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@ARTICLE{Jordan:875222,
      author       = {Jordan, Jakob and Helias, Moritz and Diesmann, Markus and
                      Kunkel, Susanne},
      title        = {{E}fficient {C}ommunication in {D}istributed {S}imulations
                      of {S}piking {N}euronal {N}etworks {W}ith {G}ap {J}unctions},
      journal      = {Frontiers in neuroinformatics},
      volume       = {14},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2020-01876},
      pages        = {12},
      year         = {2020},
      abstract     = {Investigating the dynamics and function of large-scale
                      spiking neuronal networks with realistic numbers of synapses
                      is made possible today by state-of-the-art simulation code
                      that scales to the largest contemporary supercomputers.
                      However, simulations that involve electrical interactions,
                      also called gap junctions, besides chemical synapses scale
                      only poorly due to a communication scheme that collects
                      global data on each compute node. In comparison to chemical
                      synapses, gap junctions are far less abundant. To improve
                      scalability we exploit this sparsity by integrating an
                      existing framework for continuous interactions with a
                      recently proposed directed communication scheme for spikes.
                      Using a reference implementation in the NEST simulator we
                      demonstrate excellent scalability of the integrated
                      framework, accelerating large-scale simulations with gap
                      junctions by more than an order of magnitude. This allows,
                      for the first time, the efficient exploration of the
                      interactions of chemical and electrical coupling in
                      large-scale neuronal networks models with natural synapse
                      density distributed across thousands of compute nodes.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / MSNN -
                      Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)
                      / HBP - The Human Brain Project (604102) / HBP SGA1 - Human
                      Brain Project Specific Grant Agreement 1 (720270) / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      DEEP-EST - DEEP - Extreme Scale Technologies (754304) /
                      Advanced Computing Architectures $(aca_20190115)$ /
                      Brain-Scale Simulations $(jinb33_20121101)$},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(EU-Grant)604102 / G:(EU-Grant)720270 / G:(EU-Grant)785907
                      / G:(EU-Grant)754304 / $G:(DE-Juel1)aca_20190115$ /
                      $G:(DE-Juel1)jinb33_20121101$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32431602},
      UT           = {WOS:000536333100001},
      doi          = {10.3389/fninf.2020.00012},
      url          = {https://juser.fz-juelich.de/record/875222},
}