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dc.contributor.authorSouravlas, Stavros-
dc.contributor.authorSifaleras, Angelo-
dc.contributor.authorKatsavounis, Stefanos-
dc.date.accessioned2020-03-21T09:11:40Z-
dc.date.available2020-03-21T09:11:40Z-
dc.date.issued2020-
dc.identifier10.1109/ACCESS.2020.2982227en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2982227en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/645-
dc.description.abstractRecently, a new trend has emerged in the field of parallel and high performance computing, the hybrid implementation using CPU-GPU modules. In such implementations, the computational load is shared between the CPU and GPU, in order to improve the computational efficiency. However, the task of sharing the computational load between the two modules is a rather difficult one, with a number of limitations being imposed. This paper extends our recent work [1] on community detection, which is based on transforming a network of nodes into a set of threaded binary trees. In this work, we share the computational load between the two units: the CPU takes specific samples of the network communities and organizes them in the form of threaded binary trees. The GPU takes over the heavy load of reading this data and transforming it into a path-matrix. Finally, this matrix is sent back to the CPU for analysis, community detection and overlaps, as well as network information upgrades. Our simulation results show significant improvement over our previous strategy and other known community detection strategies found in the literature.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.sourceIEEE Accessen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subjectFRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineeringen_US
dc.subject.otherCommunity Detectionen_US
dc.subject.otherParallel Algorithmsen_US
dc.subject.otherBinary Treesen_US
dc.subject.otherSocial Circlesen_US
dc.subject.otherGPU-CPU Schedulingen_US
dc.titleHybrid CPU-GPU community detection in weighted networksen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςel
local.identifier.volume8en_US
local.identifier.firstpage57527-
local.identifier.lastpage57551-
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