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dc.date.accessioned 2022-08-17T19:03:07Z
dc.date.available 2022-08-17T19:03:07Z
dc.date.issued 2022
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/140636
dc.description.abstract 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. en
dc.format.extent 12-16 es
dc.language en es
dc.subject High Performance Computing es
dc.subject identification of individuals es
dc.subject Local linear binary pattern es
dc.subject Finger veins es
dc.subject GPU es
dc.title Comparative analysis of exhaustive searching on a massive finger-vein database over multi-node/multi-core and multi-GPU platforms en
dc.type Objeto de conferencia es
sedici.identifier.isbn 978-950-34-2126-0 es
sedici.creator.person Guidet, Sebastián es
sedici.creator.person Hernández-García, Ruber es
sedici.creator.person Frati, Fernando Emmanuel es
sedici.creator.person Barrientos, Ricardo J. es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Instituto de Investigación en Informática es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2022-07
sedici.relation.event X Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 2022) es
sedici.description.peerReview peer-review es
sedici.relation.isRelatedWith http://sedici.unlp.edu.ar/handle/10915/139373 es
sedici.relation.bookTitle Short papers of the 10th Conference on Cloud Computing, Big Data & Emerging Topics es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)