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@INPROCEEDINGS{Speck:901885,
      author       = {Speck, Robert and Knobloch, Michael and Lührs, Sebastian
                      and Gocht, Andreas},
      title        = {{U}sing {P}erformance {A}nalysis {T}ools for a
                      {P}arallel-in-{T}ime {I}ntegrator},
      volume       = {356},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2021-03893},
      isbn         = {978-3-030-75932-2 (print)},
      series       = {Springer Proceedings in Mathematics $\&$ Statistics},
      pages        = {51 - 80},
      year         = {2021},
      comment      = {Parallel-in-Time Integration Methods},
      booktitle     = {Parallel-in-Time Integration Methods},
      abstract     = {While many ideas and proofs of concept for parallel-in-time
                      integration methods exists, the number of large-scale,
                      accessible time-parallel codes is rather small. This is
                      often due to the apparent or subtle complexity of the
                      algorithms and the many pitfalls awaiting developers of
                      parallel numerical software. One example of such a
                      time-parallel code is pySDC, which implements, among others,
                      the parallel full approximation scheme in space and time
                      (PFASST). Inspired by nonlinear multigrid ideas, PFASST
                      allows to integrate multiple time steps simultaneously using
                      a space-time hierarchy of spectral deferred corrections. In
                      this paper, we demonstrate the application of performance
                      analysis tools to the PFASST implementation pySDC. We trace
                      the path we took for this work, show examples of how the
                      tools can be applied, and explain the sometimes surprising
                      findings we encountered. Although focusing only on a single
                      implementation of a particular parallel-in-time integrator,
                      we hope that our results and in particular the way we
                      obtained them are a blueprint for other time-parallel
                      codes.},
      month         = {Jun},
      date          = {2020-06-08},
      organization  = {9th Workshop on Parallel-in-Time
                       Integration, online (online), 8 Jun
                       2020 - 12 Jun 2020},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / DFG project 450829162 -
                      Raum-Zeit-parallele Simulation multimodale Energiesystemen
                      (450829162) / ATMLPP - ATML Parallel Performance (ATMLPP)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(GEPRIS)450829162 /
                      G:(DE-Juel-1)ATMLPP},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000696174000003},
      doi          = {10.1007/978-3-030-75933-9_3},
      url          = {https://juser.fz-juelich.de/record/901885},
}