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@INPROCEEDINGS{Klijn:825603,
      author       = {Klijn, Wouter and Cumming, Benjamin and Karakasis,
                      Vasileios and Peyser, Alexander and Yates, Stuart},
      title        = {{N}idus by {NEST}: {A} morphologically detailed neural
                      network simulator for many core high performance computer
                      architectures},
      reportid     = {FZJ-2016-08048},
      year         = {2016},
      abstract     = {The Nidus multicompartment neural network simulator will
                      enable new scales and classes of morphologically detailed
                      network simulations on current and future supercomputing
                      architectures. Nidus is being developed as a collaboration
                      between the Neuroscience SimLab at the Forschungszentrum
                      Juelich and the Swiss National Supercomputing Center (CSCS)
                      under the aegis of the NEST Initiative. The trend towards
                      "many-core" architectures such as GPU and Intel Xeon Phi
                      based systems demands new approaches in software development
                      and algorithm design. Nidus is being written specifically
                      for these architectures; it aims to be a flexible platform
                      for neural network simulation, interoperable with the models
                      and workflows of NEST and NEURON.Improvements in performance
                      and flexibility will enable a variety of novel experiments,
                      but the design isn't finalised, and will be driven by the
                      requirements of the community. This is where you come in! We
                      are very interested in your ideas for features which will
                      make new science possible: we ask you to think outside of
                      the box and build this next generation neurosimulator
                      together with us.Possible features and use cases:o Simulate
                      significantly larger networks over longer time scales -
                      Larger proportion of CNS systems with morphological detail -
                      Longer simulations for slowly developing phenomenon -
                      Improved statistical power by leveraging large data setso A
                      well defined high performance C++ API which allows tight
                      integration with other codes - Multiscale by coupling with
                      simulations at other scales - Real-time visualization on HPC
                      resources - Online statistics to avoid scaling bottlenecks -
                      Networks embedded in physically modeled animalso Dynamic
                      data structures which allow the creation of models with a
                      time-varying number of neurons, synapses and compartments -
                      Neuronal development - Healing after injury - Age related
                      neuronal degeneration.What questions haven't you asked
                      yet?AcknowledgementsWe would like to thank the following
                      organizations for their support: Helmholtz Portfolio Theme
                      "Supercomputing and Modeling for the Human Brain", Human
                      Brain Project SP7 High Performance Analytics and Computing
                      Platform, and the Jülich-Aachen Research Alliance},
      month         = {Oct},
      date          = {2016-10-12},
      organization  = {Human Brain Project Summit 2016,
                       Florence (Italy), 12 Oct 2016 - 15 Oct
                       2016},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 574 - Theory, modelling and simulation
                      (POF3-574) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/825603},
}