Archuleta, JeremyCao, YangFeng, Wu-chunScogland, Thomas R. W.2013-06-192013-06-192009http://hdl.handle.net/10919/20195Through 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.application/pdfenIn CopyrightAlgorithmsData structuresTechnical reportTR-09-01http://eprints.cs.vt.edu/archive/00001058/01/paper.pdf