Degree Type

Dissertation

Date of Award

2019

Degree Name

Doctor of Philosophy

Department

Mathematics

Major

Applied Mathematics

First Advisor

Glenn R. Luecke

Abstract

This work is comprised of two different projects in numerical linear algebra. The first project is about using machine learning to speed up dense matrix-matrix multiplication computations on a shared-memory computer architecture. We found that found basic loop-based matrix-matrix multiplication algorithms tied to a decision tree algorithm selector were competitive to using Intel's Math Kernel Library for the same computation. The second project is a preliminary report about re-implementing an encoding format for spare matrix-vector multiplication called Compressed Spare eXtended (CSX). The goal for the second project is to use machine learning to aid in encoding matrix substructures in the CSX format without using exhaustive search and a Just-In-Time compiler.

Copyright Owner

Brandon Groth

Language

en

File Format

application/pdf

File Size

88 pages

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