Optimal speedup conditions for a parallel back-propagation algorithm

  • Hélène Paugam-Moisy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 634)


One way for implementing a parallel back-propagation algorithm is based on distributing the examples to be learned among different processors. This method provides with spectacular speedups for each epoch of back-propagation learning, but it shows a major drawback: parallelization implies alterations of the gradient descent algorithm. This paper presents an implementation of this parallel algorithm on a transputer network. It mentions experimental laws about the back-propagation convergence speed, and claims that optimal conditions still exist for performing an actual speedup by implementing such a parallel algorithm. It points out theoretical and experimental optimal conditions, in terms of the number of processors and the size of the example packets.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Hélène Paugam-Moisy
    • 1
  1. 1.Laboratoire de l'Informatique du ParallélismeEcole Normale Supérieure de LyonLyon Cedex 07France

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