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dc.contributor.authorBlasberg, Robin V.
dc.contributor.authorGobbert, Matthias K.
dc.date.accessioned2018-10-24T19:30:33Z
dc.date.available2018-10-24T19:30:33Z
dc.date.issued2008
dc.description.abstractAffinity propagation is a clustering algorithm that functions by identifying similar datapoints in an iterative process. Its structure allows for taking full advantage of parallel computing by enabling the solution of larger problems and by solving them faster than possible in serial. We show that our memory-optimal implementation with minimal number of communication commands per iteration performs excellently on the distributed-memory cluster hpc and that it is efficient to use all 128 processor cores currently available.en_US
dc.description.urihttps://userpages.umbc.edu/~gobbert/papers/BlasbergGobbertTR2008clustering.pdfen_US
dc.format.extent11 pagesen_US
dc.genreTechnical Reporten_US
dc.identifierdoi:10.13016/M2K649X3S
dc.identifier.urihttp://hdl.handle.net/11603/11676
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofseriesHPCF Technical Report;HPCF-2008-5
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectParallel Performanceen_US
dc.subjectClustering Algorithmen_US
dc.subjectdatapointsen_US
dc.subjectmemory-optimalen_US
dc.subjectHigh Performance Computing Facility (HPCF)en_US
dc.subjectaffinity propagation
dc.titleParallel Performance Studies for a Clustering Algorithmen_US
dc.typeTexten_US


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