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Parallelization of algorithms for neural networks

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Applied Parallel Computing Computations in Physics, Chemistry and Engineering Science (PARA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1041))

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Abstract

In this paper we present the strategies adopted in the parallelization of two algorithms for the simulation of two classes of neural networks: the Hopfield Network and the Error BackPropagation Network.

Although the parallel algorithms have been developed within the (loosely synchronous) SPMD parallel programming model, the particular nature of the strategies adopted make the final parallel algorithms not expressible within the HPF-like programming paradigm; therefore a more flexible programming model, the message passing programming paradigm, has been adopted, and the final development has been carried out in the PVM environment.

This work has been supported in part by the Italian Ministry of University and Scientific and Technological Research within the “40%” Project.

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References

  1. R.H.Nielsen, Neurocomputing, Addison-Wesley.

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Authors and Affiliations

  1. Dipartimento di Informatica e Sistemistica, Università di Napoli “Federico II”, Via Claudio 21, 80125, Napoli, Italy

    Beniamino Di Martino

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  1. Beniamino Di Martino
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Jack Dongarra Kaj Madsen Jerzy Waśniewski

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© 1996 Springer-Verlag Berlin Heidelberg

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Di Martino, B. (1996). Parallelization of algorithms for neural networks. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing Computations in Physics, Chemistry and Engineering Science. PARA 1995. Lecture Notes in Computer Science, vol 1041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60902-4_16

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  • DOI: https://doi.org/10.1007/3-540-60902-4_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60902-5

  • Online ISBN: 978-3-540-49670-0

  • eBook Packages: Springer Book Archive

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