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December 6, 2019 @ 3:00 pm - 5:00 pm MST
Title: Polyhedral+Dataflow Graphs
Program: Doctor of Philosophy in Computing
Advisor: Dr. Catherine Olschanowsky, Computer Science
Committee Members: Dr. Donna Calhoun, Mathematics, Dr. Steven Cutchin, Computer Science, and Dr. Elena Sherman, Computer Science
This research presents an intermediate compiler representation that is designed for optimization, and emphasizes the temporary storage requirements and execution schedule of a given computation to guide optimization decisions. The representation is expressed as a dataflow graph that describes computational statements and data mappings within the polyhedral model. The targeted applications include both the
regular and irregular scientific domains.
The intermediate representation can be integrated into existing compiler infrastructures. A specification language describes the graph components and the transformations that can be applied. The visual representation allows users to reason about optimizations. Graph variants can be translated into source code or other representations. An embedded domain specific language in C++ has been implemented to describe the computations and construct the graphs. The language, intermediate representation, and associated transformations have been applied to improve the performance of differential equation solvers, sparse matrix operations, tensor decomposition, and structured multigrid methods.