Similarity Search of Sparse Histograms on GPU Architecture
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Similarity Search of Sparse Histograms on GPU Architecture
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA15-08916S" target="_blank" >GA15-08916S: Efficient subgraph discovery for petabyte-scale web analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Similarity Search and Applications
ISBN
978-3-319-46758-0
ISSN
0302-9743
e-ISSN
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Number of pages
14
Pages from-to
325-338
Publisher name
Springer International Publishing
Place of publication
Switzerland
Event location
Tokyo
Event date
Oct 24, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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