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@INPROCEEDINGS{Cumming:840405,
      author       = {Cumming, Ben and Yates, Stuart and Klijn, Wouter and
                      Peyser, Alexander and Karakasis, Vasileios and Perez, Ivan
                      Martinez},
      title        = {{A}rbor: {A} morphologically detailed neural network
                      simulator for modern high performance computer
                      architectures},
      reportid     = {FZJ-2017-07937},
      year         = {2017},
      abstract     = {Arbor is a new multicompartment neural network simulator
                      currently under development as a collaboration between the
                      Neuroscience SimLab at the Forschungszentrum Jülich,
                      Barcelona Supercomputing Center and the Swiss National
                      Supercomputing Center. Arbor will enable new scales and
                      classes of morphologically detailed neuronal network
                      simulations on current and future supercomputing
                      architectures such as the Human Brain Project SCs. A number
                      of many-core architectures such as GPU and Intel Xeon Phi
                      based systems are available. To optimally use these emerging
                      architectures, new approaches in software development are
                      needed. Arbor is being written specifically with
                      performance for this hardware in mind 1; it aims to be a
                      flexible platform for neural network simulation while
                      keeping interoperability with models and workflows
                      developed for NEST and NEURON. Improvements in performance
                      and flexibility will enable a variety of novel experiments.
                      The design is not yet finalized and is driven by the
                      requirements of the neuroscientific community. The
                      prototype is open source
                      (https://github.com/eth-cscs/nestmc-proto). Build this next
                      generation neurosimulator together with us! • Simulate
                      large morphological detailed networks for longer time
                      scales: Study slowly developing phenomena.• Reduce the
                      time to solution: Perform more repeat experiments for
                      increased statistical power. • Create high performance
                      interfaces with other software: Perform online statistical
                      analysis and visualization of your running models, study the
                      brain at multiple scales with specialized tools or embed
                      detailed networks in physically modelled animals. •
                      Optimize dynamic structures for models with time-varying
                      number of neurons, synapses and compartments: simulate
                      neuronal development, healing after injury and age related
                      neuronal degeneration.},
      month         = {Nov},
      date          = {2017-11-15},
      organization  = {Neuroscience 2017: Society for
                       Neuroscience, Washington, DC (United
                       States), 15 Nov 2017 - 15 Nov 2017},
      subtyp        = {Other},
      cin          = {JSC / JARA-HPC},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain
                      Project Specific Grant Agreement 1 (720270) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)720270 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/840405},
}