Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10)
Popularity of one kilometre scale global geophysical models is continuously growing in Europe. In this study, we increase the resolution of the NEMO ocean model, to better identify technical limitations of the present implementation and deduce, from the performance measured on one of our top superco...
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ftdatacite:10.5281/zenodo.2596983 2023-05-15T17:53:43+02:00 Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) Maisonnave, Eric Levy, Claire Masson, Sebastien 2019 https://dx.doi.org/10.5281/zenodo.2596983 https://zenodo.org/record/2596983 en eng Zenodo https://zenodo.org/communities/esiwace https://dx.doi.org/10.5281/zenodo.2596982 https://zenodo.org/communities/esiwace Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Text Project deliverable article-journal ScholarlyArticle 2019 ftdatacite https://doi.org/10.5281/zenodo.2596983 https://doi.org/10.5281/zenodo.2596982 2021-11-05T12:55:41Z Popularity of one kilometre scale global geophysical models is continuously growing in Europe. In this study, we increase the resolution of the NEMO ocean model, to better identify technical limitations of the present implementation and deduce, from the performance measured on one of our top supercomputers, the hardware characteristics needed to routinely use a global ocean one kilometre configuration. 24,000 Intel Broadwell cores of the Météo-France supercomputer are required to measure the performance of a simplified version of NEMO global kilometre scale. From them, assuming a perfect scaling up to 9,000,000 (nine million) computing cores, we infer that we could target a maximum speed of approximately 2 SYPD with the ORCA-Km global model. : ESiWACE has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 675191 Text Orca DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
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English |
description |
Popularity of one kilometre scale global geophysical models is continuously growing in Europe. In this study, we increase the resolution of the NEMO ocean model, to better identify technical limitations of the present implementation and deduce, from the performance measured on one of our top supercomputers, the hardware characteristics needed to routinely use a global ocean one kilometre configuration. 24,000 Intel Broadwell cores of the Météo-France supercomputer are required to measure the performance of a simplified version of NEMO global kilometre scale. From them, assuming a perfect scaling up to 9,000,000 (nine million) computing cores, we infer that we could target a maximum speed of approximately 2 SYPD with the ORCA-Km global model. : ESiWACE has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 675191 |
format |
Text |
author |
Maisonnave, Eric Levy, Claire Masson, Sebastien |
spellingShingle |
Maisonnave, Eric Levy, Claire Masson, Sebastien Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
author_facet |
Maisonnave, Eric Levy, Claire Masson, Sebastien |
author_sort |
Maisonnave, Eric |
title |
Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
title_short |
Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
title_full |
Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
title_fullStr |
Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
title_full_unstemmed |
Implementation of NEMO global 1km ocean-only demonstrator and performance analysis (D2.10) |
title_sort |
implementation of nemo global 1km ocean-only demonstrator and performance analysis (d2.10) |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://dx.doi.org/10.5281/zenodo.2596983 https://zenodo.org/record/2596983 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
https://zenodo.org/communities/esiwace https://dx.doi.org/10.5281/zenodo.2596982 https://zenodo.org/communities/esiwace |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.2596983 https://doi.org/10.5281/zenodo.2596982 |
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1766161420227969024 |