Abstract
This paper discusses the application of a few parallel preconditioning techniques, which are collected in a recently developed suite of codes Parallel Algebraic Recursive Multilevel Solver (pARMS), to tackling large-scale sparse linear systems arising from real-life applications. In particular, we study the effect of different algorithmic variations and parameter choices on the overall performance of the distributed preconditioners in pARMS by means of numerical experiments related to a few realistic applications. These applications include magnetohydrodynamics, nonlinear acoustic field simulation, and tire design.
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