research-article

Improving CUDA DNA Analysis Software with Genetic Programming

Authors:

William B. Langdon,

Brian Yee Hong Lam,

Mark HarmanAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1063 - 1070

https://doi.org/10.1145/2739480.2754652

Published: 11 July 2015 Publication History

Abstract

We genetically improve BarraCUDA using a BNF grammar incorporating C scoping rules with GP. Barracuda maps next generation DNA sequences to the human genome using the Burrows-Wheeler algorithm (BWA) on nVidia Tesla parallel graphics hardware (GPUs). GI using phenotypic tabu search with manually grown code can graft new features giving more than 100 fold speed up on a performance critical kernel without loss of accuracy.

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Klus, P., et al. BarraCUDA. BMC Res. Notes 5, 27

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Cited By

Langdon WClark D(2025)Deep imperative mutations have less impactAutomated Software Engineering10.1007/s10515-024-00475-432:1Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10515-024-00475-4
Liou JAwan MLeyba KŠulc PHofmeyr SWu CForrest S(2024)Evolving to Find Optimizations Humans Miss: Using Evolutionary Computation to Improve GPU Code for Bioinformatics ApplicationsACM Transactions on Evolutionary Learning and Optimization10.1145/37039204:4(1-29)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3703920
Callan JPetke J(2024)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10515-024-00472-7
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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 11 July 2015

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EPSRC

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Langdon WClark D(2025)Deep imperative mutations have less impactAutomated Software Engineering10.1007/s10515-024-00475-432:1Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10515-024-00475-4
Liou JAwan MLeyba KŠulc PHofmeyr SWu CForrest S(2024)Evolving to Find Optimizations Humans Miss: Using Evolutionary Computation to Improve GPU Code for Bioinformatics ApplicationsACM Transactions on Evolutionary Learning and Optimization10.1145/37039204:4(1-29)Online publication date: 15-Nov-2024
https://dl.acm.org/doi/10.1145/3703920
Callan JPetke J(2024)Multi-objective improvement of Android applicationsAutomated Software Engineering10.1007/s10515-024-00472-732:1Online publication date: 4-Nov-2024
https://doi.org/10.1007/s10515-024-00472-7
Langdon WClark D(2024)Genetic Improvement of Last Level CacheGenetic Programming10.1007/978-3-031-56957-9_13(209-226)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-56957-9_13
Langdon WPetke JBlot AClark DSilva SPaquete L(2023)Genetically Improved Software with fewer Data Cache MissesProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590542(799-802)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590542
Langdon W(2023)Jaws 30Genetic Programming and Evolvable Machines10.1007/s10710-023-09467-x24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09467-x
Petke JAlexander BBarr EBrownlee AWagner MWhite D(2023)Program transformation landscapes for automated program modification using GinEmpirical Software Engineering10.1007/s10664-023-10344-528:4Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1007/s10664-023-10344-5
Zuo SBlot APetke JWagner M(2022)Evaluation of genetic improvement tools for improvement of non-functional properties of softwareProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534004(1956-1965)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534004
Zhou KHao YMellor-Crummey JMeng XLiu XFalsafi BFerdman MLu SWenisch T(2022)ValueExpert: exploring value patterns in GPU-accelerated applicationsProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507708(171-185)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507708
Liou JAwan MHofmeyr SForrest SWu C(2022)Understanding the Power of Evolutionary Computation for GPU Code Optimization2022 IEEE International Symposium on Workload Characterization (IISWC)10.1109/IISWC55918.2022.00025(185-198)Online publication date: Nov-2022
https://doi.org/10.1109/IISWC55918.2022.00025
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