Optimalization of parallel GNG by neurons assigned to processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236161" target="_blank" >RIV/61989100:27240/17:10236161 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10236161

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-59105-6_6" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-59105-6_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-59105-6_6" target="_blank" >10.1007/978-3-319-59105-6_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimalization of parallel GNG by neurons assigned to processes

Popis výsledku v původním jazyce

The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering is used to reveal structures and to reduce large amounts of raw data. There are two main issues when clustering based on unsupervised learning, such as Growing Neural Gas (GNG) [9], is performed on vast high dimensional data collection - the fast growth of computational complexity with respect to growing data dimensionality, and the specific similarity measurement in a high-dimensional space. These two factors reduce the effectiveness of clustering algorithms in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication. The achieved speed-up was better than previous approach and the results from the standard and parallel version of GNG are same.

Název v anglickém jazyce

Optimalization of parallel GNG by neurons assigned to processes

Popis výsledku anglicky

The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering is used to reveal structures and to reduce large amounts of raw data. There are two main issues when clustering based on unsupervised learning, such as Growing Neural Gas (GNG) [9], is performed on vast high dimensional data collection - the fast growth of computational complexity with respect to growing data dimensionality, and the specific similarity measurement in a high-dimensional space. These two factors reduce the effectiveness of clustering algorithms in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication. The achieved speed-up was better than previous approach and the results from the standard and parallel version of GNG are same.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/LM2015070" target="_blank" >LM2015070: IT4Innovations národní superpočítačové centrum</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 10244

ISBN

978-3-319-59104-9

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

10

Strana od-do

63-72

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Białystok

Datum konání akce

16. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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