Self-organizing migration algorithm on GPU with CUDA

Pavlech, Michal

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