School of Mathematics and Physics, University of Surrey, Guildford, GU2 7XH, UK
Lawrence Mitchell
independent researcher: Edinburgh, UK
Colin Cotter
Department of Mathematics, Imperial College London, London SW7 2AZ, UK
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Total article views: 5,844 (including HTML, PDF, and XML)
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5,278
428
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5,844
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HTML: 5,278
PDF: 428
XML: 138
Total: 5,844
BibTeX: 142
EndNote: 191
Views and downloads (calculated since 09 Dec 2024)
Cumulative views and downloads
(calculated since 09 Dec 2024)
Total article views: 4,365 (including HTML, PDF, and XML)
HTML
PDF
XML
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BibTeX
EndNote
3,820
428
117
4,365
142
191
HTML: 3,820
PDF: 428
XML: 117
Total: 4,365
BibTeX: 142
EndNote: 191
Views and downloads (calculated since 25 Jul 2025)
Cumulative views and downloads
(calculated since 25 Jul 2025)
Total article views: 1,479 (including HTML, PDF, and XML)
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1,458
0
21
1,479
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0
HTML: 1,458
PDF: 0
XML: 21
Total: 1,479
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 09 Dec 2024)
Cumulative views and downloads
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Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 5,844 (including HTML, PDF, and XML)
Thereof 5,677 with geography defined
and 167 with unknown origin.
Total article views: 4,365 (including HTML, PDF, and XML)
Thereof 4,202 with geography defined
and 163 with unknown origin.
Total article views: 1,479 (including HTML, PDF, and XML)
Thereof 1,475 with geography defined
and 4 with unknown origin.
Parallelization is important for speeding up complex geoscientific
models. In addition to spatial parallelization, several parallel-in-time
(PinT) methods have been developed. This paper introduces the reader to
PinT methods for hyperbolic and geophysical models, and it presents the
asQ library which facilitates the implementation of
diagonalization-based (ParaDiag) methods.
Parallelization is important for speeding up complex geoscientific
models. In addition to...
Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel algorithms calculate the system state (e.g. the atmosphere) at multiple times simultaneously and have exciting potential but are tricky to implement and still require development. We have developed software to simplify implementing and testing the ParaDiag algorithm on supercomputers. We show that for some atmospheric problems it can enable faster or more accurate solutions than traditional techniques.
Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel...