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@INPROCEEDINGS{Lober:1033747,
      author       = {Lober, Melissa and Diesmann, Markus and Kunkel, Susanne},
      title        = {{E}xploiting network topology in brain-scale multi-area
                      model simulations},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2024-06598},
      year         = {2024},
      abstract     = {The communication of spike events constitutes a major
                      bottleneck in simulations of brain-scale networks with
                      realistic connectivity, as for example, the multi-area model
                      of macaque visual cortex (Schmidt, 2018). The model consists
                      of 32 cortical microcircuits, each representing a downscaled
                      area of the visual cortex at the resolution of single
                      neurons and synapses. Such models not only have a dense
                      connectivity within areas but also between areas. Synaptic
                      transmission delays within an area can be as short as 0.1 ms
                      and therefore simulations require frequent spike
                      communication between compute nodes to maintain causality in
                      the network dynamics (Morrison, 2008). This poses a
                      challenge to the conventional round-robin scheme used to
                      distribute neurons uniformly across compute nodes
                      disregarding the network’s specific topology.We targe­t
                      this challenge and propose a structure-aware neuron
                      distribution scheme along with a novel spike-communication
                      framework that exploits this approach in order to make
                      communication in large-scale distributed simulations more
                      efficient. In the structure-aware neuron distribution
                      scheme, neurons are placed on the hardware in a way that
                      mimics the network’s topology. Paired with a communication
                      framework that distinguishes local short delay intra-area
                      communication and global long delay inter-area
                      communication, the structure-aware approach minimizes the
                      costly global communication and thereby reduces simulation
                      time. Our prototype implementation is fully tested and was
                      developed within the neuronal simulator tool NEST (Gewaltig,
                      2007).We show that the new strategy significantly reduces
                      communication time in weak-scaling experiments and the
                      effect increases with an increasing number of compute
                      nodes.[1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M $\&$
                      van Albada SJ (2018) Multi-scale account of the network
                      structure of macaque visual cortex. Brain Structure and
                      Function, 223: 1409
                      https://doi.org/10.1007/s00429-017-1554-4[2] Schmidt M,
                      Bakker R, Shen K, Bezgin B, Diesmann M $\&$ van Albada SJ
                      (2018) A multi-scale layer-resolved spiking network model of
                      resting-state dynamics in macaque cortex. PLOS Computational
                      Biology, 14(9): e1006359.
                      https://doi.org/10.1371/journal.pcbi.1006359[3] Morrison A,
                      Diesmann M (2008) Maintaining Causality in Discrete Time
                      Neuronal Network Simulations. Springer Berlin Heidelberg, pp
                      267-278. $https://doi.org/10.1007/978-3-540-73159-7_10$ [4]
                      https://nest-simulator.readthedocs.io/enlatest[5] Gewaltig
                      M-O $\&$ Diesmann M (2007) NEST (Neural Simulation Tool)
                      Scholarpedia 2(4):1430},
      month         = {Jun},
      date          = {2024-06-25},
      organization  = {Federation of European Neuroscience
                       Societies, Vienna (Austria), 25 Jun
                       2024 - 29 Jun 2024},
      subtyp        = {After Call},
      cin          = {IAS-6 / PGI-15 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)PGI-15-20210701 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / BMBF 03ZU1106CB - NeuroSys:
                      Algorithm-Hardware Co-Design (Projekt C) - B
                      (BMBF-03ZU1106CB) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(DE-Juel1)BMBF-03ZU1106CB /
                      G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-06598},
      url          = {https://juser.fz-juelich.de/record/1033747},
}