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@ARTICLE{Tiddia:908939,
      author       = {Tiddia, Gianmarco and Golosio, Bruno and Albers, Jasper and
                      Senk, Johanna and Simula, Francesco and Pronold, Jari and
                      Fanti, Viviana and Pastorelli, Elena and Paolucci, Pier
                      Stanislao and van Albada, Sacha J.},
      title        = {{F}ast {S}imulation of a {M}ulti-{A}rea {S}piking {N}etwork
                      {M}odel of {M}acaque {C}ortex on an {MPI}-{GPU} {C}luster},
      journal      = {Frontiers in neuroinformatics},
      volume       = {16},
      issn         = {1662-5196},
      address      = {Lausanne},
      publisher    = {Frontiers Research Foundation},
      reportid     = {FZJ-2022-02914},
      pages        = {883333},
      year         = {2022},
      abstract     = {Spiking neural network models are increasingly establishing
                      themselves as an effective tool for simulating the dynamics
                      of neuronal populations and for understanding the
                      relationship between these dynamics and brain function.
                      Furthermore, the continuous development of parallel
                      computing technologies and the growing availability of
                      computational resources are leading to an era of large-scale
                      simulations capable of describing regions of the brain of
                      ever larger dimensions at increasing detail. Recently, the
                      possibility to use MPI-based parallel codes on GPU-equipped
                      clusters to run such complex simulations has emerged,
                      opening up novel paths to further speed-ups. NEST GPU is a
                      GPU library written in CUDA-C/C++ for large-scale
                      simulations of spiking neural networks, which was recently
                      extended with a novel algorithm for remote spike
                      communication through MPI on a GPU cluster. In this work we
                      evaluate its performance on the simulation of a multi-area
                      model of macaque vision-related cortex, made up of about 4
                      million neurons and 24 billion synapses and representing 32
                      mm2 surface area of the macaque cortex. The outcome of the
                      simulations is compared against that obtained using the
                      well-known CPU-based spiking neural network simulator NEST
                      on a high- performance computing cluster. The results show
                      not only an optimal match with the NEST statistical measures
                      of the neural activity in terms of three informative
                      distributions, but also remarkable achievements in terms of
                      simulation time per second of biological activity. Indeed,
                      NEST GPU was able to simulate a second of biological time of
                      the full- scale macaque cortex model in its metastable state
                      3.1× faster than NEST using 32 compute nodes equipped with
                      an NVIDIA V100 GPU each. Using the same configuration, the
                      ground state of the full-scale macaque cortex model was
                      simulated 2.4× faster than NEST.},
      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          = {5234 - Emerging NC Architectures (POF4-523) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / DFG project 347572269 - Heterogenität von
                      Zytoarchitektur, Chemoarchitektur und Konnektivität in
                      einem großskaligen Computermodell der menschlichen
                      Großhirnrinde (347572269) / ACA - Advanced Computing
                      Architectures (SO-092) / JL SMHB - Joint Lab Supercomputing
                      and Modeling for the Human Brain (JL SMHB-2021-2027) / ICEI
                      - Interactive Computing E-Infrastructure for the Human Brain
                      Project (800858) / Open-Access-Publikationskosten
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(EU-Grant)945539 /
                      G:(EU-Grant)785907 / G:(GEPRIS)347572269 / G:(DE-HGF)SO-092
                      / G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)800858 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35859800},
      UT           = {WOS:000828368200001},
      doi          = {10.3389/fninf.2022.883333},
      url          = {https://juser.fz-juelich.de/record/908939},
}