Volume 49, pp. 244-273, 2018.

Nonlinear BDDC Methods with approximate solvers

Axel Klawonn, Martin Lanser, and Oliver Rheinbach

Abstract

New nonlinear BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods using inexact solvers for the subdomains and the coarse problem are proposed. In nonlinear domain decomposition methods, the nonlinear problem is decomposed before linearization to improve concurrency and robustness. For linear problems, the new methods are equivalent to known inexact BDDC methods. The new approaches are therefore discussed in the context of other known inexact BDDC methods for linear problems. Relations are pointed out, and the advantages of the approaches chosen here are highlighted. For the new approaches, using an algebraic multigrid method as a building block, parallel scalability is shown for more than half a million ($524\,288$) MPI ranks on the JUQUEEN IBM BG/Q supercomputer (JSC Jülich, Germany) and on up to $193\,600$ cores of the Theta Xeon Phi supercomputer (ALCF, Argonne National Laboratory, USA), which is based on the recent Intel Knights Landing (KNL) many-core architecture. One of our nonlinear inexact BDDC domain decomposition methods is also applied to three-dimensional plasticity problems. Comparisons to standard Newton-Krylov-BDDC methods are provided.

Full Text (PDF) [2.2 MB], BibTeX

Key words

nonlinear BDDC, nonlinear domain decomposition, nonlinear elimination, Newton's method, nonlinear problems, parallel computing, inexact BDDC, nonlinear elasticity, plasticity

AMS subject classifications

68W10, 68U20, 65N55, 65F08, 65Y05

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