Electronic Thesis and Dissertation Repository

Degree

Master of Science

Program

Computer Science

Supervisor

Dr. Robert E. Mercer

Abstract

Answer Set Programming is a declarative modeling paradigm enabling specialists in diverse disciplines to describe and solve complicated problems. Growth in high performance computing is driving ever smarter and more scalable parallel answer set solvers. To improve on today's cutting-edge, researchers need to develop increasingly intelligent methods for analysis of a solver's runtime information. Reflecting on the solver's search state typically pauses its progress until the analysis is complete. This work introduces methods from the domain of parallel functional programming and immutable type theory to construct a representation of the search state that is both amenable to introspection and efficiently scalable across multiple processor cores.


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