Time parallelization for hyperbolic and parabolic problems

IF 11.3 1区 数学 Q1 MATHEMATICS
Martin J. Gander, Shu-Lin Wu, Tao Zhou
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Abstract

Time parallelization, also known as PinT (parallel-in-time), is a new research direction for the development of algorithms used for solving very large-scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early as 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed for PinT computations over the past two decades, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods; waveform relaxation methods based on domain decomposition; multigrid methods in space–time; and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We will therefore focus on this important aspect, first by providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups. The first group contains four effective PinT techniques for hyperbolic problems: Schwarz waveform relaxation (SWR) with its relation to tent pitching; parallel integral deferred correction; ParaExp; and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods specifically designed for parabolic problems in the second group: Parareal; the parallel full approximation scheme in space–time (PFASST); multigrid reduction in time (MGRiT); and space–time multigrid (STMG). We complement our analysis with numerical illustrations using four time-dependent PDEs: the heat equation; the advection–diffusion equation; Burgers’ equation; and the second-order wave equation.

双曲型和抛物型问题的时间并行化
时间并行化,也称为PinT (parallel-in- Time),是一种新的研究方向,用于在高度并行计算架构上解决非常大规模的进化问题。尽管早在1964年就出现了关于PinT的有趣理论工作,但直到2004年处理器时钟速度达到其物理极限时,对PinT的研究才开始起飞。时间上并行化的一个显著特征是信息流只在时间上向前移动,这意味着时间演化过程似乎必然是顺序的。尽管如此,在过去的二十年里,许多算法已经被开发出来用于品脱计算,它们通常根据技术的工作和使用方式分为四类:射击型方法;基于域分解的波形松弛方法;时空中的多重网格方法;和直接时间并行法。然而,在过去的几年里,人们已经认识到,对于抛物线问题非常成功的PinT算法在应用于双曲问题时遇到了困难。因此,我们将重点关注这一重要方面,首先概述时间并行化的抛物型和双曲型问题之间的基本区别。然后,我们将PinT算法分为两个基本组。第一组包含四种有效的双曲问题的品脱技术:Schwarz波形松弛(SWR)及其与帐篷俯仰度的关系;并行积分延迟校正;ParaExp;和ParaDiag。虽然第一组方法也适用于抛物线问题,但我们随后提出了专门为第二组抛物线问题设计的PinT方法:Parareal;平行时空全近似格式(PFASST);多网格时间缩减(MGRiT);时空多重网格(STMG)。我们用四个与时间相关的偏微分方程的数值插图来补充我们的分析:热方程;平流扩散方程;汉堡的方程;二阶波动方程。
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来源期刊
Acta Numerica
Acta Numerica MATHEMATICS-
CiteScore
26.00
自引率
0.70%
发文量
7
期刊介绍: Acta Numerica stands as the preeminent mathematics journal, ranking highest in both Impact Factor and MCQ metrics. This annual journal features a collection of review articles that showcase survey papers authored by prominent researchers in numerical analysis, scientific computing, and computational mathematics. These papers deliver comprehensive overviews of recent advances, offering state-of-the-art techniques and analyses. Encompassing the entirety of numerical analysis, the articles are crafted in an accessible style, catering to researchers at all levels and serving as valuable teaching aids for advanced instruction. The broad subject areas covered include computational methods in linear algebra, optimization, ordinary and partial differential equations, approximation theory, stochastic analysis, nonlinear dynamical systems, as well as the application of computational techniques in science and engineering. Acta Numerica also delves into the mathematical theory underpinning numerical methods, making it a versatile and authoritative resource in the field of mathematics.
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