Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice
International audience We present a parallel implementation framework for a new dynamic/thermodynamic seaice model, called neXtSIM, based on the Elasto-Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretis...
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ftunigrenoble:oai:HAL:hal-03405832v1 2024-05-12T08:00:00+00:00 Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice Samaké, Abdoulaye Rampal, Pierre Bouillon, Sylvain Ólason, Einar Nansen Environmental and Remote Sensing Center Bergen (NERSC) 2017-09-01 https://hal.univ-grenoble-alpes.fr/hal-03405832 https://hal.univ-grenoble-alpes.fr/hal-03405832/document https://hal.univ-grenoble-alpes.fr/hal-03405832/file/Samake2017J._Comp._Phys.pdf https://doi.org/10.1016/j.jcp.2017.08.055 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2017.08.055 hal-03405832 https://hal.univ-grenoble-alpes.fr/hal-03405832 https://hal.univ-grenoble-alpes.fr/hal-03405832/document https://hal.univ-grenoble-alpes.fr/hal-03405832/file/Samake2017J._Comp._Phys.pdf doi:10.1016/j.jcp.2017.08.055 info:eu-repo/semantics/OpenAccess ISSN: 0021-9991 EISSN: 1090-2716 Journal of Computational Physics https://hal.univ-grenoble-alpes.fr/hal-03405832 Journal of Computational Physics, 2017, 350, pp.84 - 96. ⟨10.1016/j.jcp.2017.08.055⟩ [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2017 ftunigrenoble https://doi.org/10.1016/j.jcp.2017.08.055 2024-04-18T03:13:27Z International audience We present a parallel implementation framework for a new dynamic/thermodynamic seaice model, called neXtSIM, based on the Elasto-Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretisation is semi-implicit and the advection is achieved using either a pure Lagrangian scheme or an Arbitrary Lagrangian Eulerian scheme (ALE). The parallel implementation presented here focuses on the distributed-memory approach using the message-passing library MPI. The efficiency and the scalability of the parallel algorithms are illustrated by the numerical experiments performed using up to 500 processor cores of a cluster computing system. The performance obtained by the proposed parallel implementation of the neXtSIM code is shown being sufficient to perform simulations for state-of-the-art sea ice forecasting and geophysical process studies over geographical domain of several millions squared kilometers like the Arctic region. Article in Journal/Newspaper Arctic Sea ice Université Grenoble Alpes: HAL Arctic Journal of Computational Physics 350 84 96 |
institution |
Open Polar |
collection |
Université Grenoble Alpes: HAL |
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ftunigrenoble |
language |
English |
topic |
[SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDU]Sciences of the Universe [physics] Samaké, Abdoulaye Rampal, Pierre Bouillon, Sylvain Ólason, Einar Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
topic_facet |
[SDU]Sciences of the Universe [physics] |
description |
International audience We present a parallel implementation framework for a new dynamic/thermodynamic seaice model, called neXtSIM, based on the Elasto-Brittle rheology and using an adaptive mesh. The spatial discretisation of the model is done using the finite-element method. The temporal discretisation is semi-implicit and the advection is achieved using either a pure Lagrangian scheme or an Arbitrary Lagrangian Eulerian scheme (ALE). The parallel implementation presented here focuses on the distributed-memory approach using the message-passing library MPI. The efficiency and the scalability of the parallel algorithms are illustrated by the numerical experiments performed using up to 500 processor cores of a cluster computing system. The performance obtained by the proposed parallel implementation of the neXtSIM code is shown being sufficient to perform simulations for state-of-the-art sea ice forecasting and geophysical process studies over geographical domain of several millions squared kilometers like the Arctic region. |
author2 |
Nansen Environmental and Remote Sensing Center Bergen (NERSC) |
format |
Article in Journal/Newspaper |
author |
Samaké, Abdoulaye Rampal, Pierre Bouillon, Sylvain Ólason, Einar |
author_facet |
Samaké, Abdoulaye Rampal, Pierre Bouillon, Sylvain Ólason, Einar |
author_sort |
Samaké, Abdoulaye |
title |
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
title_short |
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
title_full |
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
title_fullStr |
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
title_full_unstemmed |
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice |
title_sort |
parallel implementation of a lagrangian-based model on an adaptive mesh in c++: application to sea-ice |
publisher |
HAL CCSD |
publishDate |
2017 |
url |
https://hal.univ-grenoble-alpes.fr/hal-03405832 https://hal.univ-grenoble-alpes.fr/hal-03405832/document https://hal.univ-grenoble-alpes.fr/hal-03405832/file/Samake2017J._Comp._Phys.pdf https://doi.org/10.1016/j.jcp.2017.08.055 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
ISSN: 0021-9991 EISSN: 1090-2716 Journal of Computational Physics https://hal.univ-grenoble-alpes.fr/hal-03405832 Journal of Computational Physics, 2017, 350, pp.84 - 96. ⟨10.1016/j.jcp.2017.08.055⟩ |
op_relation |
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op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1016/j.jcp.2017.08.055 |
container_title |
Journal of Computational Physics |
container_volume |
350 |
container_start_page |
84 |
op_container_end_page |
96 |
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1798841654422011904 |