Date of Award

4-2016

Degree Type

Thesis

Degree Name

Computer Information Systems (M.S.)

Department

School of Computing and Information Systems

First Advisor

Gregory Wolffe

Second Advisor

Christian Trefftz

Third Advisor

David Zeitler

Abstract

Coalescent genealogy samplers are effective tools for the study of population genetics. They are used to estimate the historical parameters of a population based upon the sampling of present-day genetic information. A popular approach employs Markov chain Monte Carlo (MCMC) methods. While effective, these methods are very computationally intensive, often taking weeks to run. Although attempts have been made to leverage parallelism in an effort to reduce runtimes, they have not resulted in scalable solutions. Due to the inherently sequential nature of MCMC methods, their performance has suffered diminishing returns when applied to large-scale computing clusters. In the interests of reduced runtimes and higher quality solutions, a more sophisticated form of parallelism is required. This paper describes a novel way to apply a recently discovered generalization of MCMC for this purpose. The new approach exploits the multiple-proposal mechanism of the generalized method to enable the desired scalable parallelism while maintaining the accuracy of the original technique. 4

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