% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Kunkel:172421,
      author       = {Kunkel, Susanne and Schmidt, Maximilian and Eppler, Jochen
                      M. and Plesser, Hans E. and Masumoto, Gen and Igarashi, Jun
                      and Ishii, Shin and Fukai, Tomoki and Morrison, Abigail and
                      Diesmann, Markus and Helias, Moritz},
      title        = {{S}piking network simulation code for petascale computers},
      journal      = {Frontiers in neuroinformatics},
      volume       = {8},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2014-05899},
      pages        = {78},
      year         = {2014},
      abstract     = {Brain-scale networks exhibit a breathtaking heterogeneity
                      in the dynamical properties and parameters of their
                      constituents. At cellular resolution, the entities of theory
                      are neurons and synapses and over the past decade
                      researchers have learned to manage the heterogeneity of
                      neurons and synapses with efficient data structures. Already
                      early parallel simulation codes stored synapses in a
                      distributed fashion such that a synapse solely consumes
                      memory on the compute node harboring the target neuron. As
                      petaflop computers with some 100,000 nodes become
                      increasingly available for neuroscience, new challenges
                      arise for neuronal network simulation software: Each neuron
                      contacts on the order of 10,000 other neurons and thus has
                      targets only on a fraction of all compute nodes;
                      furthermore, for any given source neuron, at most a single
                      synapse is typically created on any compute node. From the
                      viewpoint of an individual compute node, the heterogeneity
                      in the synaptic target lists thus collapses along two
                      dimensions: the dimension of the types of synapses and the
                      dimension of the number of synapses of a given type. Here we
                      present a data structure taking advantage of this double
                      collapse using metaprogramming techniques. After introducing
                      the relevant scaling scenario for brain-scale simulations,
                      we quantitatively discuss the performance on two
                      supercomputer. We show that the novel architecture scales to
                      the largest petascale supercomputers available today.},
      cin          = {JSC / INM-6 / IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {411 - Computational Science and Mathematical Methods
                      (POF2-411) / 331 - Signalling Pathways and Mechanisms in the
                      Nervous System (POF2-331) / Brain-Scale Simulations
                      $(jinb33_20121101)$ / HASB - Helmholtz Alliance on Systems
                      Biology (HGF-SystemsBiology) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) / MSNN -
                      Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
                      / BRAINSCALES - Brain-inspired multiscale computation in
                      neuromorphic hybrid systems (269921) / HBP - The Human Brain
                      Project (604102) / BTN-Peta - The Next-Generation Integrated
                      Simulation of Living Matter (BTN-Peta-2008-2012) /
                      W2Morrison - W2/W3 Professorinnen Programm der
                      Helmholtzgemeinschaft (B1175.01.12) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF2-411 / G:(DE-HGF)POF2-331 /
                      $G:(DE-Juel1)jinb33_20121101$ /
                      G:(DE-Juel1)HGF-SystemsBiology /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(EU-Grant)269921 /
                      G:(EU-Grant)604102 / G:(DE-Juel1)BTN-Peta-2008-2012 /
                      G:(DE-HGF)B1175.01.12 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000348207000001},
      pubmed       = {pmid:25346682},
      doi          = {10.3389/fninf.2014.00078},
      url          = {https://juser.fz-juelich.de/record/172421},
}