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@INPROCEEDINGS{Kunkel:137385,
      author       = {Kunkel, Susanne and Schmidt, Maximilian and Eppler, Jochen
                      Martin and Plesser, Hans E and Igarashi, Jun and Masumoto,
                      Gen and Fukai, Tomoki and Ishii, Shin and Morrison, Abigail
                      and Diesmann, Markus and Helias, Moritz},
      title        = {{F}rom laptops to supercomputers: a single highly scalable
                      code base for spiking neuronal network simulations},
      journal      = {BMC neuroscience},
      volume       = {14},
      number       = {Suppl 1},
      issn         = {1471-2202},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {FZJ-2013-03832},
      pages        = {P163 -},
      year         = {2013},
      abstract     = {Over the last couple of years, supercomputers such as the
                      Blue Gene/Q system JUQUEEN in Jülich and the K computer in
                      Kobe have become available for neuroscience research. These
                      massively parallel systems open the field for a new class of
                      scientific questions as they provide the resources to
                      represent and simulate brain-scale networks, but they also
                      confront the developers of simulation software with a new
                      class of problems. Initial tests with our neuronal network
                      simulator NEST [1] on JUGENE (the predecessor of JUQUEEN)
                      revealed that in order to exploit the memory capacities of
                      such machines, we needed to improve the parallelization of
                      the fundamental data structures. To address this, we
                      developed an analytical framework [2], which serves as a
                      guideline for a systematic and iterative restructuring of
                      the simulation kernel. In December 2012, the 3rd generation
                      technology was released with NEST 2.2, which enables
                      simulations of 108 neurons and 10,000 synapses per neuron on
                      the K computer [3]. Even though the redesign of the
                      fundamental data structures of NEST is driven by the demand
                      for simulations of interacting brain areas, we do not aim at
                      solutions tailored to a specific brain-scale model or
                      computing architecture. Our goal is to maintain a single
                      highly scalable code base that meets the requirements of
                      such simulations whilst still performing well on modestly
                      dimensioned lab clusters and even laptops. Here, we
                      introduce the 4 th generation simulation kernel and describe
                      the development workflow that yielded the following three
                      major improvements: the self-collapsing connection
                      infrastructure, which takes up significantly less memory in
                      the case of few local targets, the compacted node
                      infrastructure, which causes only negligible constant serial
                      memory overhead, and the reduced memory usage of synapse
                      objects, which does not affect the precision of synaptic
                      state variables. The improved code does not compromise on
                      the general usability of NEST and will be merged into the
                      common code base to be released with NEST 2.4. We show that
                      with the 4g technology it will be possible to simulate
                      networks of 10 9 neurons and 10,000 synapses per neuron on
                      the K computer.},
      month         = {Jul},
      date          = {2013-07-13},
      organization  = {Annual CNS Meeting 2013, Paris
                       (France), 13 Jul 2013 - 18 Jul 2013},
      cin          = {INM-6 / IAS-6 / JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)JSC-20090406},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / Brain-Scale Simulations
                      $(jinb33_20121101)$ / BTN-Peta - The Next-Generation
                      Integrated Simulation of Living Matter (BTN-Peta-2008-2012)
                      / HASB - Helmholtz Alliance on Systems Biology
                      (HGF-SystemsBiology) / BRAINSCALES - Brain-inspired
                      multiscale computation in neuromorphic hybrid systems
                      (269921) / SMHB - Supercomputing and Modelling for the Human
                      Brain (HGF-SMHB-2013-2017) / 411 - Computational Science and
                      Mathematical Methods (POF2-411) / W2Morrison - W2/W3
                      Professorinnen Programm der Helmholtzgemeinschaft
                      (B1175.01.12) / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-331 / $G:(DE-Juel1)jinb33_20121101$ /
                      G:(DE-Juel1)BTN-Peta-2008-2012 /
                      G:(DE-Juel1)HGF-SystemsBiology / G:(EU-Grant)269921 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-HGF)POF2-411 /
                      G:(DE-HGF)B1175.01.12 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.1186/1471-2202-14-S1-P163},
      url          = {https://juser.fz-juelich.de/record/137385},
}