Chapter

Euro-Par 2007 Parallel Processing

Volume 4641 of the series Lecture Notes in Computer Science pp 672-681

Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers

  • Hans E. PlesserAffiliated withDept. of Mathematical Sciences and Technology, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås
  • , Jochen M. EpplerAffiliated withHonda Research Institute, Offenbach/MainBernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg
  • , Abigail MorrisonAffiliated withRIKEN Brain Science Institute, Wako-shi, Saitama
  • , Markus DiesmannAffiliated withBernstein Center for Computational Neuroscience, Albert-Ludwigs-University, FreiburgRIKEN Brain Science Institute, Wako-shi, Saitama
  • , Marc-Oliver GewaltigAffiliated withHonda Research Institute, Offenbach/MainBernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg

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Abstract

To understand the principles of information processing in the brain, we depend on models with more than 105 neurons and 109 connections. These networks can be described as graphs of threshold elements that exchange point events over their connections.

From the computer science perspective, the key challenges are to represent the connections succinctly; to transmit events and update neuron states efficiently; and to provide a comfortable user interface. We present here the neural simulation tool NEST, a neuronal network simulator which addresses all these requirements. To simulate very large networks with acceptable time and memory requirements, NEST uses a hybrid strategy, combining distributed simulation across cluster nodes (MPI) with thread-based simulation on each computer. Benchmark simulations of a computationally hard biological neuronal network model demonstrate that hybrid parallelization yields significant performance benefits on clusters of multi-core computers, compared to purely MPI-based distributed simulation.