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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/130350
Ver los metadatos del registro completo
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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 |
dc.date.none.fl_str_mv |
2021-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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