Publication Date

5-2023

Date of Final Oral Examination (Defense)

February 2023

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Computing

Department

Computer Science

Major Advisor

Catherine R.M. Olschanowsky, Ph.D.

Advisor

Elena Sherman, Ph.D.

Advisor

Tim Andersen, Ph.D.

Abstract

Sparse computations are important in scientific computing. Many scientific applications compute on sparse data. Data is said to be sparse if it has a relatively small number of non-zeros. Sparse formats use auxiliary arrays to store non-zeros, as a result, the contents of auxiliary arrays are not known until run-time. The Inspector/Executor (I/E) paradigm uses run-time information for compiler optimizations. An inspector computes information at run-time to drive transformations. The executor---a compile-time transformation of the original code--- uses information computed by the inspector. The sparse polyhedral framework (SPF) encompasses a series of tools to support I/E run-time transformations. This work introduces a unified framework that wraps SPF tools while providing a holistic view of computation as an intermediate representation (IR). This work also introduces a method to automatically synthesize inspectors to transform between sparse formats and improvements to SPF to explore the performance of irregular applications.

Comments

Tobi Goodness Popoola, ORCID: https://orcid.org/0000-0001-5944-1570

DOI

https://doi.org/10.18122/td.2075.boisestate

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