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@ARTICLE{Sharples:851425,
      author       = {Sharples, Wendy and Zhukov, Ilya and Geimer, Markus and
                      Görgen, Klaus and Lührs, Sebastian and Breuer, Thomas and
                      Naz, Bibi and Kulkarni, Ketan and Brdar, Slavko and Kollet,
                      Stefan},
      title        = {{A} run control framework to streamline profiling, porting,
                      and tuning simulation runs and provenance tracking of
                      geoscientific applications},
      journal      = {Geoscientific model development},
      volume       = {11},
      number       = {7},
      issn         = {1991-9603},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2018-05072},
      pages        = {2875 - 2895},
      year         = {2018},
      abstract     = {Geoscientific modeling is constantly evolving, with
                      next-generation geoscientific models and applications
                      placing large demands on high-performance computing (HPC)
                      resources. These demands are being met by new developments
                      in HPC architectures, software libraries, and
                      infrastructures. In addition to the challenge of new
                      massively parallel HPC systems, reproducibility of
                      simulation and analysis results is of great concern. This is
                      due to the fact that next-generation geoscientific models
                      are based on complex model implementations and profiling,
                      modeling, and data processing workflows. Thus, in order to
                      reduce both the duration and the cost of code migration, aid
                      in the development of new models or model components, while
                      ensuring reproducibility and sustainability over the
                      complete data life cycle, an automated approach to
                      profiling, porting, and provenance tracking is necessary. We
                      propose a run control framework (RCF) integrated with a
                      workflow engine as a best practice approach to automate
                      profiling, porting, provenance tracking, and simulation
                      runs. Our RCF encompasses all stages of the modeling chain:
                      (1) preprocess input, (2) compilation of code (including
                      code instrumentation with performance analysis tools), (3)
                      simulation run, and (4) postprocessing and analysis, to
                      address these issues. Within this RCF, the workflow engine
                      is used to create and manage benchmark or simulation
                      parameter combinations and performs the documentation and
                      data organization for reproducibility. In this study, we
                      outline this approach and highlight the subsequent
                      developments scheduled for implementation born out of the
                      extensive profiling of ParFlow. We show that in using our
                      run control framework, testing, benchmarking, profiling, and
                      running models is less time consuming and more robust than
                      running geoscientific applications in an ad hoc fashion,
                      resulting in more efficient use of HPC resources, more
                      strategic code development, and enhanced data integrity and
                      reproducibility.},
      cin          = {IBG-3 / JSC / JARA-HPC},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / I:(DE-Juel1)JSC-20090406 /
                      $I:(DE-82)080012_20140620$},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / 511 - Computational Science and Mathematical
                      Methods (POF3-511) / EoCoE - Energy oriented Centre of
                      Excellence for computer applications (676629) / POP -
                      Performance Optimisation and Productivity (676553) /
                      Scalable Performance Analysis of Large-Scale Parallel
                      Applications $(jzam11_20091101)$ / Water4Enery
                      $(jibg31_20160501)$ / ATMLPP - ATML Parallel Performance
                      (ATMLPP) / ATMLAO - ATML Application Optimization and User
                      Service Tools (ATMLAO)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)POF3-511 /
                      G:(EU-Grant)676629 / G:(EU-Grant)676553 /
                      $G:(DE-Juel1)jzam11_20091101$ /
                      $G:(DE-Juel1)jibg31_20160501$ / G:(DE-Juel-1)ATMLPP /
                      G:(DE-Juel-1)ATMLAO},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000438637700005},
      doi          = {10.5194/gmd-11-2875-2018},
      url          = {https://juser.fz-juelich.de/record/851425},
}