Multigrid Reduction In Time Research Articles

Parallel-in-time multigrid for space–time finite element approximations of two-dimensional space-fractional diffusion equations

The paper investigates a non-intrusive parallel time integration with multigrid for space-fractional diffusion equations in two spatial dimensions, which is discretized by the space–time finite element method to propagate solutions. We develop a multigrid-reduction-in-time (MGRIT) algorithm with time-dependent time-grid propagators and provide its two-level convergence theory under the assumptions of the stability and simultaneous diagonalizability on time-grid propagators. Numerical results show that the proposed method possesses the saturation error order, theoretical results of the two-level variant deliver good predictions for our model problems, and significant speedups of the MGRIT can be achieved when compared to the two-level variant with F-relaxation (an equivalent version of the parareal algorithm) and the sequential time-stepping approach.

Necessary Conditions and Tight Two-level Convergence Bounds for Parareal and Multigrid Reduction in Time

Parareal and multigrid reduction in time (MGRiT) are two of the most popular parallel-in-time methods. The basic idea is to treat time integration in a parallel context by using a multigrid method ...

Parallel-In-Time Multigrid with Adaptive Spatial Coarsening for The Linear Advection and Inviscid Burgers Equations

We apply a multigrid reduction-in-time (MGRIT) algorithm to hyperbolic partial differential equations in one spatial dimension. This study is motivated by the observation that sequential time-stepp...

Acceleration of the Two-Level MGRIT Algorithm via the Diagonalization Technique

The multigrid-reduction-in-time (MGRIT) algorithm is an efficient parallel-in-time algorithm for solving dynamic problems. The goal of this paper is to accelerate this algorithm via two strategies. The first strategy is to improve the convergence rate by using the 2nd-order Lobatto IIIC (LIIIC-2) method as the $\mathcal{G}$-propagator, instead of using the backward-Euler method which is the common choice in this field. For a system of linear ODEs with a symmetric positive definite (SPD) coefficient matrix, we show that such a choice reduces the convergence factor of the MGRIT algorithm from 0.1 to 0.02. We prove a robust convergence factor for the MGRIT algorithm, which is independent of the eigenvalues of the coefficient matrix and the ratio $J=\Delta T/\Delta t$. The second strategy is to make the coarse-grid-correction (CGC) parallel by using the diagonalization technique. By properly choosing the involved parameter, we show that the new MGRIT algorithm has the same convergence rate as that of the original algorithm. Moreover, we show that within the framework of the parallel CGC the cost of the LIIIC-2 method, which is an implicit two-stage Runge--Kutta method, can be reduced to the same cost of the backward-Euler method. The idea toward this goal is still a suitable application of the diagonalization technique. Numerical experiments for the advection-diffusion equations with uncertain coefficients and the Gray--Scott model are given to support our findings.

Convergence of the multigrid reduction in time algorithm for the linear elasticity equations

SummaryThis paper presents some recent advances for parallel‐in‐time methods applied to linear elasticity. With recent computer architecture changes leading to stagnant clock speeds, but ever increasing numbers of cores, future speedups will be available through increased concurrency. Thus, sequential algorithms, such as time stepping, will suffer a bottleneck. This paper explores multigrid reduction in time (MGRIT) for an important application area, linear elasticity. Previously, efforts at parallel‐in‐time for elasticity have experienced difficulties, for example, the beating phenomenon. As a result, practical parallel‐in‐time algorithms for this application area currently do not exist. This paper proposes some solutions made possible by MGRIT (e.g., slow temporal coarsening and FCF‐relaxation) and, more importantly, a different formulation of the problem that is more amenable to parallel‐in‐time methods. Using a recently developed convergence theory for MGRIT and Parareal, we show that the changed formulation of the problem avoids the instability issues and allows the reduction of the error using two temporal grids. We then extend our approach to the multilevel case, where we demonstrate how slow temporal coarsening improves convergence. The paper ends with supporting numerical results showing a practical algorithm enjoying speedup benefits over the sequential algorithm.

Multigrid Reduction in Time for Nonlinear Parabolic Problems: A Case Study

The need for parallelism in the time dimension is being driven by changes in computer architectures, where performance increases are now provided through greater concurrency, not faster clock speeds. This creates a bottleneck for sequential time marching schemes because they lack parallelism in the time dimension. Multigrid reduction in time (MGRIT) is an iterative procedure that allows for temporal parallelism by utilizing multigrid reduction techniques and a multilevel hierarchy of coarse time grids. MGRIT has been shown to be effective for linear problems, with speedups of up to 50 times. The goal of this work is the efficient solution of nonlinear problems with MGRIT, where efficiency is defined as achieving similar performance when compared to an equivalent linear problem. The benchmark nonlinear problem is the $p$-Laplacian, where p=4 corresponds to a well-known nonlinear diffusion equation and $p=2$ corresponds to the standard linear diffusion operator, our benchmark linear problem. The key difficu...

Two-Level Convergence Theory for Multigrid Reduction in Time (MGRIT)

In this paper we develop a two-grid convergence theory for the parallel-in-time scheme known as multigrid reduction in time (MGRIT), as it is implemented in the open-source package [XBraid: Parallel Multigrid in Time, http://llnl.gov/casc/xbraid]. MGRIT is a scalable and multilevel approach to parallel-in-time simulations that nonintrusively uses existing time-stepping schemes, and in a specific two-level setting it is equivalent to the widely known parareal algorithm. The goal of this paper is twofold. First, we present a two-level MGRIT convergence analysis for linear problems where the spatial discretization matrix can be diagonalized, and then apply this analysis to our two basic model problems, the heat equation and the advection equation. One important assumption is that the coarse and fine time-grid propagators can be diagaonalized by the same set of eigenvectors, which is often the case when the same spatial discretization operator is used on the coarse and fine time grids. In many cases, the MGRI...

Parallel time integration with multigrid

AbstractWith current trends in computer architectures leading towards systems with more, but not faster, processors, faster time‐to‐solution must come from greater parallelism. We present a family of truly multilevel approaches to parallel time integration based on multigrid reduction (MGR) principles. The resulting multigrid‐reduction‐in‐time (MGRIT) algorithms are non‐intrusive approaches, which directly use an existing time propagator and, thus, can easily exploit substantially more computational resources then standard sequential time‐stepping. Furthermore, we demonstrate that MGRIT offers excellent strong and weak parallel scaling up to thousands of processors for solving diffusion equations in two and three space dimensions. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)