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@ARTICLE{Sharples:841906,
      author       = {Sharples, Wendy and Zhukov, Ilya and Geimer, Markus and
                      Görgen, Klaus and Kollet, Stefan and Lührs, Sebastian and
                      Breuer, Thomas and Naz, Bibi and Kulkarni, Ketan and Brdar,
                      Slavko},
      title        = {{B}est practice regarding the three {P}'s: profiling,
                      portability and provenance when running {HPC} geoscientific
                      applications},
      journal      = {Geoscientific model development discussions},
      volume       = {242},
      issn         = {1991-9611},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2018-00203},
      pages        = {1 - 39},
      year         = {2017},
      abstract     = {Geoscientific modeling is constantly evolving, with next
                      generation geoscientific models and applications placing
                      high demands on high performance computing (HPC) resources.
                      These demands are being met by new developments in HPC
                      architectures, software libraries, and infrastructures. New
                      HPC developments require new programming paradigms leading
                      to substantial investment in model porting, tuning, and
                      refactoring of complicated legacy code in order to use these
                      resources effectively. In addition to the challenge of new
                      massively parallel HPC systems, reproducibility of
                      simulation and analysis results is of great concern, as the
                      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, a streamlined approach to
                      profiling, porting, and provenance tracking is necessary.We
                      propose a run control framework (RCF) integrated with a
                      workflow engine which encompasses all stages of the modeling
                      chain: 1. preprocess input, 2. compilation of code
                      (including code instrumentation with performance analysis
                      tools), 3. simulation run, 4. postprocess 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. This approach automates
                      the process of porting and tuning, profiling, testing, and
                      running a geoscientific model. We show that in using our run
                      control framework, testing, benchmarking, profiling, and
                      running models is less time consuming and more robust,
                      resulting in more efficient use of HPC resources, more
                      strategic code development, and enhanced data integrity and
                      reproducibility.},
      cin          = {IBG-3 / JSC},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 255 - Terrestrial Systems: From Observation to
                      Prediction (POF3-255) / EoCoE - Energy oriented Centre of
                      Excellence for computer applications (676629) / POP -
                      Performance Optimisation and Productivity (676553) /
                      Scalable Performance Analysis of Large-Scale Parallel
                      Applications $(jzam11_20191101)$ / ATMLPP - ATML Parallel
                      Performance (ATMLPP) / ATMLAO - ATML Application
                      Optimization and User Service Tools (ATMLAO)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-255 /
                      G:(EU-Grant)676629 / G:(EU-Grant)676553 /
                      $G:(DE-Juel1)jzam11_20191101$ / G:(DE-Juel-1)ATMLPP /
                      G:(DE-Juel-1)ATMLAO},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmd-2017-242},
      url          = {https://juser.fz-juelich.de/record/841906},
}