- author = "Hannah Peeler and Shuyue Stella Li and Andrew N. Sloss and Kenneth N. Reid and Yuan Yuan and Wolfgang Banzhaf",
- title = "Optimizing {LLVM} Pass Sequences with {Shackleton}: A Linear Genetic Programming Framework",
- howpublished = "Arxiv",
- year = "2022",
- month = "31 " # jan,
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Evolutionary Algorithms, Compiler Optimization, Parameter Tuning, Taguchi Method, Metaheuristics, C, AArch64, ACO, TSP, SSP",
- eprint = "2201.13305",
- archiveprefix = "arXiv",
- primaryclass = "cs.NE",
-
URL = "
https://arxiv.org/abs/2201.13305",
-
code_url = "https://github.com/ARM-software/Shackleton-Framework",
- size = "11 pages",
- abstract = "we introduce Shackleton as a generalized frame-work enabling the application of linear genetic programming to a variety of use cases. We also explore here a novel application for this class of methods: optimising 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 using 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.",
-
notes = "p3 'Ant Colony Optimization for the Traveling Salesman
Problem (ACOTSP)'
http://www.aco-metaheuristic.org/aco-code
p3 'Backtrack Algorithm for the Subset Sum Problem (SSP)' https://github.com/parthnan/SubsetSum-BacktrackAlgorithm",
