Similarity Search of Sparse Histograms on GPU Architecture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10327984" target="_blank" >RIV/00216208:11320/16:10327984 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-46759-7_25" target="_blank" >http://dx.doi.org/10.1007/978-3-319-46759-7_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-46759-7_25" target="_blank" >10.1007/978-3-319-46759-7_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Similarity Search of Sparse Histograms on GPU Architecture

Popis výsledku v původním jazyce

Searching for similar objects within large-scale database is a hard problem due to the exponential increase of multimedia data. The time required to find the nearest objects to the specific query in a high-dimensional space has become a serious constraint of the searching algorithms. One of the possible solution for this problem is utilization of massively parallel platforms such as GPU architectures. This solution becomes very sensitive for the applications working with sparse dataset. The performance of the algorithm can be totally changed depending on the different sparsity settings of the input data. In this paper, we study four different approaches on the GPU architecture for finding the similar histograms to the given queries. The performance and efficiency of observed methods were studied on sparse dataset of half a million histograms. We summarize our empirical results and point out the optimal GPU strategy for sparse histograms with different sparsity settings.

Název v anglickém jazyce

Similarity Search of Sparse Histograms on GPU Architecture

Popis výsledku anglicky

Searching for similar objects within large-scale database is a hard problem due to the exponential increase of multimedia data. The time required to find the nearest objects to the specific query in a high-dimensional space has become a serious constraint of the searching algorithms. One of the possible solution for this problem is utilization of massively parallel platforms such as GPU architectures. This solution becomes very sensitive for the applications working with sparse dataset. The performance of the algorithm can be totally changed depending on the different sparsity settings of the input data. In this paper, we study four different approaches on the GPU architecture for finding the similar histograms to the given queries. The performance and efficiency of observed methods were studied on sparse dataset of half a million histograms. We summarize our empirical results and point out the optimal GPU strategy for sparse histograms with different sparsity settings.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-08916S" target="_blank" >GA15-08916S: Efektivní identifikace podgrafů při analýze webových grafů velikosti petabajtů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Similarity Search and Applications

ISBN

978-3-319-46758-0

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

14

Strana od-do

325-338

Název nakladatele

Springer International Publishing

Místo vydání

Switzerland

Místo konání akce

Tokyo

Datum konání akce

24. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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