Hybrid multigrid methods for high-order discontinuous Galerkin discretizations

The present work develops hybrid multigrid methods for high-order discontinuous Galerkin discretizations of elliptic problems. Fast matrix-free operator evaluation on tensor product elements is used to devise a computationally efficient PDE solver. The multigrid hierarchy exploits all possibilities of geometric, polynomial, and algebraic coarsening, targeting engineering applications on complex geometries. Additionally, a transfer from discontinuous to continuous function spaces is performed within the multigrid hierarchy. This does not only further reduce the problem size of the coarse-grid problem, but also leads to a discretization most suitable for state-of-the-art algebraic multigrid methods applied as coarse-grid solver. The relevant design choices regarding the selection of optimal multigrid coarsening strategies among the various possibilities are discussed with the metric of computational costs as the driving force for algorithmic selections. We find that a transfer to a continuous function space at highest polynomial degree (or on the finest mesh), followed by polynomial and geometric coarsening, shows the best overall performance. The success of this particular multigrid strategy is due to a significant reduction in iteration counts as compared to a transfer from discontinuous to continuous function spaces at lowest polynomial degree (or on the coarsest mesh). The coarsening strategy with transfer to a continuous function space on the finest level leads to a multigrid algorithm that is robust with respect to the penalty parameter of the SIPG method. Detailed numerical investigations are conducted for a series of examples ranging from academic test cases to more complex, practically relevant geometries. Performance comparisons to state-of-the-art methods from the literature demonstrate the versatility and computational efficiency of the proposed multigrid algorithms

A hybrid geometric + algebraic multigrid method with semi-iterative smoothers

We propose a multigrid method for solving large-scale sparse linear systems arising from discretizations of partial differential equations, such as those from finite element and generalized finite difference methods. Our proposed method has the following two characteristics. First, we introduce a hybrid geometric+algebraic multigrid method, or HyGA, to leverage the rigor, accuracy, and efficiency of geometric multigrid (GMG) for hierarchical unstructured meshes, with the flexibility of algebraic multigrid (AMG). Second, we introduce efficient smoothers based on the Chebyshev-Jacobi method for both symmetric and asymmetric matrices. We also introduce a unified derivation of restriction and prolongation operators for weighted-residual formulations over unstructured hierarchical meshes and apply it to both finite element and generalized finite difference methods. We present numerical results of our method for Poisson equations in both 2-D and 3-D and compare our method against the classical GMG and AMG as well as Krylov subspace methods. © 2014 John Wiley & Sons, Ltd

Matrix-free finite-element computations at extreme scale and for challenging applications

For numerical computations based on finite element methods (FEM), it is common practice to assemble the system matrix related to the discretized system and to pass this matrix to an iterative solver. However, the assembly step can be costly and the matrix might become locally dense, e.g., in the context of high-order, high-dimensional, or strongly coupled multicomponent FEM, leading to high costs when applying the matrix due to limited bandwidth on modern CPU- and GPU-based hardware. Matrix-free algorithms are a means of accelerating FEM computations on HPC systems, by applying the effect of the system matrix without assembling it. Despite convincing arguments for matrix-free computations as a means of improving performance, their usage still tends to be an exception at the time of writing of this thesis, not least because they have not yet proven their applicability in all areas of computational science, e.g., solid mechanics. In this thesis, we further develop a state-of-the-art matrix-free framework for high-order FEM computations with focus on the preconditioning and adopt it in novel application fields. In the context of high-order FEM, we develop means of improving cache efficiency by interleaving cell loops with vector updates, which we use to increase the throughput of preconditioned conjugate gradient methods and of block smoothers based on additive Schwarz methods; we also propose an algorithm for the fast application of hanging-node constraints in 3D for up to 137 refinement configurations. We develop efficient geometric and polynomial multigrid solvers with optimized transfer operators, whose performance is experimentally investigated in detail in the context of locally refined meshes, indicating the superiority of global-coarsening algorithms. We apply the developed solvers in the context of novel stage-parallel implicit Runge–Kutta methods and demonstrate the benefit of stage–parallel solvers in decreasing the time to solution at the scaling limit. Novel challenging application fields of matrix-free computations include high-dimensional computational plasma physics, solid-state-sintering simulations with a high and dynamically changing number of strongly coupled components, and coupled multiphysics problems with evaluation and integration at arbitrary points. In the context of these fields, we detail computational challenges, propose modified versions of the standard matrix-free algorithms for high-performance computing, and discuss preconditioning-related topics. The efficiency of the derived algorithms on the node level and at extreme scales is demonstrated experimentally on SuperMUC-NG, one of Germany’s leading supercomputers, with up to 150k processes and by solving systems of up to 5 × 1012 unknowns. Such problem sizes would not be conceivable for equivalent matrix-based algorithms. The major achievements of this thesis allow to run larger simulations faster and more efficiently, enabling progress and new possibilities for a range of application fields in computational science