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

Caviedes-Voullième, Daniel; Morales-Hernández, Mario; Norman, Matthew R.; Özgen-Xian, Ilhan

doi:10.5194/gmd-16-977-2023

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

Journal Article · Wed Feb 08 00:00:00 EST 2023 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-16-977-2023· OSTI ID:1970338

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Forschungszentrum Juelich (Germany)
Universidad de Zaragoza (Spain); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Technische Universität Braunschweig (Germany); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

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. At the core of SERGHEI's innovation is its performance-portable high-performance parallel-computing (HPC) implementation, built from scratch on the Kokkos portability layer, allowing SERGHEI to be deployed, in a performance-portable fashion, in graphics processing unit (GPU)-based heterogeneous systems. 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) and its HPC implementation. This module is designed to be applicable to hydrological and environmental problems including flooding and runoff generation, with an outlook towards Earth system modelling. Its applicability is demonstrated by testing several well-known benchmarks and large-scale problems, for which SERGHEI-SWE achieves excellent results for the different types of shallow-water problems. Finally, SERGHEI-SWE scalability and performance portability is demonstrated and evaluated on several TOP500 HPC systems, with very good scaling in the range of over 20 000 CPUs and up to 256 state-of-the art GPUs.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231; AC05-00OR22725

OSTI ID:: 1970338

Alternate ID(s):: OSTI ID: 2000308

Journal Information:: Geoscientific Model Development (Online), Vol. 16, Issue 3; ISSN 1991-9603

Publisher:: Copernicus Publications, EGUCopyright Statement

Country of Publication:: United States

Language:: English

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