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@INPROCEEDINGS{Lober:1033746,
      author       = {Lober, Melissa and Diesmann, Markus and Kunkel, Susanne},
      title        = {{E}xploiting network structure in {NEST}: {E}fficient
                      communication in brain-scale simulations},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2024-06597},
      year         = {2024},
      abstract     = {The communication of spike events constitutes a major
                      bottleneck in simulations of brain-scale networks with
                      realistic connectivity. Models such as the multi-area model1
                      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 dynamics2. 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 NEST3.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] Schmidt et
                      al., PLoS Comput Biol, 14(10), 1-38, 2018[2] Morrison $\&$
                      Diesmann, Springer Berlin Heidelberg, pp 267-278, 2008[3]
                      Gewaltig $\&$ Diesmann, Scholarpedia 2(4):1430 , 2007},
      month         = {Jun},
      date          = {2024-06-17},
      organization  = {NEST Conference 2024, virtual
                       (virtual), 17 Jun 2024 - 18 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-2024-06597},
      url          = {https://juser.fz-juelich.de/record/1033746},
}