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Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

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Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

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Nombre: Cecilia-Canales;C ...
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dc.contributor.author Cecilia-Canales, José María es_ES
dc.contributor.author Cano, Juan-Carlos es_ES
dc.contributor.author Morales-García, Juan es_ES
dc.contributor.author Llanes, Antonio es_ES
dc.contributor.author Imbernón, Baldomero es_ES
dc.date.accessioned 2021-03-09T04:32:16Z
dc.date.available 2021-03-09T04:32:16Z
dc.date.issued 2020-11 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163481
dc.description.abstract [EN] Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms. es_ES
dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5 and by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Clustering algorithms es_ES
dc.subject IoT applications es_ES
dc.subject Intelligent systems es_ES
dc.subject Edge computing es_ES
dc.subject Cloud computing es_ES
dc.subject GPU computing es_ES
dc.subject Low-power es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s20216335 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/ES/PLANIFICACIÓN Y GESTIÓN DE RECURSOS HÍDRICOS A PARTIR DE ANÁLISIS DE DATOS DE IoT (WATERoT)/ 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.relation.projectID info:eu-repo/grantAgreement/AEI//RYC-2018-025580-I/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTC-2019-007159-5/ES/Desarrollo de infraestructuras IoT de altas prestaciones contra el cambio climático basadas en inteligencia artificial/ 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 Cecilia-Canales, JM.; Cano, J.; Morales-García, J.; Llanes, A.; Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors. 20(21):1-19. https://doi.org/10.3390/s20216335 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s20216335 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 21 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 33172017 es_ES
dc.identifier.pmcid PMC7664181 es_ES
dc.relation.pasarela S\425200 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
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