Article

Neuroinformatics

, Volume 8, Issue 1, pp 43-60

Open Access This content is freely available online to anyone, anywhere at any time.

Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework

  • Mikael DjurfeldtAffiliated withSchool of Computer Science and Communication, Royal Institute of TechnologyRIKEN Brain Science Institute Email author 
  • , Johannes HjorthAffiliated withSchool of Computer Science and Communication, Royal Institute of Technology
  • , Jochen M. EpplerAffiliated withHonda Research Institute Europe GmbHBernstein Center for Computational Neuroscience, Albert-Ludwigs-Universität Freiburg
  • , Niraj DudaniAffiliated withNational Centre for Biological Sciences
  • , Moritz HeliasAffiliated withBernstein Center for Computational Neuroscience, Albert-Ludwigs-Universität Freiburg
  • , Tobias C. PotjansAffiliated withInstitute of Neurosciences and Medicine, Research Center JülichRIKEN Computational Science Research Program
  • , Upinder S. BhallaAffiliated withNational Centre for Biological Sciences
  • , Markus DiesmannAffiliated withRIKEN Brain Science InstituteBernstein Center for Computational Neuroscience, Albert-Ludwigs-Universität FreiburgRIKEN Computational Science Research Program
  • , Jeanette Hellgren KotaleskiAffiliated withSchool of Computer Science and Communication, Royal Institute of Technology
    • , Örjan EkebergAffiliated withSchool of Computer Science and Communication, Royal Institute of Technology

Abstract

MUSIC is a standard API allowing large scale neuron simulators to exchange data within a parallel computer during runtime. A pilot implementation of this API has been released as open source. We provide experiences from the implementation of MUSIC interfaces for two neuronal network simulators of different kinds, NEST and MOOSE. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. Benchmarks show that the MUSIC pilot implementation provides efficient data transfer in a cluster computer with good scaling. We conclude that MUSIC fulfills the design goal that it should be simple to adapt existing simulators to use MUSIC. In addition, since the MUSIC API enforces independence of the applications, the multi-simulation could be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.

Keywords

MUSIC Large-scale simulation Computer simulation Computational neuroscience Neuronal network models Inter-operability MPI Parallel processing