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@ARTICLE{Ippen:830181,
      author       = {Ippen, Tammo and Eppler, Jochen M. and Plesser, Hans E. and
                      Diesmann, Markus},
      title        = {{C}onstructing {N}euronal {N}etwork {M}odels in {M}assively
                      {P}arallel {E}nvironments},
      journal      = {Frontiers in neuroinformatics},
      volume       = {11},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2017-03757},
      pages        = {30},
      year         = {2017},
      abstract     = {Recent advances in the development of data structures to
                      represent spiking neuron network models enable us to exploit
                      the complete memory of petascale computers for a single
                      brain-scale network simulation. In this work, we investigate
                      how well we can exploit the computing power of such
                      supercomputers for the creation of neuronal networks. Using
                      an established benchmark, we divide the runtime of
                      simulation code into the phase of network construction and
                      the phase during which the dynamical state is advanced in
                      time. We find that on multi-core compute nodes network
                      creation scales well with process-parallel code but exhibits
                      a prohibitively large memory consumption. Thread-parallel
                      network creation, in contrast, exhibits speedup only up to a
                      small number of threads but has little overhead in terms of
                      memory. We further observe that the algorithms creating
                      instances of model neurons and their connections scale well
                      for networks of ten thousand neurons, but do not show the
                      same speedup for networks of millions of neurons. Our work
                      uncovers that the lack of scaling of thread-parallel network
                      creation is due to inadequate memory allocation strategies
                      and demonstrates that thread-optimized memory allocators
                      recover excellent scaling. An analysis of the loop order
                      used for network construction reveals that more complex
                      tests on the locality of operations significantly improve
                      scaling and reduce runtime by allowing construction
                      algorithms to step through large networks more efficiently
                      than in existing code. The combination of these techniques
                      increases performance by an order of magnitude and harnesses
                      the increasingly parallel compute power of the compute nodes
                      in high-performance clusters and supercomputers.},
      cin          = {INM-6 / IAS-6 / INM-10 / JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)JSC-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 511 -
                      Computational Science and Mathematical Methods (POF3-511) /
                      Brain-Scale Simulations $(jinb33_20121101)$ / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP - The Human Brain Project
                      (604102) / HBP SGA1 - Human Brain Project Specific Grant
                      Agreement 1 (720270) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-511 /
                      $G:(DE-Juel1)jinb33_20121101$ /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)604102 /
                      G:(EU-Grant)720270 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000401368100001},
      pubmed       = {pmid:28559808},
      doi          = {10.3389/fninf.2017.00030},
      url          = {https://juser.fz-juelich.de/record/830181},
}