Date of Award

Spring 1-1-2017

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Elizabeth Jessup

Second Advisor

Ian Karlin

Third Advisor

Boyana Norris

Fourth Advisor

Xiao-Chuan Cai

Fifth Advisor

Clayton Lewis

Abstract

Solving sparse systems of linear equations is a commonly encountered computation in scientific and high-performance computing applications. Applications that depend on solving sparse linear systems as part of their workflow can spend a large percentage of their total runtime solving sparse systems. However, selecting the best iterative solver and preconditioner for solving a given sparse linear system, especially for novice users, is not a simple task. To address this problem, previous works have used machine learning techniques to find similarities between sparse matrices and the corresponding performance that solver-preconditioner pairs have on solving the resulting linear systems. This dissertation expands on existing work by introducing new techniques that incorporate hardware information into the prediction of ideal iterative linear solver and preconditioners for sparse linear systems. By accounting for hardware, it is possible to create more specially tailored solver-preconditioner recommendations for a novice user.

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