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@ARTICLE{Feldotto:907815,
      author       = {Feldotto, Benedikt and Eppler, Jochen Martin and
                      Jimenez-Romero, Cristian and Bignamini, Christopher and
                      Gutierrez, Carlos Enrique and Albanese, Ugo and Retamino,
                      Eloy and Vorobev, Viktor and Zolfaghari, Vahid and Upton,
                      Alex and Sun, Zhe and Yamaura, Hiroshi and Heidarinejad,
                      Morteza and Klijn, Wouter and Morrison, Abigail and Cruz,
                      Felipe and McMurtrie, Colin and Knoll, Alois C. and
                      Igarashi, Jun and Yamazaki, Tadashi and Doya, Kenji and
                      Morin, Fabrice O.},
      title        = {{D}eploying and {O}ptimizing {E}mbodied {S}imulations of
                      {L}arge-{S}cale {S}piking {N}eural {N}etworks on {HPC}
                      {I}nfrastructure},
      journal      = {Frontiers in neuroinformatics},
      volume       = {16},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2022-02232},
      pages        = {884180},
      year         = {2022},
      abstract     = {Simulating the brain-body-environment trinity in closed
                      loop is an attractive proposal to investigate how
                      perception, motor activity and interactions with the
                      environment shape brain activity, and vice versa. The
                      relevance of this embodied approach, however, hinges
                      entirely on the modeled complexity of the various simulated
                      phenomena. In this article, we introduce a software
                      framework that is capable of simulating large-scale,
                      biologically realistic networks of spiking neurons embodied
                      in a biomechanically accurate musculoskeletal system that
                      interacts with a physically realistic virtual environment.
                      We deploy this framework on the high performance computing
                      resources of the EBRAINS research infrastructure and we
                      investigate the scaling performance by distributing
                      computation across an increasing number of interconnected
                      compute nodes. Our architecture is based on requested
                      compute nodes as well as persistent virtual machines; this
                      provides a high-performance simulation environment that is
                      accessible to multi-domain users without expert knowledge,
                      with a view to enable users to instantiate and control
                      simulations at custom scale via a web-based graphical user
                      interface. Our simulation environment, entirely open source,
                      is based on the Neurorobotics Platform developed in the
                      context of the Human Brain Project, and the NEST simulator.
                      We characterize the capabilities of our parallelized
                      architecture for large-scale embodied brain simulations
                      through two benchmark experiments, by investigating the
                      effects of scaling compute resources on performance defined
                      in terms of experiment runtime, brain instantiation and
                      simulation time. The first benchmark is based on a
                      large-scale balanced network, while the second one is a
                      multi-region embodied brain simulation consisting of more
                      than a million neurons and a billion synapses. Both
                      benchmarks clearly show how scaling compute resources
                      improves the aforementioned performance metrics in a
                      near-linear fashion. The second benchmark in particular is
                      indicative of both the potential and limitations of a highly
                      distributed simulation in terms of a trade-off between
                      computation speed and resource cost. Our simulation
                      architecture is being prepared to be accessible for everyone
                      as an EBRAINS service, thereby offering a community-wide
                      tool with a unique workflow that should provide momentum to
                      the investigation of closed-loop embodiment within the
                      computational neuroscience community.},
      cin          = {JSC / INM-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) / HBP SGA2
                      - Human Brain Project Specific Grant Agreement 2 (785907) /
                      ICEI - Interactive Computing E-Infrastructure for the Human
                      Brain Project (800858) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS) / 5234 - Emerging NC Architectures
                      (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907 / G:(EU-Grant)800858 /
                      G:(DE-Juel1)Helmholtz-SLNS / G:(DE-HGF)POF4-5234},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35662903},
      UT           = {WOS:000805555900001},
      doi          = {10.3389/fninf.2022.884180},
      url          = {https://juser.fz-juelich.de/record/907815},
}