Iterative Solver Selection Techniques for Sparse Linear Systems
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Date
2019-09-18
Authors
Sood, Kanika
Journal Title
Journal ISSN
Volume Title
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