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dc.contributor.authorArchuleta, Jeremyen_US
dc.contributor.authorCao, Yangen_US
dc.contributor.authorFeng, Wu-chunen_US
dc.contributor.authorScogland, Thomas R. W.en_US
dc.date.accessioned2013-06-19T14:35:52Z
dc.date.available2013-06-19T14:35:52Z
dc.date.issued2009
dc.identifierhttp://eprints.cs.vt.edu/archive/00001058/en_US
dc.identifier.urihttp://hdl.handle.net/10919/20195
dc.description.abstractThrough the algorthmic design patterns of data parallelism and task parallelism, the graphics processing unit (GPU) offers the potential to vastly accelerate discovery and innovation across a multitude of disciplines. For example, the exponential growth in data volume now presents an obstacle for high-throughput data mining in fields such as neuroinformatics and bioinformatics. As such, we present a characterization of a MapReduce-based data-mining application on a general-purpose GPU (GPGPU). Using neuroscience as the application vehicle, the results of our multi-dimensional performance evaluation show that a “one-size-fits-all” approach maps poorly across different GPGPU cards. Rather, a high-performance implementation on the GPGPU should factor in the 1) problem size, 2) type of GPU, 3) type of algorithm, and 4) data-access method when determining the type and level of parallelism. To guide the GPGPU programmer towards optimal performance within such a broad design space, we provide eight general performance characterizations of our data-mining application.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectAlgorithmsen_US
dc.subjectData structuresen_US
dc.titleMulti-Dimensional Characterization of Temporal Data Mining on Graphics Processorsen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-09-01en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001058/01/paper.pdf


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