An Adjoint Approach for Stabilizing the Parareal Method

The parareal algorithm seeks to extract parallelism in the time-integration direction of time-dependent differential equations. While it has been applied with success to a wide range of problems, it suffers from some stability issues when applied to non-dissipative problems. We express the method through an iteration matrix and show that the problematic behavior is related to the non-normal structure of the iteration matrix. To enforce monotone convergence we propose an adjoint parareal algorithm, accelerated by the Conjugate Gradient Method. Numerical experiments confirm the stability and suggest directions for further improving the performance

Convergence of Parareal for the Navier-Stokes Equations Depending on the Reynolds Number

The paper presents first a linear stability analysis for the time-parallel Parareal method, using an IMEX Euler as coarse and a Runge-Kutta-3 method as fine propagator, confirming that dominant imaginary eigenvalues negatively affect Parareal’s convergence. This suggests that when Parareal is applied to the nonlinear Navier-Stokes equations, problems for small viscosities could arise. Numerical results for a driven cavity benchmark are presented, confirming that Parareal’s convergence can indeed deteriorate as viscosity decreases and the flow becomes increasingly dominated by convection. The effect is found to strongly depend on the spatial resolution

Parallel-in-time integration of kinematic dynamos

The precise mechanisms responsible for the natural dynamos in the Earth and Sun are still not fully understood. Numerical simulations of natural dynamos are extremely computationally intensive, and are carried out in parameter regimes many orders of magnitude away from real conditions. Parallelization in space is a common strategy to speed up simulations on high performance computers, but eventually hits a scaling limit. Additional directions of parallelization are desirable to utilise the high number of processor cores now available. Parallel-in-time methods can deliver speed up in addition to that offered by spatial partitioning but have not yet been applied to dynamo simulations. This paper investigates the feasibility of using the parallel-in-time algorithm Parareal to speed up initial value problem simulations of the kinematic dynamo, using the open source Dedalus spectral solver. Both the time independent Roberts and time dependent Galloway-Proctor 2.5D dynamos are investigated over a range of magnetic Reynolds numbers. Speedups beyond those possible from spatial parallelisation are found in both cases. Results for the Galloway-Proctor flow are promising, with Parareal efficiency found to be close to 0.3. Roberts flow results are less efficient, but Parareal still shows some speed up over spatial parallelisation alone. Parallel in space and time speed ups of ∼300 were found for 1600 cores for the Galloway-Proctor flow, with total parallel efficiency of ∼0.16

A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLA

In view of the rapid rise of the number of cores in modern supercomputers, time-parallel methods that introduce concurrency along the temporal axis are becoming increasingly popular. For the solution of time-dependent partial differential equations, these methods can add another direction for concurrency on top of spatial parallelization. The paper presents an implementation of the time-parallel Parareal method in a C++ domain specific language for stencil computations (STELLA). STELLA provides both an OpenMP and a CUDA backend for a shared memory parallelization, using the CPU or GPU inside a node for the spatial stencils. Here, we intertwine this node-wise spatial parallelism with the time-parallel Parareal. This is done by adding an MPI-based implementation of Parareal, which allows us to parallelize in time across nodes. The performance of Parareal with both backends is analyzed in terms of speedup, parallel efficiency and energy-to-solution for an advection-diffusion problem with a time-dependent diffusion coefficient

Communication-aware adaptive parareal with application to a nonlinear hyperbolic system of partial dierential equations

In the strong scaling limit, the performance of conventional spatial domain decomposition techniques for the parallel solution of PDEs saturates. When sub-domains become small, halo-communication and other overheard come to dominate. A potential path beyond this scaling limit is to introduce domain-decomposition in time, with one such popular approach being the Parareal algorithm which has received a lot of attention due to its generality and potential scalability. Low efficiency, particularly on convection dominated problems, has however limited the adoption of the method. In this paper we introduce a new strategy, Communication Aware Adaptive Parareal (CAAP) to overcome some of the challenges. With CAAP, we choose time-subdomains short enough that convergence of the Parareal algorithm is quick, yet long enough that the overheard of communicating time-subdomain interfaces does not induce a new limit to parallel speed-up. Furthermore, we propose an adaptive work scheduling algorithm that overlaps consecutive Parareal cycles and decouples the number of time-subdomains and number of active node-groups in an efficient manner to allow for comparatively high parallel eciency. We demonstrate the viability of CAAP trough the parallel-in-time integration of a hyperbolic system of PDEs in the form of the two-dimensional nonlinear shallow-water wave equation solved using a 3rd order accurate WENO-RK discretization. For the computational cheap approximate operator needed as a preconditioner in the Parareal corrections we use a lower order Roe type discretization. Time-parallel integration of purely hyperbolic type evolution problems is traditionally considered impractical. Trough large-scale numerical experiments we demonstrate that with CAAP, it is possible not only to obtain time-parallel speedup on this class of evolution problems, but also that we may obtain parallel acceleration beyond what is possible using conventional spatial domain-decomposition techniques alone. The approach is widely applicable for parallel-in-time integration over long time domains, regardless of the class of evolution problem