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@INPROCEEDINGS{Villamar:1014309,
      author       = {Villamar, Jose and Golosio, Bruno and Tiddia, Gianmarco and
                      Pastorelli, Elena and Stapmanns, Jonas and Fanti, Viviana
                      and Diesmann, Markus and Paolucci, Pier Stanislao and
                      Morrison, Abigail and Senk, Johanna},
      title        = {{A}ccelerating {N}euronal {N}etwork {C}onstruction through
                      {D}ynamic {GPU} {M}emory {I}nstantiation},
      reportid     = {FZJ-2023-03231},
      year         = {2023},
      note         = {This project was also funded by the Italian PNRR MUR
                      project PE0000013-FAIR, funded by NextGenerationEU.},
      abstract     = {Efficient simulation of large-scale spiking neuronal
                      networks is important for neuroscientific research, and both
                      the simulation speed and the time it takes to instantiate
                      the network in computer memory are key factors. In recent
                      years, hardware acceleration through highly parallel GPUs
                      has become increasingly popular. Similarly, code generation
                      approaches have been utilized to optimize software
                      performance, albeit at the cost of repeated code
                      regeneration and recompilation after modifications to the
                      network model [1].To address the need for greater
                      flexibility in iterative model changes, we propose a new
                      method for creating network connections dynamically and
                      directly in GPU memory. This method uses a set of commonly
                      used high-level connection rules [2], enabling interactive
                      network construction.We validate the simulation performance
                      with both consumer and data center GPUs on a cortical
                      microcircuit of about 77,000 leaky-integrate-and-fire neuron
                      models and 300 million synapses [3], and a two-population
                      recurrently connected network designed to allow benchmarking
                      of a variety of connection rules.We implement our proposed
                      method in NEST GPU [4,5] and demonstrate the same or shorter
                      network construction and simulation times compared to other
                      state-of-the-art simulation technologies. Moreover, our
                      approach meets the flexibility demands of explorative
                      network modeling by enabling direct and dynamic changes to
                      the network in GPU memory.[1] Knight, J.C.; Nowotny, T. GPUs
                      Outperform Current HPC and Neuromorphic Solutions in Terms
                      of Speed and Energy When Simulating a Highly-Connected
                      Cortical Model. Frontiers in Neuroscience 2018, 12.
                      https://doi.org/10.3389/fnins.2018.00941.[2] Senk, J.;
                      Kriener, B.; Djurfeldt, M.; Voges, N.; Jiang, H.J.;
                      Schüttler, L.; Gramelsberger, G.; Diesmann, M.; Plesser,
                      H.E.; van Albada, S.J. Connectivity concepts in neuronal
                      network modeling. PLOS Computational Biology 2022, 18,
                      e1010086. https://doi.org/10.1371/journal.pcbi.1010086.[3]
                      Potjans, T.C.; Diesmann, M. The Cell-Type Specific Cortical
                      Microcircuit: Relating Structure and Activity in a
                      Full-Scale Spiking Network Model. Cerebral Cortex 2014, 24,
                      785–806. https://doi.org/10.1093/cercor/bhs358.[4]
                      Golosio, B.; Tiddia, G.; De Luca, C.; Pastorelli, E.;
                      Simula, F.; Paolucci, P.S. Fast Simulations of
                      Highly-Connected Spiking Cortical Models Using GPUs.
                      Frontiers in Computational Neuroscience 2021, 15.
                      https://doi.org/10.3389/fncom.2021.627620.[5] Tiddia, G.;
                      Golosio, B.; Albers, J.; Senk, J.; Simula, F.; Pronold, J.;
                      Fanti, V.; Pastorelli, E.; Paolucci, P.S.; van Albada, S.J.
                      Fast Simulation of a Multi-Area Spiking Network Model of
                      Macaque Cortex on an MPI-GPU Cluster. Frontiers in
                      Neuroinformatics 2022, 16.
                      https://doi.org/10.3389/fninf.2022.883333.},
      month         = {Jun},
      date          = {2023-06-15},
      organization  = {NEST Conference, Virtual (Germany), 15
                       Jun 2023 - 16 Jun 2023},
      subtyp        = {After Call},
      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)},
      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},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1014309},
}