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

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


Zusammenfassung
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.

Schlüsselwörter
ART 2, batch, skeletons, training example parallelism



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2008

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

Konferenzort
Innsbruck, Austria

Herausgeber
Burkhart H

Erste Seite
195

Letzte Seite
201

Verlag
ACTA Press

Ort
Calgary, Canada

Sprache
Englisch

ISBN
978-0-88986-713-0

Gesamter Text