Optimizing Coarse Propagators in Parareal Algorithms

Fuente: arXiv
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Main Authors: Jin, Bangti, Lin, Qingle, Zhou, Zhi
Format: Preprint
Published: 2023
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_version_ 1866912204254609408
author Jin, Bangti
Lin, Qingle
Zhou, Zhi
author_facet Jin, Bangti
Lin, Qingle
Zhou, Zhi
contents The parareal algorithm represents an important class of parallel-in-time algorithms for solving evolution equations and has been widely applied in practice. To achieve effective speedup, the choice of the coarse propagator in the algorithm is vital. In this work, we investigate the use of {optimized} coarse propagators. Building upon the error estimation framework, we present a systematic procedure for constructing coarse propagators that enjoy desirable stability and consistent order. Additionally, we provide preliminary mathematical guarantees for the resulting parareal algorithm. Numerical experiments on a variety of settings, e.g., linear diffusion model, Allen-Cahn model, and viscous Burgers model, show that the optimizing procedure can significantly improve parallel efficiency when compared with the more ad hoc choice of some conventional and widely used coarse propagators.
format Preprint
id arxiv_https___arxiv_org_abs_2311_15320
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Optimizing Coarse Propagators in Parareal Algorithms
Jin, Bangti
Lin, Qingle
Zhou, Zhi
Numerical Analysis
The parareal algorithm represents an important class of parallel-in-time algorithms for solving evolution equations and has been widely applied in practice. To achieve effective speedup, the choice of the coarse propagator in the algorithm is vital. In this work, we investigate the use of {optimized} coarse propagators. Building upon the error estimation framework, we present a systematic procedure for constructing coarse propagators that enjoy desirable stability and consistent order. Additionally, we provide preliminary mathematical guarantees for the resulting parareal algorithm. Numerical experiments on a variety of settings, e.g., linear diffusion model, Allen-Cahn model, and viscous Burgers model, show that the optimizing procedure can significantly improve parallel efficiency when compared with the more ad hoc choice of some conventional and widely used coarse propagators.
title Optimizing Coarse Propagators in Parareal Algorithms
topic Numerical Analysis
url https://arxiv.org/abs/2311.15320