Goodness of the GPU Permutation Index: Performance and Quality Results

Autores
Lopresti, Mariela; Piccoli, María Fabiana; Reyes, Nora Susana
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
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.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Permutation Index
GPU
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/130350

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spelling Goodness of the GPU Permutation Index: Performance and Quality ResultsLopresti, MarielaPiccoli, María FabianaReyes, Nora SusanaCiencias InformáticasPermutation IndexGPUSimilarity 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.Workshop: WBDMD - Base de Datos y Minería de DatosRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf321-332http://sedici.unlp.edu.ar/handle/10915/130350enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:47Zoai:sedici.unlp.edu.ar:10915/130350Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:47.967SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Goodness of the GPU Permutation Index: Performance and Quality Results
title Goodness of the GPU Permutation Index: Performance and Quality Results
spellingShingle Goodness of the GPU Permutation Index: Performance and Quality Results
Lopresti, Mariela
Ciencias Informáticas
Permutation Index
GPU
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
dc.creator.none.fl_str_mv Lopresti, Mariela
Piccoli, María Fabiana
Reyes, Nora Susana
author Lopresti, Mariela
author_facet Lopresti, Mariela
Piccoli, María Fabiana
Reyes, Nora Susana
author_role author
author2 Piccoli, María Fabiana
Reyes, Nora Susana
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Permutation Index
GPU
topic Ciencias Informáticas
Permutation Index
GPU
dc.description.none.fl_txt_mv 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.
Workshop: WBDMD - Base de Datos y Minería de Datos
Red de Universidades con Carreras en Informática
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.
publishDate 2021
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