Date of Final Oral Examination (Defense)
Type of Culminating Activity
Doctor of Philosophy in Computing
Catherine R.M. Olschanowsky, Ph.D.
Elena Sherman, Ph.D.
Tim Andersen, Ph.D.
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.
Popoola, Tobi Goodness, "Sparse Format Conversion and Code Synthesis" (2023). Boise State University Theses and Dissertations. 2075.