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@INPROCEEDINGS{Woodman:836543,
      author       = {Woodman, Marmaduke and Diaz, Sandra and Peyser, Alexander},
      title        = {{A}utomatically generating {HPC}-optimized code for
                      simulations using neural mass models},
      reportid     = {FZJ-2017-05648},
      year         = {2017},
      abstract     = {High performance computing is becoming every day a more
                      accessible and desirable concept for researchers in
                      neuroscience. Simulations of brain networks and analysis of
                      medical data can now be performed on larger scales and with
                      higher resolution. However many software tools which are
                      currently available to neuroscientists are not yet capable
                      of utilizing the full power of supercomputers, GPGPUs and
                      other computational accelerators. The Virtual Brain (TVB)[1]
                      software is a validated and popular choice for the
                      simulation of whole brain activity. With TVB the user can
                      create simulations using neural mass models which can have
                      outputs on different experimental modalities (EEG, MEG,
                      fMRI, etc). TVB allows the scientists to explore and analyze
                      simulated and experimental signals and contains tools to
                      evaluate relevant scientific parameters over both types of
                      data[2]. Internally, the TVB simulator contains several
                      models for the generation of neural activity at the region
                      scale. Most of these neural mass models can be efficiently
                      described with groups of coupled differential equations
                      which are numerically solved for large spans of simulation
                      time. Currently, the models simulated in TVB are written in
                      Python and have not been optimized for parallel execution or
                      deployment on High Performance Computing architectures.
                      Moreover, several elements of these models can be
                      abstracted, generalized and re-utilized, but the design for
                      the right abstract description for the models is still
                      missing. In this work we want to present the first results
                      of porting several workflows from The Virtual Brain into
                      High Performance Computing accelerators. In order to reduce
                      the effort required by neuroscientist to utilize different
                      HPC platforms, we have developed an automatic code
                      generation tool which can be used to define abstract models
                      at all stages of a simulation. These models are then
                      translated into hardware specific code. Our simulation
                      workflows involve different neural mass models (Kuramoto
                      [3], Reduced Wong Wang [4], etc ), pre-processing and
                      post-processing kernels (ballon model [5], correlation
                      metrics, etc). We discuss the strategies used to keep the
                      code portable through several architectures but optimized to
                      each platform. We also point out the benefits and
                      limitations of this approach. Finally we show initial
                      performance comparisons and give the user an idea of what
                      can be achieved with the new code in terms of scalability
                      and simulation times. AcknowledgementsWe would like to thank
                      our collaborators Lia Domide, Mihai Andrei, Vlad Prunar for
                      their work on the integration of the new software with the
                      already existing TVB platform as well as Petra Ritter and
                      Michael Schirner for providing an initial use case for our
                      tests. The authors would also like to acknowledge the
                      support by the Excellence Initiative of the German federal
                      and state governments, the Jülich Aachen Research Alliance
                      CRCNS grant and the Helmholtz Association through the
                      portfolio theme SMHB and the Initiative and Networking Fund.
                      In addition, this project has received funding from the
                      European Union's Horizon 2020 research and innovation
                      programme under grant agreement No 720270 (HBP SGA1).},
      month         = {Jul},
      date          = {2017-07-25},
      organization  = {26th Computational Neuroscience
                       Meeting, Antwerp (Belgium), 25 Jul 2017
                       - 25 Jul 2017},
      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) / Virtual Connectomics -
                      Deutschland - USA Zusammenarbeit in Computational Science:
                      Mechanistische Zusammenhänge zwischen Struktur und
                      funktioneller Dynamik im menschlichen Gehirn
                      (BMBF-01GQ1504B) / HBP SGA1 - Human Brain Project Specific
                      Grant Agreement 1 (720270) / 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)BMBF-01GQ1504B
                      / G:(EU-Grant)720270 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/836543},
}