Comparison of Multigrid Algorithms for High-order Continuous Finite Element Discretizations

TL;DR

This paper compares various multigrid algorithms for solving high-order finite element discretizations of elliptic PDEs, analyzing their efficiency, convergence, and computational costs across different approaches and problem settings.

Contribution

It provides a comprehensive comparison of $h$-multigrid, $p$-multigrid, and first-order approximation multigrid methods for high-order finite element systems, including performance insights.

Findings

Both $h$- and $p$-multigrid methods perform well.

Chebyshev and SSOR smoothers outperform Jacobi for variable coefficients.

Multigrid as a preconditioner reduces Krylov iteration counts significantly.

Abstract

We present a comparison of different multigrid approaches for the solution of systems arising from high-order continuous finite element discretizations of elliptic partial differential equations on complex geometries. We consider the pointwise Jacobi, the Chebyshev-accelerated Jacobi and the symmetric successive over-relaxation (SSOR) smoothers, as well as elementwise block Jacobi smoothing. Three approaches for the multigrid hierarchy are compared: 1) high-order $h$-multigrid, which uses high-order interpolation and restriction between geometrically coarsened meshes; 2) $p$-multigrid, in which the polynomial order is reduced while the mesh remains unchanged, and the interpolation and restriction incorporate the different-order basis functions; and 3), a first-order approximation multigrid preconditioner constructed using the nodes of the high-order discretization. This latter approach is often combined with algebraic multigrid for the low-order operator and is attractive for high-order discretizations on unstructured meshes, where geometric coarsening is difficult. Based on a simple performance model, we compare the computational cost of the different approaches. Using scalar test problems in two and three dimensions with constant and varying coefficients, we compare the performance of the different multigrid approaches for polynomial orders up to 16. Overall, both $h$- and $p$-multigrid work well; the first-order approximation is less efficient. For constant coefficients, all smoothers work well. For variable coefficients, Chebyshev and SSOR smoothing outperforms Jacobi smoothing. While all of the tested methods converge in a mesh-independent number of iterations, none of them behaves completely independent of the polynomial order. When multigrid is used as a preconditioner in a Krylov method, the iteration number decreases significantly compared to using multigrid as a solver.