Nonlinear FETI-DP and BDDC Methods

In the simulation of deformation processes in material science the consideration of a microscopic material structure is often necessary, as in the simulation of modern high strength steels. A straightforward finite element discretization of the complete deformed body resolving the microscopic structure leads to very large nonlinear problems and a solution is out of reach, even on modern supercomputers. In homogenization approaches, as the computational scale bridging approach FE2, the macroscopic scale of the deformed object is decoupled from the microscopic scale of the material structure. These approaches only consider the microstructure in a localized fashion on independent and parallel representative volume elements (RVEs). This introduces massive parallelism on the macroscopic level and is thus ideal for modern computer architectures with large numbers of parallel computational cores. Nevertheless, the discretization of an RVE can still result in large nonlinear problems and thus highly scalable parallel solvers are necessary. In this context, nonlinear FETI-DP (Finite Element Tearing and Interconnecting - Dual-Primal) and BDDC (Balancing Domain Decomposition by Constraints) domain decomposition methods are discussed in this thesis, which are parallel solution methods for nonlinear problems arising from a finite element discretization. These approaches can be viewed as a strategies to further localize the computational work and to extend the parallel scalability of classical FETI-DP and BDDC methods towards extreme-scale supercomputers. Also variants providing an inexact solution of the FETI-DP coarse problem are considered in this thesis, combining two successful paradigms, i.e., nonlinear domain decomposition and AMG (Algebraic Multigrid). An efficient implementation of the resulting inexact reduced Nonlinear-FETI-DP-1 method is presented and scalability beyond 200,000 computational cores is showed. Finally, a highly scalable FE2 implementation using recent inexact reduced FETI-DP methods to solve the RVE problems on the microscopic level is presented and scalability on all 458,752 cores of the JUQUEEN BlueGene/Q system at Forschungszentrum Jülich is demonstrated

Adaptive Nonlinear Elimination in Nonlinear FETI-DP Methods

Highly scalable and robust Newton-Krylov domain decomposition approaches are widely used for the solution of nonlinear implicit problems. In these methods, the nonlinear problem is first linearized and then decomposed into subdomains. By changing this order and first decomposing the nonlinear problem, many nonlinear domain decomposition methods have been designed in the last two decades. These methods often show a higher robustness compared to classical Newton-Krylov variants due to a better resolution of nonlinear effects. Additionally, the balance between local work, communication, and synchronization is usually more favorable for modern computer architectures. In all our nonlinear FETI-DP methods, we introduce a nonlinear right-preconditioner that can be interpreted as a (partial) nonlinear elimination of variables. The choice of the elimination set has a huge impact on the nonlinear convergence behavior. In order to design a nonlinear FETI-DP method that is tailored to arbitrary problems, we introduce a strategy, based on the residual of the nonlinear saddle point system, to adaptively choose sets of variables for the nonlinear elimination. The new strategy is applied to challenging distributions of nonlinearity in problems based on the p-Laplace operator. Promising numerical results are presented

Linear and nonlinear substructured Restricted Additive Schwarz iterations and preconditioning

Iterative substructuring Domain Decomposition (DD) methods have been extensively studied, and they are usually associated with nonoverlapping decompositions. It is less known that classical overlapping DD methods can also be formulated in substructured form, i.e., as iterative methods acting on variables defined exclusively on the interfaces of the overlapping domain decomposition. We call such formulations substructured domain decomposition methods. We introduce here a substructured version of Restricted Additive Schwarz (RAS) which we call SRAS. We show that RAS and SRAS are equivalent when used as iterative solvers, as they produce the same iterates, while they are substantially different when used as preconditioners for GMRES. We link the volume and substructured Krylov spaces and show that the iterates are different by deriving the least squares problems solved at each GMRES iteration. When used as iterative solvers, SRAS presents computational advantages over RAS, as it avoids computations with matrices and vectors at the volume level. When used as preconditioners, SRAS has the further advantage of allowing GMRES to store smaller vectors and perform orthogonalization in a lower dimensional space. We then consider nonlinear problems, and we introduce SRASPEN (Substructured Restricted Additive Schwarz Preconditioned Exact Newton), where SRAS is used as a preconditioner for Newton’s method. In contrast to the linear case, we prove that Newton’s method applied to the preconditioned volume and substructured formulation produces the same iterates in the nonlinear case. Next, we introduce two-level versions of nonlinear SRAS and SRASPEN. Finally, we validate our theoretical results with numerical experiments

Efficient Adaptive Elimination Strategies in Nonlinear FETI-DP Methods in Combination with Adaptive Spectral Coarse Spaces

