Employing GPU Architectures for Permutation-based Indexing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10326734" target="_blank" >RIV/00216208:11320/16:10326734 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11042-016-3677-7" target="_blank" >http://dx.doi.org/10.1007/s11042-016-3677-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-016-3677-7" target="_blank" >10.1007/s11042-016-3677-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Employing GPU Architectures for Permutation-based Indexing

Popis výsledku v původním jazyce

Permutation-based indexing is one of the most popular techniques for the approximate nearest-neighbor search problem in high-dimensional spaces. Due to the exponential increase of multimedia data, the time required to index this data has become a serious constraint. One of the possible steps towards faster index construction is utilization of massively parallel platforms such as the GPGPU architectures. In this paper, we have analyzed the computational costs of individual steps of the permutation-based index construction in a high-dimensional feature space and summarized our hybrid CPU-GPU solution. Our experience gained from this research may be utilized in other individual problems that require computing L_p distances in high-dimensional spaces, parallel top-k selection, or partial sorting of multiple smaller sets. We also provide guidelines how to balance workload in hybrid CPU-GPU systems.

Název v anglickém jazyce

Employing GPU Architectures for Permutation-based Indexing

Popis výsledku anglicky

Permutation-based indexing is one of the most popular techniques for the approximate nearest-neighbor search problem in high-dimensional spaces. Due to the exponential increase of multimedia data, the time required to index this data has become a serious constraint. One of the possible steps towards faster index construction is utilization of massively parallel platforms such as the GPGPU architectures. In this paper, we have analyzed the computational costs of individual steps of the permutation-based index construction in a high-dimensional feature space and summarized our hybrid CPU-GPU solution. Our experience gained from this research may be utilized in other individual problems that require computing L_p distances in high-dimensional spaces, parallel top-k selection, or partial sorting of multiple smaller sets. We also provide guidelines how to balance workload in hybrid CPU-GPU systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GP14-14292P" target="_blank" >GP14-14292P: Nasazení moderních paralelních architektur ve specifických oblastech databázových systémů</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 periodika

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

—

Svazek periodika

2017

Číslo periodika v rámci svazku

76

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

29

Strana od-do

11859-11887

Kód UT WoS článku

000400845000040

EID výsledku v databázi Scopus

2-s2.0-84978151341