Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms

Autores
Guidet, Sebastián; Hernández-García, Ruber; Frati, Fernando Emmanuel; Barrientos, Ricardo J.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
When searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm.
Instituto de Investigación en Informática
Materia
Ciencias Informáticas
High Performance Computing
identification of individuals
Local linear binary pattern
Finger veins
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:10915/140636
Acceder
id SEDICI_5209a837cdf61bb59982edac48b7d80d
oai_identifier_str oai:sedici.unlp.edu.ar:10915/140636
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platformsGuidet, SebastiánHernández-García, RuberFrati, Fernando EmmanuelBarrientos, Ricardo J.Ciencias InformáticasHigh Performance Computingidentification of individualsLocal linear binary patternFinger veinsGPUWhen searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm.Instituto de Investigación en Informática2022-07info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf12-16http://sedici.unlp.edu.ar/handle/10915/140636enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2126-0info:eu-repo/semantics/reference/hdl/10915/139373info: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:UNLP2025-09-29T11:35:43Zoai:sedici.unlp.edu.ar:10915/140636Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:35:43.982SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
title Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
spellingShingle Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
Guidet, Sebastián
Ciencias Informáticas
High Performance Computing
identification of individuals
Local linear binary pattern
Finger veins
GPU
title_short Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
title_full Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
title_fullStr Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
title_full_unstemmed Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
title_sort Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms
dc.creator.none.fl_str_mv Guidet, Sebastián
Hernández-García, Ruber
Frati, Fernando Emmanuel
Barrientos, Ricardo J.
author Guidet, Sebastián
author_facet Guidet, Sebastián
Hernández-García, Ruber
Frati, Fernando Emmanuel
Barrientos, Ricardo J.
author_role author
author2 Hernández-García, Ruber
Frati, Fernando Emmanuel
Barrientos, Ricardo J.
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
High Performance Computing
identification of individuals
Local linear binary pattern
Finger veins
GPU
topic Ciencias Informáticas
High Performance Computing
identification of individuals
Local linear binary pattern
Finger veins
GPU
dc.description.none.fl_txt_mv When searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm.
Instituto de Investigación en Informática
description When searching on unstructured data (video, images, etc.), response times are a critical factor. In this work we propose an implementation on two types of multi-GPU and multi-node/multi-core platforms, for massive searches. The presented method aims to reduce the time involved in the search process by solving simultaneous queries over the system and a database of millions of elements. The results show that the multi-GPU approach is 1.6 times superior to the multi-node/multi-core algorithm. Moreover, in both algorithms the speedup is directly proportional to the number of nodes reaching 156x for 4 GPUs, and 87x in the case of the hybrid multi-node/multi-core algorithm.
publishDate 2022
dc.date.none.fl_str_mv 2022-07
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
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/140636
url http://sedici.unlp.edu.ar/handle/10915/140636
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2126-0
info:eu-repo/semantics/reference/hdl/10915/139373
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
12-16
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844616235916984320
score 13.070432

Publicaciones similares