KSPHPDDM and PCHPDDM: Extending PETSc with advanced Krylov methods and robust multilevel overlapping Schwarz preconditioners

[EN] Contemporary applications in computational science and engineering often require the solution of linear systems which may be of different sizes, shapes, and structures. The goal of this paper is to explain how two libraries, PETSc and HPDDM, have been interfaced in order to offer end-users robust overlapping Schwarz preconditioners and advanced Krylov methods featuring recycling and the ability to deal with multiple right-hand sides. The flexibility of the implementation is showcased and explained with minimalist, easy-to-run, and reproducible examples, to ease the integration of these algorithms into more advanced frameworks. The examples provided cover applications from eigenanalysis, elasticity, combustion, and electromagnetism.Jose E. Roman was supported by the Spanish Agencia Estatal de Investigacion (AEI) under project SLEPc-DA (PID2019-107379RB-I00)Jolivet, P.; Roman, JE.; Zampini, S. (2021). KSPHPDDM and PCHPDDM: Extending PETSc with advanced Krylov methods and robust multilevel overlapping Schwarz preconditioners. Computers & Mathematics with Applications. 84:277-295. https://doi.org/10.1016/j.camwa.2021.01.0032772958

Complex additive geometric multilevel solvers for Helmholtz equations on spacetrees

We introduce a family of implementations of low-order, additive, geometric multilevel solvers for systems of Helmholtz equations arising from Schrödinger equations. Both grid spacing and arithmetics may comprise complex numbers, and we thus can apply complex scaling to the indefinite Helmholtz operator. Our implementations are based on the notion of a spacetree and work exclusively with a finite number of precomputed local element matrices. They are globally matrix-free. Combining various relaxation factors with two grid transfer operators allows us to switch from additive multigrid over a hierarchical basis method into a Bramble-Pasciak-Xu (BPX)-type solver, with several multiscale smoothing variants within one code base. Pipelining allows us to realize full approximation storage (FAS) within the additive environment where, amortized, each grid vertex carrying degrees of freedom is read/written only once per iteration. The codes realize a single-touch policy. Among the features facilitated by matrix-free FAS is arbitrary dynamic mesh refinement (AMR) for all solver variants. AMR as an enabler for full multigrid (FMG) cycling—the grid unfolds throughout the computation—allows us to reduce the cost per unknown. The present work primary contributes toward software realization and design questions. Our experiments show that the consolidation of single-touch FAS, dynamic AMR, and vectorization-friendly, complex scaled, matrix-free FMG cycles delivers a mature implementation blueprint for solvers of Helmholtz equations in general. For this blueprint, we put particular emphasis on a strict implementation formalism as well as some implementation correctness proofs

A Multilevel in Space and Energy Solver for Multigroup Diffusion and Coarse Mesh Finite Difference Eigenvalue Problems

In reactor physics, the efficient solution of the multigroup neutron diffusion eigenvalue problem is desired for various applications. The diffusion problem is a lower-order but reasonably accurate approximation to the higher-fidelity multigroup neutron transport eigenvalue problem. In cases where the full-fidelity of the transport solution is needed, the solution of the diffusion problem can be used to accelerate the convergence of transport solvers via methods such as Coarse Mesh Finite Difference (CMFD). The diffusion problem can have O(108) unknowns, and, despite being orders of magnitude smaller than a typical transport problem, obtaining its solution is still not a trivial task. In the Michigan Parallel Characteristics Transport (MPACT) code, the lack of an efficient CMFD solver has resulted in a computational bottleneck at the CMFD step. Solving the CMFD system can comprise 50% or more of the overall runtime in MPACT when the de facto default CMFD solver is used; addressing this bottleneck is the motivation for our work. The primary focus of this thesis is the theory, development, implementation, and testing of a new Multilevel-in-Space-and-Energy Diffusion (MSED) method for efficiently solving multigroup diffusion and CMFD eigenvalue problems. As its name suggests, MSED efficiently converges multigroup diffusion and CMFD problems by leveraging lower-order systems with coarsened energy and/or spatial grids. The efficiency of MSED is verified via various Fourier analyses of its components and via testing in a 1-D diffusion code. In the later chapters of this thesis, the MSED method is tested on a variety of reactor problems in MPACT. Compared to the default CMFD solver, our implementation of MSED in MPACT has resulted in an ~8-12x reduction in the CMFD runtime required by MPACT for single statepoint calculations on 3-D, full-core, 51-group reactor models. The number of transport sweeps is also typically reduced by the use of MSED, which is able to better converge the CMFD system than the default CMFD solver. This leads to a further savings in overall runtime that is not captured by the differences in CMFD runtime.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146075/1/bcyee_1.pd

Additive and Multiplicative Multi-Grid - a Comparison

Introduction In recent years, multi-grid methods have become powerful tools for solving problems from many scientific and technical applications. As methods of optimal complexity, they contribute substantially to solving very large problems on supercomputers as well as medium-scale problems on workstations. In practical problems, features like robustness are crucial for the actual performance of the method. There are two main components of the multi-grid method, smoothing and coarse-grid correction. Particularly in view of robustness, smoothing plays a very important rôle. In combination with adaptive grid refinement, many multi-grid variants have been developed. In the present paper, we describe and classify these numerous variants such as BPX, hierarchical basis, HBMG, local multi-grid etc. in the classical multi-grid framework. We compare additive and multiplicative multi-grid and investigate in particular their different behaviour with respect to smoothing. Theoretical as w