A Combined Parallel-in-time Direct Inverse (ParaDIn)-Parareal Method for Nonlinear Differential Equations
Subhash Paudel, Nail K. Yamaleev

TL;DR
This paper introduces a combined ParaDIn-Parareal method that enhances parallelization and speedup for solving nonlinear differential equations by integrating block-Jacobi preconditioning with parallel-in-time algorithms.
Contribution
The paper presents a novel combined parallel-in-time approach that uses ParaDIn with Parareal, enabling higher parallel efficiency and significant speedup for nonlinear PDEs.
Findings
Achieved up to 124x speedup on 480 cores.
Effectively parallelized the coarse and fine propagators.
Demonstrated improved performance on nonlinear heat and Burgers equations.
Abstract
As has been shown in our previous work, the parallel-in-time direct inverse (ParaDIn) method introduced by Yamaleev and Paudel in (arXiv: 2406.00878v1, 2024) imposes some constraint on the maximum number of time levels, , that can be integrated in parallel. To circumvent this problem and further increase the speedup, we combine the ParaDIn method with the Parareal algorithm to efficiently parallelize the first-order time derivative term in nonlinear partial differential equations discretized by the method of lines. The main idea of the proposed approach is to use a block-Jacobi preconditioner, so that each block is solved by using the ParaDIn method. To accelerate the convergence of Jacobi iterations, we use the Parareal method which can be interpreted as a two-level multigrid method in time. In contrast to the conventional Parareal algorithm whose coarse grid correction step is…
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Taxonomy
TopicsMatrix Theory and Algorithms · Numerical methods for differential equations · Advanced Numerical Methods in Computational Mathematics
