Preprints
https://doi.org/10.5194/gmd-2022-208
https://doi.org/10.5194/gmd-2022-208
Submitted as: model description paper
08 Sep 2022
Submitted as: model description paper | 08 Sep 2022
Status: this preprint is currently under review for the journal GMD.

SERGHEI (-SWE) v1.0: a performance portable HPC shallow water solver for hydrology and environmental hydraulics

Daniel Caviedes-Voullième1,2, Mario Morales-Hernández3,4, Matthew R. Norman4, and Ilhan Özgen-Xian5,6 Daniel Caviedes-Voullième et al.
  • 1Simulation and Data Lab Terrestrial Systems, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
  • 2Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Germany
  • 3Fluid Mechanics, I3A-Universidad de Zaragoza, Spain
  • 4Oak Ridge National Laboratory, USA
  • 5Institute of Geoecology, Technische Universität Braunschweig, Germany
  • 6Earth & Environmental Sciences Area, Lawrence Berkeley National Laboratory, USA

Abstract. The Simulation Environment for Geomorphology, Hydrodynamics and Ecohydrology in Integrated form (SERGHEI) is a multi-dimensional, multi-domain and multi-physics model framework for environmental and landscape simulation, designed with an outlook towards Earth System Modelling. It aims to provide a modelling environment for hydrodynamics, ecohydrology, morphodynamics, and, most importantly, interactions and feedbacks among such processes at different levels of complexity and across spatiotemporal scales. The small scale feedbacks and interactions, which warrant high resolution, can result in emergent behaviours manifesting at larger scales, thus warranting large model domains. At the core of SERGHEI's technical innovation is its HPC implementation, built from scratch on the Kokkos portability layer. Consequently, SERGHEI achieves performance-portability from personal computers to top HPC systems, including GPU-based and heterogeneous systems. SERGHEI relies Kokkos to handle memory spaces, thread management and execution policies for the required backend programming models. In this work we explore combinations of MPI and Kokkos using OpenMP and CUDA backends. In this contribution, we introduce the SERGHEI model framework, and present with detail its first operational module for solving shallow water equations (SERGHEI-SWE). This module is designed to be applicable to hydrological, environmental and consequently Earth System Modelling problems, but also to classical engineering problems such as fluvial or urban flood modelling. We also provide evidence of its applicability by testing it against several well-known benchmarks. We also evaluate its performance on several benchmarks and large scale problems. Finally, SERGHEI-SWE is evaluated in terms of scaling (on several TOP500 HPC systems).

Daniel Caviedes-Voullième et al.

Status: open (until 03 Nov 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2022-208', Reinhard Hinkelmann, 23 Sep 2022 reply

Daniel Caviedes-Voullième et al.

Data sets

SERGHEI test cases - static with DOI Caviedes-Voullieme, D., Morales-Hernandez, and M., Özgen-Xian, I. https://doi.org/10.5281/zenodo.7041392

SERGHEI test cases repository Caviedes-Voullieme, D., Morales-Hernandez, M., Özgen-Xian, I. https://gitlab.com/serghei-model/serghei_testcases

Model code and software

SERGHEI code - static v1.0 with DOI Caviedes-Voullieme, D., Morales-Hernandez, M., Norman, M., Özgen-Xian, I. https://doi.org/10.5281/zenodo.7041423

SERGHEI code repository Caviedes-Voullieme, D., Morales-Hernandez, M., Norman, M., Özgen-Xian, I. https://gitlab.com/serghei-model/serghei

Daniel Caviedes-Voullième et al.

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Short summary
This paper introduces the SERGHEI framework and a solver for shallow water problems. Such models, often used for surface flow and flood modelling are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing SERGHEI to be ready for surface flow simulation on the newest and upcoming consumer hardware and supercomputers very efficiently.