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dc.contributor.authorCecilia, José María
dc.contributor.authorCano, Juan Carlos
dc.contributor.authorMorales García, Juan
dc.contributor.authorLlanes, Antonio
dc.contributor.authorImbernón Tudela, Baldomero
dc.date.accessioned2020-12-23T11:00:19Z
dc.date.available2020-12-23T11:00:19Z
dc.date.issued2020-11-06
dc.identifier.citationCecilia, J.M.; Cano, J.-C.; Morales-García, J.; Llanes, A.; Imbernón, B. Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors 2020, 20, 6335.es
dc.identifier.issn1424-8220
dc.identifier.otherCODEN: SENSC9
dc.identifier.urihttp://hdl.handle.net/10952/4720
dc.description.abstractInternet 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 11× 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
dc.description.sponsorshipThis 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 Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18.es
dc.language.isoenes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectIoT applicationses
dc.subjectGPU computinges
dc.subjectClustering algorithmses
dc.subjectIntelligent systemses
dc.subjectEdge computinges
dc.subjectCloud computinges
dc.subjectLow-poweres
dc.titleEvaluation of Clustering Algorithms on GPU-Based Edge Computing Platformses
dc.typearticlees
dc.rights.accessRightsopenAccesses
dc.journal.titleSensorses
dc.volume.number2es
dc.issue.number21es
dc.description.disciplineIngeniería, Industria y Construcciónes
dc.identifier.doi10.3390/s20216335es


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