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  4. Improving efficiency of parallel across the method spectral deferred corrections
 
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Improving efficiency of parallel across the method spectral deferred corrections

Citation Link: https://doi.org/10.15480/882.15023
Publikationstyp
Journal Article
Date Issued
2025-02-11
Sprache
English
Author(s)
Caklovi, Gayatri
Lunet, Thibaut  
Mathematik E-10  
Götschel, Sebastian  orcid-logo
Mathematik E-10  
Ruprecht, Daniel  orcid-logo
Mathematik E-10  
TORE-DOI
10.15480/882.15023
TORE-URI
https://hdl.handle.net/11420/54766
Journal
SIAM journal on scientific computing  
Volume
47
Issue
1
Start Page
A430
End Page
A453
Citation
SIAM Journal on Scientific Computing 47 (1): A430-A453 (2025)
Publisher DOI
10.1137/24M1649800
Scopus ID
2-s2.0-85217916905
Publisher
SIAM
Is Supplemented By
11420/55238
Parallel across the method time integration can provide small scale parallelism when solving initial value problems. Spectral deferred corrections (SDCs) with a diagonal sweeper, closely related to iterated Runge-Kutta methods proposed by Van der Houwen and Sommeijer, can use a number of threads equal to the number of quadrature nodes in the underlying collocation method. However, convergence speed, efficiency, and stability depend critically on the coefficients of the used SDC preconditioner. Previous approaches used numerical optimization to find good diagonal coefficients. Instead, we propose an approach that allows one to find optimal diagonal coefficients analytically. We show that the resulting parallel SDC methods provide stability domains and convergence order very similar to those of well established serial SDC variants. Using a model for computational cost that assumes 80% efficiency of an implementation of parallel SDCs, we show that our variants are competitive with serial SDC, previously published parallel SDC coefficients, Picard iteration, and a fourth-order explicit as well as a fourth-order implicit diagonally implicit Runge-Kutta method.
Subjects
iterated Runge-Kutta methods | parallel across the method | parallel in time (PinT) | spectral deferred correction | stiff and non-stiff problems
DDC Class
600: Technology
Funding(s)
Leistungsverbesserung des ICON-O Ozeanmodells auf heterogenen Exascale-Supercomputern mit Methoden des Maschinellen Lernens  
TIME parallelisation: for eXascale computing and beyond  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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