Goodness of the GPU Permutation Index: Performance and Quality Results

Similarity searching is a useful operation for many real applications that work on non-structured or multimedia databases. In these scenarios, it is significant to search similar objects to another object given as a query. There exist several indexes to avoid exhaustively review all database objects...

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Detalles Bibliográficos
Autores principales: Lopresti, Mariela, Piccoli, María Fabiana, Reyes, Nora Susana
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Permutation Index
GPU
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/130350
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-130350
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Permutation Index
GPU
spellingShingle Ciencias Informáticas
Permutation Index
GPU
Lopresti, Mariela
Piccoli, María Fabiana
Reyes, Nora Susana
Goodness of the GPU Permutation Index: Performance and Quality Results
topic_facet Ciencias Informáticas
Permutation Index
GPU
description Similarity searching is a useful operation for many real applications that work on non-structured or multimedia databases. In these scenarios, it is significant to search similar objects to another object given as a query. There exist several indexes to avoid exhaustively review all database objects to answer a query. In many cases, even with the help of an index, it could not be enough to have reasonable response times, and it is necessary to consider approximate similarity searches. In this kind of similarity search, accuracy or determinism is traded for faster searches. A good representative for approximate similarity searches is the Permutation Index. In this paper, we give an implementation of the Permutation Index on GPU to speed approximate similarity search on massive databases. Our implementation takes advantage of the GPU parallelism. Besides, we consider speeding up the answer time of several queries at the same time. We also evaluate our parallel index considering answer quality and time performance on the different GPUs. The search performance is promising, independently of their architecture, because of careful planning and the correct resources use.
format Objeto de conferencia
Objeto de conferencia
author Lopresti, Mariela
Piccoli, María Fabiana
Reyes, Nora Susana
author_facet Lopresti, Mariela
Piccoli, María Fabiana
Reyes, Nora Susana
author_sort Lopresti, Mariela
title Goodness of the GPU Permutation Index: Performance and Quality Results
title_short Goodness of the GPU Permutation Index: Performance and Quality Results
title_full Goodness of the GPU Permutation Index: Performance and Quality Results
title_fullStr Goodness of the GPU Permutation Index: Performance and Quality Results
title_full_unstemmed Goodness of the GPU Permutation Index: Performance and Quality Results
title_sort goodness of the gpu permutation index: performance and quality results
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/130350
work_keys_str_mv AT loprestimariela goodnessofthegpupermutationindexperformanceandqualityresults
AT piccolimariafabiana goodnessofthegpupermutationindexperformanceandqualityresults
AT reyesnorasusana goodnessofthegpupermutationindexperformanceandqualityresults
bdutipo_str Repositorios
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