Perils of Combining Parallel Distance Computations with Metric and Ptolemaic Indexing in kNN Queries

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10281374" target="_blank" >RIV/00216208:11320/14:10281374 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-11988-5_12" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-11988-5_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-11988-5_12" target="_blank" >10.1007/978-3-319-11988-5_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Perils of Combining Parallel Distance Computations with Metric and Ptolemaic Indexing in kNN Queries

Popis výsledku v původním jazyce

Similarity search methods face serious performance issues since similarity functions are rather expensive to compute. Many optimization techniques were designed to reduce the number of similarity computations, when a query is being resolved. Indexing methods, like pivot table prefiltering, based on the metric properties of feature space, are one of the most popular methods. They can increase the speed of query evaluation even by orders of magnitude. Another approach is to employ highly parallel architectures like GPUs to accelerate evaluation by unleashing their raw computational power. Unfortunately, resolving the k~nearest neighbors (kNN) queries optimized with metric indexing is a problem that is serial in nature. In this paper, we explore the perils of kNN parallelization and we propose a new algorithm that basically converts kNN queries into range queries, which are perfectly parallelizable. We have experimentally evaluated all approaches using a~highly parallel environment compri

Název v anglickém jazyce

Perils of Combining Parallel Distance Computations with Metric and Ptolemaic Indexing in kNN Queries

Popis výsledku anglicky

Similarity search methods face serious performance issues since similarity functions are rather expensive to compute. Many optimization techniques were designed to reduce the number of similarity computations, when a query is being resolved. Indexing methods, like pivot table prefiltering, based on the metric properties of feature space, are one of the most popular methods. They can increase the speed of query evaluation even by orders of magnitude. Another approach is to employ highly parallel architectures like GPUs to accelerate evaluation by unleashing their raw computational power. Unfortunately, resolving the k~nearest neighbors (kNN) queries optimized with metric indexing is a problem that is serial in nature. In this paper, we explore the perils of kNN parallelization and we propose a new algorithm that basically converts kNN queries into range queries, which are perfectly parallelizable. We have experimentally evaluated all approaches using a~highly parallel environment compri

Druh

D - Stať ve sborníku

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)

Rok uplatnění

2014

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

978-3-319-11987-8

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

127-138

Název nakladatele

Springer Berlin Heidelberg

Místo vydání

Heidelberg, Germany

Místo konání akce

Los Cabos, Mexiko

Datum konání akce

29. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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