|Publisher DOI:||10.1016/j.jcpx.2020.100057||arXiv ID:||1902.00387v1||Title:||Parallel-in-time integration of kinematic dynamos||Language:||English||Authors:||Clarke, Andrew T.
Davies, Christopher J.
Tobias, Steven M.
|Keywords:||IMEX;Induction equation;Kinematic dynamo;Parallel-in-time;Parareal;Spectral methods;Physics - Computational Physics;Physics - Computational Physics||Issue Date:||24-Mar-2020||Publisher:||Elsevier||Source:||Journal of Computational Physics X (7): 100057 (2020)||Journal or Series Name:||Journal of computational physics: X||Abstract (english):||The 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.||URI:||http://hdl.handle.net/11420/5733||DOI:||10.15480/882.2740||ISSN:||2590-0552||Institute:||Mathematik E-10||Type:||(wissenschaftlicher) Artikel||Funded by:||Supported 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).||License:||CC BY 4.0 (Attribution)|
|Appears in Collections:||Publications with fulltext|
Show full item record
Files in This Item:
checked on Nov 28, 2020
checked on Nov 28, 2020
Note about this record
This item is licensed under a Creative Commons License