Data parallel density-based genetic clustering on CUDA architecture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092261" target="_blank" >RIV/61989100:27240/14:86092261 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092261

Výsledek na webu

<a href="http://dx.doi.org/10.1002/cpe.3054" target="_blank" >http://dx.doi.org/10.1002/cpe.3054</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cpe.3054" target="_blank" >10.1002/cpe.3054</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data parallel density-based genetic clustering on CUDA architecture

Popis výsledku v původním jazyce

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 canexecute 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 d

Název v anglickém jazyce

Data parallel density-based genetic clustering on CUDA architecture

Popis výsledku anglicky

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 canexecute 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 d

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Concurrency Computation Practice and Experience

ISSN

1532-0626

e-ISSN

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Svazek periodika

26

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

1097-1112

Kód UT WoS článku

000332983700007

EID výsledku v databázi Scopus

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