Comparing Domain Decomposition Methods for the Parallelization of Distributed Land Surface Models

Alexander von Ramm, Jens Weismüller, Wolfgang Kurtz, Tobias Neckel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Current research challenges in hydrology require high resolution models, which simulate the processes comprising the water-cycle on a global scale. These requirements stand in great contrast to the current capabilities of distributed land surface models. Hardly any literature noting efficient scalability past approximately 64 processors could be found. Porting these models to supercomputers is no simple task, because the greater part of the computational load stems from the evaluation of highly parametrized equations. Furthermore, the load is heterogeneous in both spatial and temporal dimension, and considerable load-imbalances occur triggered by input data. We investigate different domain decomposition methods for distributed land surface models and focus on their properties concerning load balancing and communication minimizing partitionings. Artificial strong scaling experiments from a single core to 8, 192 cores show that graph-based methods can distribute the computational load of the application almost as efficiently as coordinate-based methods, while the partitionings found by the graph-based method significantly reduce communication overhead.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2019 - 19th International Conference, Proceedings
EditorsJoão M.F. Rodrigues, Pedro J.S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Peter M.A. Sloot, Jack J. Dongarra
PublisherSpringer Verlag
Pages197-210
Number of pages14
ISBN (Print)9783030227333
DOIs
StatePublished - 2019
Event19th International Conference on Computational Science, ICCS 2019 - Faro, Portugal
Duration: 12 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11536 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Computational Science, ICCS 2019
Country/TerritoryPortugal
CityFaro
Period12/06/1914/06/19

Keywords

  • Graph-partitioning
  • High-perfomance computing
  • Hydrology
  • Load-balancing

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