Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors
| dc.contributor.author | Archuleta, Jeremy | en |
| dc.contributor.author | Cao, Yang | en |
| dc.contributor.author | Feng, Wu-chun | en |
| dc.contributor.author | Scogland, Thomas R. W. | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2013-06-19T14:35:52Z | en |
| dc.date.available | 2013-06-19T14:35:52Z | en |
| dc.date.issued | 2009 | en |
| dc.description.abstract | Through 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 |
| dc.format.mimetype | application/pdf | en |
| dc.identifier | http://eprints.cs.vt.edu/archive/00001058/ | en |
| dc.identifier.sourceurl | http://eprints.cs.vt.edu/archive/00001058/01/paper.pdf | en |
| dc.identifier.trnumber | TR-09-01 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/20195 | en |
| dc.language.iso | en | en |
| dc.publisher | Department of Computer Science, Virginia Polytechnic Institute & State University | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Algorithms | en |
| dc.subject | Data structures | en |
| dc.title | Multi-Dimensional Characterization of Temporal Data Mining on Graphics Processors | en |
| dc.type | Technical report | en |
| dc.type.dcmitype | Text | en |
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