Implementing and evaluating parallel evolutionary algorithms in modern GPU computing libraries
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10455088" target="_blank" >RIV/00216208:11320/22:10455088 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3520304.3529000" target="_blank" >https://doi.org/10.1145/3520304.3529000</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3520304.3529000" target="_blank" >10.1145/3520304.3529000</a>
Alternative languages
Result language
angličtina
Original language name
Implementing and evaluating parallel evolutionary algorithms in modern GPU computing libraries
Original language description
In this paper, we describe FFEAT - a library for GPU-based implementation of evolutionary algorithms based on Torch. We discuss limitations of GPU computing and how they affect implementations of evolutionary algorithms and other population-based heuristics. Using FFEAT, we implement a number of different types of nature inspired algorithms, including evolutionary algorithms, evolution strategies, and particle swarm optimization. We investigate the performance of such algorithms in a number of benchmarks and with varying algorithm settings. We show that in some cases, we can obtain an order of magnitude speed-up by running the algorithm on a GPU compared to running it on a CPU.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
ISBN
978-1-4503-9268-6
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
506-509
Publisher name
Association for Computing Machinery
Place of publication
New York, United States
Event location
Boston, USA
Event date
Jul 9, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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