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@INPROCEEDINGS{Lober:1027274,
      author       = {Lober, Melissa and Diesmann, Markus and Kunkel, Susanne},
      title        = {{O}ptimizing communication in brain-scale multi-area model
                      simulations},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2024-03723},
      year         = {2024},
      abstract     = {With the field of dedicated hardware for neuronal
                      simulations growing rapidly over the past years,
                      conventional hardware still serves as an important reference
                      benchmark while maintaining more flexibility at potentially
                      lower cost. Kurth et al. [1] have shown that for a realistic
                      cortical microcircuit model neuronal simulation technology
                      for conventional hardware keeps pace with novel computing
                      architectures, such as SpiNNaker [2], regarding real-time
                      factor as well as energy efficiency. In this project we
                      develop and advance simulation technology for spiking neural
                      networks for conventional computer architectures, thereby
                      challenging and inspiring novel neuromorphic systems. 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 [3]. 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 [4]. 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 [5].For the
                      benchmarking of our approach, we developed a multi-area
                      model that resembles the macaque multi-area model in terms
                      of connectivity and work load, while being more easily
                      scalable as it retains constant activity levels. 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] Kurth et al.,
                      Neuromorph. Comput. Eng., 2, 021001, 2022[2] Furber $\&$
                      Bogdan, Boston-Delft: now publishers, 2020[3] Schmidt et
                      al., PLoS Comput Biol, 14(10), 1-38, 2018[4] Morrison $\&$
                      Diesmann, Springer Berlin Heidelberg, pp 267-278, 2008[5]
                      Gewaltig $\&$ Diesmann, Scholarpedia 2(4):1430 , 2007},
      month         = {Jun},
      date          = {2024-06-03},
      organization  = {International Conference on
                       Neuromorphic Computing and Engineering,
                       Aachen (Germany), 3 Jun 2024 - 6 Jun
                       2024},
      subtyp        = {After Call},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / 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-2024-03723},
      url          = {https://juser.fz-juelich.de/record/1027274},
}