Exploiting Training Example Parallelism with a Batch Variant of the ART2 Classification Algorithm

Ciechanowicz P, Dlugosz S, Kuchen H, Müller-Funk U


Abstract
In this article we develop a batch variant of the ART 2 clas sification algorithm invented by Carpenter and Grossberg. Our algorithm exploits training example parallelism while leaving the overall design of the ART 2 network unchanged such that a significant reduction of the execution time can be achieved on a multiprocessor system. We present a par allel implementation strategy and analyze it w.r.t. execu tion time and speedup. As our algorithm naturally benefits from data parallelism, the implementation uses data paral lel skeletons of the Muenster skeleton library Muesli. We show that skeletons are an efficient way to write parallel applications compared to a manual MPI implementation.

Keywords
ART 2, batch, skeletons, training example parallelism



Publication type
Conference Paper

Peer reviewed
Yes

Publication status
Published

Year
2008

Conference
7th IASTED International Conference on Parallel and Distributed Computing and Networks (PDCN)

Venue
Innsbruck, Austria

Editor
Burkhart H

Start page
195

End page
201

Publisher
ACTA Press

Place
Calgary, Canada

Language
English

ISBN
978-0-88986-713-0

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