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@MISC{Bode:1018654,
      author       = {Bode, Mathis and Göbbert, Jens Henrik and Windgassen,
                      Jonathan},
      title        = {{B}est {P}aper {A}ward at {ISAV} 2023},
      publisher    = {“ISAV 2023: In Situ Infrastructures for Enabling
                      Extreme-scale Analysis and Visualization” workshop},
      reportid     = {FZJ-2023-04957},
      year         = {2023},
      note         = {The paper is available online:
                      https://doi.org/10.1145/3624062.3624159},
      abstract     = {Mathis Bode, Jens Henrik Göbbert, Jonathan Windgassen and
                      their collaborators from Argonne National Laboratory (USA)
                      have won the Best Paper Award for their paper “Scaling
                      Computational Fluid Dynamics: In Situ Visualization of NekRS
                      using SENSEI”. It was presented at the “ISAV 2023: In
                      Situ Infrastructures for Enabling Extreme-scale Analysis and
                      Visualization” workshop, which took place in conjunction
                      with the SC23 on 13 November 2023 in Denver, Colorado,
                      USA.The team describes in their paper a novel pipeline for
                      in situ and in transit visualization and analysis utilizing
                      SENSEI, ADIOS2, and ParaView over Python. The aim is to
                      solve the dilemma having to choose between data accuracy or
                      decreasing the resolution for Computational Fluid Dynamics
                      on GPU-powered HPC systems. Their approach makes more
                      regular data snapshots directly from memory and thus
                      bypasses the pitfalls of checkpointing. The application
                      NekRS is a GPU-centric thermal-fluid simulation, which
                      showcases diverse in situ and in transit strategies.
                      Experiments on the Polaris and JUWELS Booster supercomputers
                      were conducted to demonstrate real-world implications, which
                      offered crucial insights how efficient data management can
                      be achieved without compromising accuracy.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)38},
      url          = {https://juser.fz-juelich.de/record/1018654},
}