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@INPROCEEDINGS{Baumann:1028865,
      author       = {Baumann, Thomas and Speck, Robert},
      title        = {{F}rom {I}teration {C}ounting to {A}pplications with
                      py{SDC}},
      reportid     = {FZJ-2024-04851},
      year         = {2024},
      abstract     = {Many parallel-in-time (PinT) algorithms replace the serial
                      and, in this regard, direct way of time stepping by
                      algorithms that iterate on multiple time steps in
                      parallel.Efficient parallel implementation is a demanding
                      exercise and may exceed the scope of more math-based PinT
                      projects.Instead, PinT research typically resorts to
                      "counting iterations" of the algorithm as a means of
                      measuring its performance independently of its actual
                      implementation.However, this hides many non-negligible
                      sources of computational cost, such as communication.If the
                      true parallel efficiency of candidate algorithms is under
                      consideration, this needs to be obtained in a more coherent
                      way.We present here one prototyping library which aims to
                      cover both aspects of PinT research: method development and
                      fair efficiency testing.pySDC is a Python code that, while
                      not providing production-level performance, allows users to
                      detect pitfalls in parallel algorithms before committing to
                      optimized implementations or rigorous mathematical
                      analysis.Parallel efficiency can be estimated by comparing
                      to various time-serial algorithms implemented in the same
                      framework.The modular structure allows users to easily apply
                      a new algorithm to a wide range of problems and
                      configurations without awareness of all the intricacies of
                      the code.pySDC is publicly hosted on GitHub and well tested
                      and documented in order to provide new users with a smooth
                      and rapid start.},
      month         = {Mar},
      date          = {2024-03-05},
      organization  = {SIAM Parallel Processing, Baltimore
                       (USA), 5 Mar 2024 - 8 Mar 2024},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / TIME-X - TIME
                      parallelisation: for eXascale computing and beyond (955701)
                      / RGRSE - RG Research Software Engineering for HPC (RG RSE)
                      (RG-RSE)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)955701 /
                      G:(DE-Juel-1)RG-RSE},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2024-04851},
      url          = {https://juser.fz-juelich.de/record/1028865},
}