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@INPROCEEDINGS{Haensel:889149,
      author       = {Haensel, David and Morgenstern, Laura and Beckmann, Andreas
                      and Kabadshow, Ivo and Dachsel, Holger},
      title        = {{E}ventify: {E}vent-{B}ased {T}ask {P}arallelism for
                      {S}trong {S}caling},
      publisher    = {ACM New York, NY, USA},
      reportid     = {FZJ-2021-00074},
      pages        = {1-10},
      year         = {2020},
      comment      = {Proceedings of the Platform for Advanced Scientific
                      Computing Conference - ACM New York, NY, USA, 2020. - ISBN
                      9781450379939 - doi:10.1145/3394277.3401858},
      booktitle     = {Proceedings of the Platform for
                       Advanced Scientific Computing
                       Conference - ACM New York, NY, USA,
                       2020. - ISBN 9781450379939 -
                       doi:10.1145/3394277.3401858},
      abstract     = {Today's processors become fatter, not faster. However, the
                      exploitation of these massively parallel compute resources
                      remains a challenge for many traditional HPC applications
                      regarding scalability, portability and programmability. To
                      tackle this challenge, several parallel programming
                      approaches such as loop parallelism and task parallelism are
                      researched in form of languages, libraries and frameworks.
                      Task parallelism as provided by OpenMP, HPX, StarPU, Charm++
                      and Kokkos is the most promising approach to overcome the
                      challenges of ever increasing parallelism. The
                      aforementioned parallel programming technologies enable
                      scalability for a broad range of algorithms with
                      coarse-grained tasks, e. g. in linear algebra and classical
                      N-body simulation. However, they do not fully address the
                      performance bottlenecks of algorithms with fine-grained
                      tasks and the resultant large task graphs. Additionally, we
                      experienced the description of large task graphs to be
                      cumbersome with the common approach of providing in-, out-
                      and inout-dependencies. We introduce event-based task
                      parallelism to solve the performance and programmability
                      issues for algorithms that exhibit fine-grained task
                      parallelism and contain repetitive task patterns. With
                      user-defined event lists, the approach provides a more
                      convenient and compact way to describe large task graphs.
                      Furthermore, we show how these event lists are processed by
                      a task engine that reuses user-defined, algorithmic data
                      structures. As use case, we describe the implementation of a
                      fast multipole method for molecular dynamics with
                      event-based task parallelism. The performance analysis
                      reveals that the event-based implementation is 52 $\%$
                      faster than a classical loop-parallel implementation with
                      OpenMP.},
      month         = {Jun},
      date          = {2020-06-29},
      organization  = {PASC '20: Platform for Advanced
                       Scientific Computing Conference, Geneva
                       (Switzerland), 29 Jun 2020 - 1 Jul
                       2020},
      cin          = {JSC / IAS-7},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-7-20180321},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1145/3394277.3401858},
      url          = {https://juser.fz-juelich.de/record/889149},
}