LLVM pass sequences. Combined with analysis of different hyperparameter settings, we report the results on automatically optimizing pass sequences with Shackleton for two software applications at differing complexity levels. Finally, we reflect on the advantages and limitations of our current implementation and lay out a path for further improvements. These improvements aim to surpass hand-crafted solutions with an automatic discovery method for an optimal pass sequence.",
keywords = "compiler optimization, evolutionary algorithms, genetic programming, metaheuristics, parameter tuning",
author = "Hannah Peeler and Li, \{Shuyue Stella\} and Sloss, \{Andrew N.\} and Reid, \{Kenneth N.\} and Yuan Yuan and Wolfgang Banzhaf",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 2022 Genetic and Evolutionary Computation Conference, GECCO 2022 ; Conference date: 09-07-2022 Through 13-07-2022",
year = "2022",
month = jul,
day = "9",
doi = "10.1145/3520304.3528945",
language = "英语",
series = "GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "578--581",
booktitle = "GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference",
}