Nonlinear FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) is a nonlinear nonoverlapping domain decomposition method (DDM) which has a superior nonlinear convergence behavior compared with classical Newton-Krylov-DDMs - at least for many problems. Its fast and robust nonlinear convergence is strongly influenced by the choice of the second level or, in other words, the choice of the coarse constraints. Additionally, the convergence is also affected by the choice of an elimination set, that is, a set of degrees of freedom which are eliminated nonlinearly before linearization. In this article, an adaptive coarse space is combined with a problem-dependent and residual-based choice of the elimination set. An efficient implementation exploiting sparse local saddle point problems instead of an explicit transformation of basis is used. Unfortunately, this approach makes a further adaption of the elimination sets necessary, that is, edges and faces with coarse constraints have to be either included in the elimination set completely or not at all. Different strategies to fulfill this additional constraint are discussed and compared with a solely residual-based approach. The latter approach has to be implemented with an explicit transformation of basis. In general, the residual which is used to choose the elimination set has to be transformed to a space which basis functions explicitly contain the coarse constraints. This is computationally expensive. Here, for the first time, it is suggested to use an approximation of the transformed residual instead to compute the elimination set

Energy Efficiency of Nonlinear Domain Decomposition Methods

A nonlinear domain decomposition (DD) solver is considered with respect to improved energy efficiency. In this method, nonlinear problems are solved using Newton’s method on the subdomains in parallel and in asynchronous iterations. The method is compared to the more standard Newton-Krylov approach, where a linear domain decomposition solver is applied to the overall nonlinear problem after linearization using Newton’s method. It is found that in the nonlinear domain decomposition method, making use of the asynchronicity, some processor cores can be set to sleep to save energy and to allow better use of the power and thermal budget. Energy savings up to 77% are observed compared to the more traditional Newton-Krylov approach, which is synchronous by design, using up to 5120 Intel Broadwell (Xeon E5-2630v4) cores. The total time to solution is not affected. On the contrary, remaining cores of the same processor may be able to go to turbo mode, thus reducing the total time to solution slightly. Last, we consider the same strategy for the ASPIN (Additive Schwarz Preconditioned Inexact Newton) nonlinear domain decomposition method and observe a similar potential to save energy

Substructured formulations of nonlinear structure problems - influence of the interface condition

We investigate the use of non-overlapping domain decomposition (DD) methods for nonlinear structure problems. The classic techniques would combine a global Newton solver with a linear DD solver for the tangent systems. We propose a framework where we can swap Newton and DD, so that we solve independent nonlinear problems for each substructure and linear condensed interface problems. The objective is to decrease the number of communications between subdomains and to improve parallelism. Depending on the interface condition, we derive several formulations which are not equivalent, contrarily to the linear case. Primal, dual and mixed variants are described and assessed on a simple plasticity problem.Comment: in International Journal for Numerical Methods in Engineering, Wiley, 201

Nonlinear parallel-in-time Schur complement solvers for ordinary differential equations

In this work, we propose a parallel-in-time solver for linear and nonlinear ordinary differential equations. The approach is based on an efficient multilevel solver of the Schur complement related to a multilevel time partition. For linear problems, the scheme leads to a fast direct method. Next, two different strategies for solving nonlinear ODEs are proposed. First, we consider a Newton method over the global nonlinear ODE, using the multilevel Schur complement solver at every nonlinear iteration. Second, we state the global nonlinear problem in terms of the nonlinear Schur complement (at an arbitrary level), and perform nonlinear iterations over it. Numerical experiments show that the proposed schemes are weakly scalable, i.e., we can efficiently exploit increasing computational resources to solve for more time steps the same problem.Peer ReviewedPostprint (author's final draft

Space-time balancing domain decomposition

No separate or additional fees are collected for access to or distribution of the work.In this work, we propose two-level space-time domain decomposition preconditioners for parabolic problems discretized using finite elements. They are motivated as an extension to space-time of balancing domain decomposition by constraints preconditioners. The key ingredients to be defined are the subassembled space and operator, the coarse degrees of freedom (DOFs) in which we want to enforce continuity among subdomains at the preconditioner level, and the transfer operator from the subassembled to the original finite element space. With regard to the subassembled operator, a perturbation of the time derivative is needed to end up with a well-posed preconditioner. The set of coarse DOFs includes the time average (at the space-time subdomain) of classical space constraints plus new constraints between consecutive subdomains in time. Numerical experiments show that the proposed schemes are weakly scalable in time, i.e., we can efficiently exploit increasing computational resources to solve more time steps in the same total elapsed time. Further, the scheme is also weakly space-time scalable, since it leads to asymptotically constant iterations when solving larger problems both in space and time. Excellent wall clock time weak scalability is achieved for space-time parallel solvers on some thousands of coresPeer ReviewedPostprint (published version