Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences
Abstract
Genetic improvement is a search technique that aims to improve a given acceptable solution to a problem. In this paper, we present the novel use of genetic improvement to find problem-specific optimized LLVM pass sequences. We develop a pass-level patch representation in the linear genetic programming framework, Shackleton, to evolve the modifications to be applied to the default optimization pass sequences. Our GI-evolved solution has a mean of 3.7% runtime improvement compared to the -O3 optimization level in the default code generation options which optimizes on runtime. The proposed GI method provides an automatic way to find a problem-specific optimization sequence that improves upon a general solution without any expert domain knowledge. In this paper, we discuss the advantages and limitations of the GI feature in the Shackleton Framework and present our results.
Keywords
Cite
@article{arxiv.2204.13261,
title = {Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences},
author = {Shuyue Stella Li and Hannah Peeler and Andrew N. Sloss and Kenneth N. Reid and Wolfgang Banzhaf},
journal= {arXiv preprint arXiv:2204.13261},
year = {2022}
}
Comments
3 pages, 2 figures