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High-throughput fuzzy clustering on heterogeneous architectures

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High-throughput fuzzy clustering on heterogeneous architectures

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dc.contributor.author Cebrian, Juan M. es_ES
dc.contributor.author Imbernón, Baldomero es_ES
dc.contributor.author Soto, Jesús es_ES
dc.contributor.author García, José M. es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.date.accessioned 2021-06-29T03:31:24Z
dc.date.available 2021-06-29T03:31:24Z
dc.date.issued 2020-05 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/168482
dc.description.abstract [EN] The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data'' because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40x compared to the sequential counterpart version. es_ES
dc.description.sponsorship This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE), RTI2018-096384-B-I00, RTI2018-098156-B-C53 and RTC-2017-6389-5. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Parallel fuzzy clustering es_ES
dc.subject Fuzzy clustering es_ES
dc.subject Fuzzy minimals es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title High-throughput fuzzy clustering on heterogeneous architectures es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2020.01.022 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2016-78799-P/ES/DESARROLLO HOLISTICO DE APLICACIONES EMERGENTES EN SISTEMAS HETEROGENEOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098156-B-C53/ES/TECNICAS INNOVADORAS EN COMPUTACION ESPECIALIZADA Y DE ALTAS PRESTACIONES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Cebrian, JM.; Imbernón, B.; Soto, J.; García, JM.; Cecilia-Canales, JM. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems. 106:401-411. https://doi.org/10.1016/j.future.2020.01.022 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2020.01.022 es_ES
dc.description.upvformatpinicio 401 es_ES
dc.description.upvformatpfin 411 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 106 es_ES
dc.relation.pasarela S\403841 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
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