Iterative Solver Selection Techniques for Sparse Linear Systems

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Date

2019-09-18

Authors

Sood, Kanika

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Publisher

University of Oregon

Abstract

Scientific and engineering applications are dominated by linear algebra and depend on scalable solutions of sparse linear systems. For large problems, preconditioned iterative methods are a popular choice. High-performance numerical libraries offer a variety of preconditioned Newton-Krylov methods for solving sparse problems. However, the selection of a well-performing Krylov method remains to be the user’s responsibility. This research presents the technique for choosing well-performing parallel sparse linear solver methods, based on the problem characteristics and the amount of communication involved in the Krylov methods

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Keywords

machine learning, numerical library, PETSc, solver selection, sparse linear systems

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