Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction
journal contributionposted on 2021-05-18, 08:08 authored 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.
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).
- Mechanical, Electrical and Manufacturing Engineering