Best practice regarding the three P's: profiling, portability and provenance when running HPC geoscientific applications

Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Stefan Kollet, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar
2017 Geoscientific Model Development Discussions  
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
more » ... tively. 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. <br><br> 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.
doi:10.5194/gmd-2017-242 fatcat:fm72nuuadrfkraniya6yvuqv3e