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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86092261%21RIV15-MSM-27740___
rdf:type
skos:Concept n19:Vysledek
dcterms:description
Evolutionary 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. Evolutionary 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.
dcterms:title
Data parallel density-based genetic clustering on CUDA architecture Data parallel density-based genetic clustering on CUDA architecture
skos:prefLabel
Data parallel density-based genetic clustering on CUDA architecture Data parallel density-based genetic clustering on CUDA architecture
skos:notation
RIV/61989100:27740/14:86092261!RIV15-MSM-27740___
n3:aktivita
n5:S n5:P
n3:aktivity
P(ED1.1.00/02.0070), P(EE.2.3.20.0073), S
n3:cisloPeriodika
5
n3:dodaniDat
n16:2015
n3:domaciTvurceVysledku
n17:4347269 n17:6026877 n17:9175970
n3:druhVysledku
n9:J
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
9807
n3:idVysledku
RIV/61989100:27740/14:86092261
n3:jazykVysledku
n12:eng
n3:klicovaSlova
SIMD; GPU; genetic clustering; genetic algorithms; density-based clustering; CUDA
n3:klicoveSlovo
n14:genetic%20clustering n14:SIMD n14:CUDA n14:density-based%20clustering n14:genetic%20algorithms n14:GPU
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[F9D38E87EE78]
n3:nazevZdroje
Concurrency Computation Practice and Experience
n3:obor
n13:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n7:EE.2.3.20.0073 n7:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n16:2014
n3:svazekPeriodika
26
n3:tvurceVysledku
Snášel, Václav Platoš, Jan Krömer, Pavel
n3:wos
000332983700007
s:issn
1532-0626
s:numberOfPages
16
n15:doi
10.1002/cpe.3054
n4:organizacniJednotka
27740