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18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 15
Massively Parallel Data Mining Using Reconfigurable Hardware: Approximate String Matching
Santa Fe, New Mexico
April 26-April 30
ISBN: 0-7695-2132-0
Qiong Zhang, Washington University in St. Louis
Roger D. Chamberlain, Washington University in St. Louis
Ronald S. Indeck, Washington University in St. Louis
Benjamin M. West, Washington University in St. Louis
Jason White, Washington University in St. Louis
Data mining is an application that is commonly executed on massively parallel systems, often using clusters with hundreds of processors. With a disk-based data store, however, the data must first be delivered to the processors before effective mining can take place. Here, we describe the prototype of an experimental system that moves processing closer to where the data resides, on the disk, and exploits massive parallelism via reconfigurable hardware to perform the computation. The performance of the prototype is also reported.
Citation:
Qiong Zhang, Roger D. Chamberlain, Ronald S. Indeck, Benjamin M. West, Jason White, "Massively Parallel Data Mining Using Reconfigurable Hardware: Approximate String Matching," ipdps, vol. 16, pp.259a, 18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 15, 2004
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