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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 7
Geosci. Model Dev., 11, 2875-2895, 2018
https://doi.org/10.5194/gmd-11-2875-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 2875-2895, 2018
https://doi.org/10.5194/gmd-11-2875-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 13 Jul 2018

Development and technical paper | 13 Jul 2018

A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications

Wendy Sharples1,2,3, Ilya Zhukov1, Markus Geimer1, Klaus Goergen2,4, Sebastian Luehrs1, Thomas Breuer1, Bibi Naz2,4, Ketan Kulkarni1,4, Slavko Brdar1,4, and Stefan Kollet2,4 Wendy Sharples et al.
  • 1Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
  • 2Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany
  • 3Meteorological Institute, University of Bonn, Bonn, Germany
  • 4Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich, Germany

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.

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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Next-generation geoscientific models are based on complex model implementations and workflows....
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