Original Research ARTICLE

Front. Neuroinform., 02 November 2012 | doi: 10.3389/fninf.2012.00026

Supercomputers ready for use as discovery machines for neuroscience

  • 1Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Centre, Jülich, Germany
  • 2RIKEN Brain Science Institute, Wako, Japan
  • 3Simulation Laboratory Neuroscience – Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Centre, Jülich, Germany
  • 4Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg, Germany
  • 5High-Performance Computing Team, RIKEN Computational Science Research Program, Kobe, Japan
  • 6Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan
  • 7Integrated Systems Biology Laboratory, Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto, Japan
  • 8Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany
  • 9Medical Faculty, RWTH Aachen University, Aachen, Germany

NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to achieve excellent scaling. K is capable of simulating networks corresponding to a brain area with 108 neurons and 1012 synapses in the worst case scenario of random connectivity; for larger networks of the brain its hierarchical organization can be exploited to constrain the number of communicating computer nodes. We discuss the limits of the software technology, comparing maximum filling scaling plots for K and the JUGENE BG/P system. The usability of these machines for network simulations has become comparable to running simulations on a single PC. Turn-around times in the range of minutes even for the largest systems enable a quasi interactive working style and render simulations on this scale a practical tool for computational neuroscience.

Keywords: supercomputer, large-scale simulation, spiking neural networks, parallel computing, computational neuroscience

Citation: Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A and Diesmann M (2012) Supercomputers ready for use as discovery machines for neuroscience. Front. Neuroinform. 6:26. doi: 10.3389/fninf.2012.00026

Received: 12 July 2012; Accepted: 08 October 2012;
Published online: 02 November 2012.

Edited by:

Andrew P. Davison, Centre National de la Recherche Scientifique, France

Reviewed by:

Anders Lansner, Kungliga Tekniska Högskolan, Sweden
Michael Hines, Yale University, USA
James A. Bednar, University of Edinburgh, UK

Copyright: © 2012 Helias, Kunkel, Masumoto, Igarashi, Eppler, Ishii, Fukai, Morrison and Diesmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Moritz Helias, Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Centre, 52425 Jülich, Germany. e-mail: m.helias@fz-juelich.de

Back to top