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Three is not a crowd: ACPU-GPU-FPGA K-means implementation
dc.contributor.author | Canales, Marcos | |
dc.contributor.author | Cáncer, Jorge | |
dc.contributor.author | Constantinescu, Denisa-Andreea | |
dc.contributor.author | Escuin, Carlos | |
dc.contributor.author | Perez, Borja | |
dc.date.accessioned | 2017-06-15T10:12:02Z | |
dc.date.available | 2017-06-15T10:12:02Z | |
dc.date.created | 2017 | |
dc.date.issued | 2017-06-15 | |
dc.identifier.uri | http://hdl.handle.net/10630/13891 | |
dc.description.abstract | Clustering is the task of assigning a set of objects into groups (clusters) so that objects in the same group are more similar to each other than to those in other groups. In particular, K-means is a clustering algorithm that calculates the cluster with the nearest mean for each object. To achieve this, it uses a function like Euclidean or Manhattan distance. Our objective is to exploit our heterogeneous computing environment, that integrates an Intel Core i7-6700K chip, 2x NVIDIA TITAN X and an Intel Altera Terasic Stratix V DE5-NET FPGA, to run K-means as fast as possible. | es_ES |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Algoritmos computacionales | es_ES |
dc.subject.other | K-means | es_ES |
dc.subject.other | Heterogeneous Computing | es_ES |
dc.subject.other | GPU+FPGA | es_ES |
dc.title | Three is not a crowd: ACPU-GPU-FPGA K-means implementation | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.centro | E.T.S.I. Informática | es_ES |
dc.relation.eventtitle | European Network on High Performance and Embedded Architecture and Compilation (HiPEAC 2017) | es_ES |
dc.relation.eventplace | Zagreb, Croatia | es_ES |
dc.relation.eventdate | 27 april 2017 | es_ES |
dc.rights.cc | by-nc-nd |