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@INPROCEEDINGS{Kurth:889327,
      author       = {Kurth, Anno and Finnerty, Justin and Terhorst, Dennis and
                      Pronold, Jari and Senk, Johanna and Diesmann, Markus},
      title        = {{S}ub {R}ealtime {S}imulation of a {F}ull {D}ensity
                      {C}ortical {M}icrocircuit {M}odel on a {S}ingle {C}ompute
                      {N}ode},
      reportid     = {FZJ-2021-00218},
      year         = {2020},
      abstract     = {The local cortical microcircuit is a building block of the
                      mammalian brain. Its prominent characteristics are similar
                      across species and cortical areas. This dual homogeneity
                      raises hopes that principles of cortical computation can be
                      discovered. The tissue below a 1 mm2 patch of cortex
                      comprises about 100,000 neurons. It is the smallest network
                      in which both a realistic number of about 10,000 synapses
                      per neuron and a connection probability of 0.1 are realized
                      simultaneously. Potjans and Diesmann [1] compiled a
                      prototype network model of the microcircuit. In this model
                      the spatial structure is neglected and replaced by cell-type
                      specific random connectivity. Each layer is represented by
                      an excitatory and inhibitory population of
                      integrate-and-fire model neurons. The circuit has become a
                      benchmark network for neuromorphic computing systems: its
                      natural size renders questions of downscaling irrelevant
                      [2], it can routinely be simulated by present systems [3,4],
                      and it marks an upper bound from which neuronal networks in
                      nature are necessarily less densely connected.To apply
                      neuronal networks with natural densities in neurorobotics,
                      their simulation needs to become faster. The same holds true
                      for the investigation of the long time scales of
                      system-level learning. Achieving this is a promise of
                      neuromorphic computing. The first intermediate objective is
                      real-time performance, accomplished for the microcircuit
                      only recently [4]. However, these results have to be
                      evaluated in the light of continuously advancing mainstream
                      architectures as they provide more flexibility at
                      potentially lower costs.In this contribution we show
                      performance data for the microcircuit model on two recent
                      AMD EPYC Rome 128 core compute nodes coupled by a
                      point-to-point Infiniband interconnect. The software is the
                      NEST 2.14 [5] (including cherry picked bug fix
                      726f9b04bbd47c) simulation code providing double precision
                      numerics and weight resolution. On a single compute node we
                      measure sub realtime performance. With two nodes the
                      simulation is 1.7 times faster than realtime. For the single
                      node the energy per synaptic event is 0.26 μJ, and for the
                      fastest configuration using two nodes 0.39 μJ. These values
                      are in the same order of magnitude as the lowest reported
                      for a simulation of the microcircuit model so far [6]. Our
                      study exposes present bottlenecks and can guide the design
                      of future software and hardware systems.},
      month         = {Sep},
      date          = {2020-09-29},
      organization  = {Bernstein Conference 2020, Online
                       (Germany), 29 Sep 2020 - 1 Oct 2020},
      subtyp        = {Other},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) /
                      Advanced Computing Architectures $(aca_20190115)$ / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF3-574 / $G:(DE-Juel1)aca_20190115$ /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/889327},
}