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dc.contributor.authorKrömer, Pavel
dc.contributor.authorPlatoš, Jan
dc.contributor.authorSnášel, Václav
dc.date.accessioned2014-05-20T10:43:37Z
dc.date.available2014-05-20T10:43:37Z
dc.date.issued2014
dc.identifier.citationConcurrency and Computation: Practice and Experience. 2014, vol. 26, issue 5, p. 1097-1112.cs
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.urihttp://hdl.handle.net/10084/101855
dc.description.abstractEvolutionary clustering algorithms have been proven as a good ability to find clusters in data. Among their advantages belong the abilities to adapt to data and to determine the number of clusters automatically, thus requiring less a priori assumptions about analyzed objects than traditional clustering methods. Unfortunately, such a clustering by genetic algorithms and evolutionary algorithms in general suffers from high computational costs when it comes to recurrent fitness function evaluation. Computing on graphic processing units (GPUs) is a recent programming and development paradigm bringing high performance parallel computing closer to general audience. Modern general purpose GPUs are composed of tens to thousands of computational cores that can execute programs in parallel using the single instruction multiple data parallel processing approach. General purpose GPU programs need to be designed and implemented in a data parallel way and with respect to the architecture of target devices to fully utilize their high performance. This study presents a design, implementation, and evaluation of a data parallel genetic algorithm for density-based clustering. The algorithm was implemented and evaluated on the nVidia Compute Unified Device Architecture (CUDA) platform.cs
dc.language.isoencs
dc.publisherWileycs
dc.relation.ispartofseriesConcurrency and Computation: Practice and Experiencecs
dc.relation.urihttp://dx.doi.org/10.1002/cpe.3054cs
dc.rightsCopyright © 2013 John Wiley & Sons, Ltd.
dc.subjectgenetic algorithmscs
dc.subjectdensity-based clusteringcs
dc.subjectgenetic clusteringcs
dc.subjectSIMDcs
dc.subjectGPUcs
dc.subjectCUDAcs
dc.titleData parallel density-based genetic clustering on CUDA Architecturecs
dc.typearticlecs
dc.identifier.doi10.1002/cpe.3054
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume26cs
dc.description.issue5cs
dc.description.lastpage1112cs
dc.description.firstpage1097cs
dc.identifier.wos000332983700007


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