Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2740
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dc.contributor.authorClarke, Andrew T.-
dc.contributor.authorDavies, Christopher J.-
dc.contributor.authorRuprecht, Daniel-
dc.contributor.authorTobias, Steven M.-
dc.date.accessioned2020-04-15T08:22:30Z-
dc.date.available2020-04-15T08:22:30Z-
dc.date.issued2020-03-24-
dc.identifier.citationJournal of Computational Physics X (7): 100057 (2020)de_DE
dc.identifier.issn2590-0552de_DE
dc.identifier.urihttp://hdl.handle.net/11420/5733-
dc.description.abstractThe precise mechanisms responsible for the natural dynamos in the Earth and Sun are still not fully understood. Numerical simulations of natural dynamos are extremely computationally intensive, and are carried out in parameter regimes many orders of magnitude away from real conditions. Parallelization in space is a common strategy to speed up simulations on high performance computers, but eventually hits a scaling limit. Additional directions of parallelization are desirable to utilise the high number of processor cores now available. Parallel-in-time methods can deliver speed up in addition to that offered by spatial partitioning but have not yet been applied to dynamo simulations. This paper investigates the feasibility of using the parallel-in-time algorithm Parareal to speed up initial value problem simulations of the kinematic dynamo, using the open source Dedalus spectral solver. Both the time independent Roberts and time dependent Galloway-Proctor 2.5D dynamos are investigated over a range of magnetic Reynolds numbers. Speed ups beyond those possible from spatial parallelization are found in both cases. Results for the Galloway-Proctor flow are promising, with Parareal efficiency found to be close to 0.3. Roberts flow results are less efficient, but Parareal still shows some speed up over spatial parallelization alone. Parallel in space and time speed ups of ∼300 were found for 1600 cores for the Galloway-Proctor flow, with total parallel efficiency of ∼0.16.en
dc.language.isoende_DE
dc.publisherElsevierde_DE
dc.relation.ispartofJournal of computational physics: Xde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectIMEXde_DE
dc.subjectInduction equationde_DE
dc.subjectKinematic dynamode_DE
dc.subjectParallel-in-timede_DE
dc.subjectPararealde_DE
dc.subjectSpectral methodsde_DE
dc.subjectPhysics - Computational Physicsde_DE
dc.subjectPhysics - Computational Physicsde_DE
dc.subject.ddc510: Mathematikde_DE
dc.titleParallel-in-time integration of kinematic dynamosde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.2740-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.087040-
tuhh.oai.showtruede_DE
tuhh.abstract.englishThe precise mechanisms responsible for the natural dynamos in the Earth and Sun are still not fully understood. Numerical simulations of natural dynamos are extremely computationally intensive, and are carried out in parameter regimes many orders of magnitude away from real conditions. Parallelization in space is a common strategy to speed up simulations on high performance computers, but eventually hits a scaling limit. Additional directions of parallelization are desirable to utilise the high number of processor cores now available. Parallel-in-time methods can deliver speed up in addition to that offered by spatial partitioning but have not yet been applied to dynamo simulations. This paper investigates the feasibility of using the parallel-in-time algorithm Parareal to speed up initial value problem simulations of the kinematic dynamo, using the open source Dedalus spectral solver. Both the time independent Roberts and time dependent Galloway-Proctor 2.5D dynamos are investigated over a range of magnetic Reynolds numbers. Speed ups beyond those possible from spatial parallelization are found in both cases. Results for the Galloway-Proctor flow are promising, with Parareal efficiency found to be close to 0.3. Roberts flow results are less efficient, but Parareal still shows some speed up over spatial parallelization alone. Parallel in space and time speed ups of ∼300 were found for 1600 cores for the Galloway-Proctor flow, with total parallel efficiency of ∼0.16.de_DE
tuhh.publisher.doi10.1016/j.jcpx.2020.100057-
tuhh.publication.instituteMathematik E-10de_DE
tuhh.identifier.doi10.15480/882.2740-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.volume7de_DE
tuhh.container.startpage100057de_DE
dc.rights.nationallicensefalsede_DE
dc.identifier.arxiv1902.00387v1de_DE
dc.identifier.scopus2-s2.0-85082591567de_DE
tuhh.container.articlenumber100057de_DE
local.status.inpressfalsede_DE
local.funding.infoSupported by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Fluid Dynamics (EP/L01615X/1). Supported by a Natural Environment Research Council (NERC) Independent Research Fellowship (NE/L011328/1). Support from grants EPSRC EP/P02372X/1 and NERC NE/R008795/1. Supported by a Leverhulme Fellowship and by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. D5S-DLV-786780).de_DE
datacite.resourceTypeJournal Article-
datacite.resourceTypeGeneralText-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorGNDClarke, Andrew T.-
item.creatorGNDDavies, Christopher J.-
item.creatorGNDRuprecht, Daniel-
item.creatorGNDTobias, Steven M.-
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.creatorOrcidClarke, Andrew T.-
item.creatorOrcidDavies, Christopher J.-
item.creatorOrcidRuprecht, Daniel-
item.creatorOrcidTobias, Steven M.-
item.languageiso639-1en-
item.mappedtypeArticle-
crisitem.author.deptMathematik E-10-
crisitem.author.orcid0000-0003-2128-0016-
crisitem.author.orcid0000-0003-1904-2473-
crisitem.author.orcid0000-0003-0205-7716-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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