Adaptive multipreconditioned FETI: Scalability results and robustness assessment

•The robustness and efficiency of the adaptive multipreconditioned FETI is assessed.•Adaptive multipreconditioned FETI solves critical problems in significantly fewer iterations than classical FETI.•Tests went up to 500 subdomains and more than 3500 cores, without any performance drop. The purpose of this article is to assess the adaptive multipreconditioned FETI solvers (AMPFETI) on real industrial problems and hardware. The multipreconditioned FETI algorithm (first introduced as Simultaneous FETI (Gosselet et al., 2015) is a non-overlapping domain decomposition method which exhibits good robustness properties without requiring the explicit knowledge of the original partial differential equation, or any a priori analysis of the algebraic system through eigenvalues problems. Multipreconditioned FETI solves critical problems in significantly fewer iterations than classical FETI but each iteration involves a larger computational effort. An adaptive strategy (known as the adaptive multipreconditioned conjugate gradient algorithm (Spillane, 2016) has been proposed to achieve balance between robustness and efficiency and we will observe that it provides an efficient solver for the problems considered here.