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Explicit Parallel-in-time Integration of a Linear Acoustic-Advection System
Publikationstyp
Journal Article
Date Issued
2012-04-30
Sprache
English
Author(s)
Journal
Volume
59
Start Page
72
End Page
83
Citation
Computers & Fluids 59: 72-83 (2012-04-30)
Publisher DOI
Scopus ID
ArXiv ID
Peer Reviewed
true
The applicability of the Parareal parallel-in-time integration scheme for the solution of a linear, two-dimensional hyperbolic acoustic-advection system, which is often used as a test case for integration schemes for numerical weather prediction (NWP), is addressed. Parallel-in-time schemes are a possible way to increase, on the algorithmic level, the amount of parallelism, a requirement arising from the rapidly growing number of CPUs in high performance computer systems. A recently introduced modification of the "parallel implicit time-integration algorithm" could successfully solve hyperbolic problems arising in structural dynamics. It has later been cast into the framework of Parareal. The present paper adapts this modified Parareal and employs it for the solution of a hyperbolic flow problem, where the initial value problem solved in parallel arises from the spatial discretization of a partial differential equation by a finite difference method. It is demonstrated that the modified Parareal is stable and can produce reasonably accurate solutions while allowing for a noticeable reduction of the time-to-solution. The implementation relies on integration schemes already widely used in NWP (RK-3, partially split forward Euler, forward-backward). It is demonstrated that using an explicit partially split scheme for the coarse integrator allows to avoid the use of an implicit scheme while still achieving speedup.
Subjects
Acoustic-advection system
Krylov-subspace-enhancement
Numerical weather prediction
Parallel-in-time integration
Parareal
Computer Science - Computational Engineering; Finance; and Science
Computer Science - Computational Engineering; Finance; and Science
Computer Science - Distributed; Parallel; and Cluster Computing
Mathematics - Numerical Analysis
DDC Class
510: Mathematik