dc.contributor.advisor |
Norris, Boyana |
|
dc.contributor.author |
Sood, Kanika |
|
dc.date.accessioned |
2019-09-18T19:29:48Z |
|
dc.date.available |
2019-09-18T19:29:48Z |
|
dc.date.issued |
2019-09-18 |
|
dc.identifier.uri |
https://scholarsbank.uoregon.edu/xmlui/handle/1794/24931 |
|
dc.description.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 |
en_US |
dc.language.iso |
en_US |
|
dc.publisher |
University of Oregon |
|
dc.rights |
All Rights Reserved. |
|
dc.subject |
machine learning |
en_US |
dc.subject |
numerical library |
en_US |
dc.subject |
PETSc |
en_US |
dc.subject |
solver selection |
en_US |
dc.subject |
sparse linear systems |
en_US |
dc.title |
Iterative Solver Selection Techniques for Sparse Linear Systems |
|
dc.type |
Electronic Thesis or Dissertation |
|
thesis.degree.name |
Ph.D. |
|
thesis.degree.level |
doctoral |
|
thesis.degree.discipline |
Department of Computer and Information Science |
|
thesis.degree.grantor |
University of Oregon |
|