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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • e-ISSN

  • 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