A scalable performance prediction method for parallel neural network simulations

  • Louis Vuurpijl
  • Theo Schouten
  • Jan Vytopil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 796)


A performance prediction method is presented for indicating the performance range of MIMD parallel processor systems for neural network simulations. The total execution time of a parallel application is modeled as the sum of its calculation and communication times. The method is scalable because based on the times measured on one processor and one communication link, the performance, speedup, and efficiency can be predicted for a larger processor system. It is validated quantitatively by applying it to two popular neural networks, backpropagation and the Kohonen self-organizing feature map, decomposed on a GCel-512, a 512 transputer system. Agreement of the model with the measurements is within 9%.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Louis Vuurpijl
    • 1
  • Theo Schouten
    • 1
  • Jan Vytopil
    • 1
  1. 1.University of NijmegenED NijmegenThe Netherlands

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