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Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

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journal contribution
posted on 18.05.2021, 08:08 by Tommaso Benacchio, Luca Bonaventura, Mirco Altenbernd, Chris D Cantwell, Peter D Düben, Mike Gillard, Luc Giraud, Dominik Göddeke, Erwan Raffin, Keita Teranishi, Nils Wedi
Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

Funding

This work was supported by the ESCAPE-2 project, European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 800897); the ESiWACE2 Centre of Excellence, European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 823988); and the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy – EXC-2075 (Grant Agreement No. 390740016).

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Journal of High Performance Computing Applications

Volume

35

Issue

4

Pages

285-311

Publisher

SAGE Publications

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by SAGE under the Creative Commons Attribution-NonCommercial 4.0 International Licence (CC BY-NC 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/

Acceptance date

01/01/2021

Publication date

2021-02-08

Copyright date

2021

ISSN

1094-3420

eISSN

1741-2846

Language

en

Depositor

Dr Mike Gillard. Deposit date: 17 May 2021