Articles | Volume 12, issue 10
Geosci. Model Dev., 12, 4425–4441, 2019
https://doi.org/10.5194/gmd-12-4425-2019
Geosci. Model Dev., 12, 4425–4441, 2019
https://doi.org/10.5194/gmd-12-4425-2019

Development and technical paper 22 Oct 2019

Development and technical paper | 22 Oct 2019

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Andreas Müller et al.

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Andreas Mueller on behalf of the Authors (04 Jun 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (17 Jun 2019) by Chiel van Heerwaarden
RR by Anonymous Referee #1 (08 Jul 2019)
RR by Anonymous Referee #2 (09 Jul 2019)
ED: Publish subject to minor revisions (review by editor) (19 Jul 2019) by Chiel van Heerwaarden
AR by Andreas Mueller on behalf of the Authors (27 Aug 2019)  Author's response    Manuscript
ED: Publish as is (04 Sep 2019) by Chiel van Heerwaarden
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Short summary
This paper presents an overview of the ESCAPE project. Dwarfs (key patterns in terms of computation and communication) are identified in weather prediction models. They are optimised for different hardware architectures. New algorithms are developed that are specifically designed for better energy efficiency and improved portability through domain-specific languages. Different numerical techniques are compared in terms of energy efficiency and performance for a variety of computing technologies.