Parareal is a well-known parallel-in-time algorithm that combines a coarse and fine propagator within a parallel iteration. It allows for large-scale parallelism that leads to significantly reduced computational time compared to serial time-stepping methods. However, like many parallel-in-time methods it can fail to achieve parallel speedup when applied to non-diffusive equations such as hyperbolic systems or dispersive nonlinear wave equations. This paper explores the use of exponential integrators within the Parareal iteration. Exponential integrators are particularly interesting candidates for Parareal because of their ability to resolve fast-moving waves, even at the large stepsizes used by coarse propagators. This work begins with an introduction to exponential Parareal integrators followed by several motivating numerical experiments involving the nonlinear Schr\"odinger equation. These experiments are then analyzed using linear analysis that approximates the stability and convergence properties of the exponential Parareal iteration on nonlinear problems. The paper concludes with two additional numerical experiments involving the dispersive Kadomtsev-Petviashvili equation and the hyperbolic Vlasov-Poisson equation. These experiments demonstrate that exponential Parareal methods can achieve significant parallel speedup on different types of non-diffusive equations.
翻译:帕拉里尔是一个众所周知的平行时间算法, 它在平行的迭代中结合粗和细细的传播器。 它允许大规模平行化, 导致与序列时间步骤方法相比, 计算时间与序列时间步骤方法相比大大缩短。 但是, 和许多平行方法一样, 当应用到非硬性方程式, 如超双曲系统或分散的非线性非线性波方程式时, 它可能无法实现平行加速。 本文探索了在帕拉里尔的迭代中使用指数化集成器的情况。 实验者是帕拉里尔特别有趣的候选者, 因为他们有能力解决快速移动的波, 甚至在粗化的传播器所使用的大阶梯级化方法上。 这项工作始于引入指数化极性聚合器, 然后进行一些涉及非线性方程式Schr\' 或分散性非线性非线性波方程式等方程式的激励性实验。 这些实验然后用直线性分析非线性变相接近指数性Prealality重复的问题的稳定性和趋性特性。 论文最后以另外两项数字性实验为结论, 涉及极性卡利奥里萨维利亚式的极式超级平式的极性平式可解性方程式演示方法。