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@ARTICLE{Golosio:1014310,
      author       = {Golosio, Bruno and Villamar, Jose and Tiddia, Gianmarco and
                      Pastorelli, Elena and Stapmanns, Jonas and Fanti, Viviana
                      and Paolucci, Pier Stanislao and Morrison, Abigail and Senk,
                      Johanna},
      title        = {{R}untime {C}onstruction of {L}arge-{S}cale {S}piking
                      {N}euronal {N}etwork {M}odels on {GPU} {D}evices},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-03232},
      year         = {2023},
      note         = {29 pages, 9 figures. This project was also funded by the
                      Italian PNRR MUR project PE0000013-FAIR, funded by
                      NextGenerationEU.},
      abstract     = {Simulation speed matters for neuroscientific research: this
                      includes not only how quickly the simulated model time of a
                      large-scale spiking neuronal network progresses, but also
                      how long it takes to instantiate the network model in
                      computer memory.On the hardware side, acceleration via
                      highly parallel GPUs is being increasingly utilized.On the
                      software side, code generation approaches ensure highly
                      optimized code, at the expense of repeated code regeneration
                      and recompilation after modifications to the network
                      model.Aiming for a greater flexibility with respect to
                      iterative model changes, here we propose a new method for
                      creating network connections interactively, dynamically, and
                      directly in GPU memory through a set of commonly used
                      high-level connection rules.We validate the simulation
                      performance with both consumer and data center GPUs on two
                      neuroscientifically relevant models:a cortical microcircuit
                      of about 77,000 leaky-integrate-and-fire neuron models and
                      300 million static synapses, and a two-population network
                      recurrently connected using a variety of connection
                      rules.With our proposed ad hoc network instantiation, both
                      network construction and simulation times are comparable or
                      shorter than those obtained with other state-of-the-art
                      simulation technologies, while still meeting the flexibility
                      demands of explorative network modeling.},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / 5235 -
                      Digitization of Neuroscience and User-Community Building
                      (POF4-523) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / MetaMoSim - Generic metadata
                      management for reproducible high-performance-computing
                      simulation workflows - MetaMoSim (ZT-I-PF-3-026) / JL SMHB -
                      Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / Brain-Scale Simulations
                      $(jinb33_20220812)$ / ICEI - Interactive Computing
                      E-Infrastructure for the Human Brain Project (800858) / DFG
                      project 491111487 - Open-Access-Publikationskosten / 2022 -
                      2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5235 /
                      G:(EU-Grant)945539 / G:(DE-Juel-1)ZT-I-PF-3-026 /
                      G:(DE-Juel1)JL SMHB-2021-2027 /
                      $G:(DE-Juel1)jinb33_20220812$ / G:(EU-Grant)800858 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.34734/FZJ-2023-03232},
      url          = {https://juser.fz-juelich.de/record/1014310},
}