A scalability study of the Ice-sheet and Sea-level System Model (ISSM, version 4.18)

• Published in: Geoscientific Model Development
• Main Authors: Fischler, Yannic, Rückamp, Martin, Bischof, Christian, Aizinger, Vadym, Morlighem, Mathieu, Humbert, Angelika
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2022
• Subjects: Greenland; Antarc*; Antarctica; Ice Sheet
• Online Access: https://epic.awi.de/id/eprint/56093/; https://epic.awi.de/id/eprint/56093/1/Fischler_et_al_2022.pdf; https://doi.org/10.5194/gmd-15-3753-2022; https://hdl.handle.net/10013/epic.dc02c8be-9f27-40fc-8c32-4b8efa97916b; https://hdl.handle.net/

Accurately modelling the contribution of Greenland and Antarctica to sea level rise requires solving partial differential equations at a high spatial resolution. In this paper, we discuss the scaling of the Ice-sheet and Sea-level System Model (ISSM) applied to the Greenland Ice Sheet with horizontal grid resolutions varying between 10 and 0.25 km. The model setup used as benchmark problem comprises a variety of modules with different levels of complexity and computational demands. The core builds the so-called stress balance module, which uses the higher-order approximation (or Blatter–Pattyn) of the Stokes equations, including free surface and ice-front evolution as well as thermodynamics in form of an enthalpy balance, and a mesh of linear prismatic finite elements, to compute the ice flow. We develop a detailed user-oriented, yet low-overhead, performance instrumentation tailored to the requirements of Earth system models and run scaling tests up to 6144 Message Passing Interface (MPI) processes. The results show that the computation of the Greenland model scales overall well up to 3072 MPI processes but is eventually slowed down by matrix assembly, the output handling and lower-dimensional problems that employ lower numbers of unknowns per MPI process. We also discuss improvements of the scaling and identify further improvements needed for climate research. The instrumented version of ISSM thus not only identifies potential performance bottlenecks that were not present at lower core counts but also provides the capability to continually monitor the performance of ISSM code basis. This is of long-term significance as the overall performance of ISSM model depends on the subtle interplay between algorithms, their implementation, underlying libraries, compilers, run-time systems and hardware characteristics, all of which are in a constant state of flux. We believe that future large-scale high-performance computing (HPC) systems will continue to employ the MPI-based programming paradigm on the road to exascale. ...