Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Li:2022:GI,
-
author = "Shuyue Stella Li and Hannah Peeler and
Andrew N. Sloss and Kenneth N. Reid and Wolfgang Banzhaf",
-
title = "Genetic Improvement in the {Shackleton} Framework for
Optimizing {LLVM} Pass Sequences",
-
booktitle = "GI @ GECCO 2022",
-
year = "2022",
-
editor = "Bobby R. Bruce and Vesna Nowack and Aymeric Blot and
Emily Winter and W. B. Langdon and Justyna Petke",
-
pages = "1938--1939",
-
address = "Boston, USA",
-
publisher_address = "New York, NY, USA",
-
month = "9 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
note = "{Winner Best Presentation}",
-
keywords = "genetic algorithms, genetic programming, genetic
improvement, linear genetic programming, Compilers,
Evolutionary Algorithms, Compiler Optimization,
Parameter Tuning, Metaheuristics, Shackleton-GI",
-
isbn13 = "978-1-4503-9268-6/22/07",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/gecco2022/gi2022/papers/Li_2022_GI.pdf",
-
URL = "https://arxiv.org/abs/2204.13261",
-
DOI = "doi:10.1145/3520304.3534000",
-
slides_url = "http://geneticimprovementofsoftware.com/slides/gi2022gecco/li-genetic-improvement-in-the-shackleton-gi-gecco-22.pdf",
-
code_url = "https://github.com/ARM-software/Shackleton-Framework",
-
video_url = "https://www.youtube.com/watch?v=20l_d3UuDPU&list=PLI8fiFpB7BoIHgl5CsdtjfWvHlE5N6pje&index=3",
-
size = "2 pages",
-
abstract = "Genetic Improvement is a search technique that aims to
improve a given acceptable solution to a problem. we
present the novel use of genetic improvement to find
problem-specific optimized LLVM Pass sequences. We
develop a Pass-level edit 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 3.7percent runtime improvement compared
to the default LLVM optimisation level -O3 which
targets 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. we discuss the advantages
and limitations of the GI feature in the Shackleton
Framework and present our results.",
-
notes = "http://geneticimprovementofsoftware.com/events/gecco2022
Backtrack Algorithm for the Subset Sum Problem
(SSP)
GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Shuyue Stella Li
Hannah Peeler
Andrew N Sloss
Kenneth N Reid
Wolfgang Banzhaf
Citations