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@ARTICLE{Golosio:1014311,
      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},
      journal      = {Applied Sciences},
      volume       = {13},
      number       = {17},
      issn         = {2076-3417},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2023-03233},
      pages        = {9598, 29 pages},
      year         = {2023},
      note         = {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},
      ddc          = {600},
      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)16},
      UT           = {WOS:001062652800001},
      doi          = {10.3390/app13179598},
      url          = {https://juser.fz-juelich.de/record/1014311},
}