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Self-organizing Migration Algorithm on GPU with CUDA

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dc.title Self-organizing Migration Algorithm on GPU with CUDA en
dc.contributor.author Pavlech, Michal
dc.relation.ispartof Soft Computing Models in Industrial and Environmental Applications
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-642-32921-0
dc.identifier.isbn 978-3-642-32922-7
dc.date.issued 2013
utb.relation.volume 188
dc.citation.spage 173
dc.citation.epage 182
dc.event.title 7th Int Conf on Soft Comp Models in Industrial and Environm Applications/5th Computational Intelligence in Security for Information Syst/3rd Int Conf on EUropean Transnational Educ
dc.event.location Ostrava
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2012-09-05
dc.event.edate 2012-09-07
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag Berlin en
dc.identifier.doi 10.1007/978-3-642-32922-7_18
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-642-32922-7_18
dc.subject SOMA en
dc.subject CUDA en
dc.subject GPGPU en
dc.subject evolutionary algorithm en
dc.description.abstract A modification of Self-organizing migration algorithm for general-purpose computing on graphics processing units is proposed in this paper. The algorithm is implemented in C++ with its core parts in c-CUDA. Its implementation details and performance are evaluated and compared to previous, pure C++ version of algorithm. 6 commonly used artificial test functions are used to test the performance. The test results clearly show significant speed gains without a compromise in convergence quality. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1003127
utb.identifier.obdid 43870125
utb.identifier.scopus 2-s2.0-84868266842
utb.identifier.wok 000312974600018
utb.source d-wok
dc.date.accessioned 2013-02-19T09:01:34Z
dc.date.available 2013-02-19T09:01:34Z
utb.contributor.internalauthor Pavlech, Michal
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