The field of evolutionary biology has become reliant on the ability to quickly and accurately infer phylogenies, but current software solutions for phylogenetic inference are inadequate to keep up with the growing demands of biologists. FPGA co-processor architectures have been shown to effectively speed up the performance of software applications by performing the most time-consuming parts of the algorithm on custom hardware. The goal of this thesis is to investigate a reconfigurable hardware implementation of the popular phylogenetic inference software MrBayes. This work details the development of an FPGA design that works with the MrBayes software to perform tree likelihood calculations that are needed for the algorithm. The design is implemented and tested in order to study the potential of reconfigurable hardware to accelerate MrBayes.
","abstract_html":"<p>The field of evolutionary biology has become reliant on the ability to quickly and accurately infer phylogenies, but current software solutions for phylogenetic inference are inadequate to keep up with the growing demands of biologists. FPGA co-processor architectures have been shown to effectively speed up the performance of software applications by performing the most time-consuming parts of the algorithm on custom hardware. The goal of this thesis is to investigate a reconfigurable hardware implementation of the popular phylogenetic inference software MrBayes. This work details the development of an FPGA design that works with the MrBayes software to perform tree likelihood calculations that are needed for the algorithm. The design is implemented and tested in order to study the potential of reconfigurable hardware to accelerate MrBayes.</p>","abstract_has_math":false,"creators":["Zierke, Stephanie"],"institution":null,"degree_name":"MS","degree_level":"Campus Access Thesis","degree_discipline":"Computer Science and Engineering","degree_department":null,"school":null,"contributors":["Jason D. Bakos"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-01T08:00:00Z","date_published":"2009-01-01T08:00:00Z","updated_at":"2026-07-24T04:36:43Z","subjects":["Computer Sciences","Electrical and Computer Engineering","Engineering","Physical Sciences and Mathematics"],"languages":[],"rights":["© 2009, Stephanie Zierke"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarcommons.sc.edu/etd/120","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jason D. Bakos"]},{"key":"dc:creator","label":"Author","values":["Zierke, Stephanie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Sciences","Electrical and Computer Engineering","Engineering","Physical Sciences and Mathematics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© 2009, Stephanie Zierke"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarcommons.sc.edu/etd/120"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The field of evolutionary biology has become reliant on the ability to quickly and accurately infer phylogenies, but current software solutions for phylogenetic inference are inadequate to keep up with the growing demands of biologists. FPGA co-processor architectures have been shown to effectively speed up the performance of software applications by performing the most time-consuming parts of the algorithm on custom hardware. The goal of this thesis is to investigate a reconfigurable hardware implementation of the popular phylogenetic inference software MrBayes. This work details the development of an FPGA design that works with the MrBayes software to perform tree likelihood calculations that are needed for the algorithm. The design is implemented and tested in order to study the potential of reconfigurable hardware to accelerate MrBayes.
"]},{"key":"dc:title","label":"Title","values":["A Reconfigurable Implementation of Bayesian Phylogenetic Inference"]}]}],"canonical_facts":{"dc:contributor":["Jason D. Bakos"],"dc:creator":["Zierke, Stephanie"],"dc:description.abstract":["The field of evolutionary biology has become reliant on the ability to quickly and accurately infer phylogenies, but current software solutions for phylogenetic inference are inadequate to keep up with the growing demands of biologists. FPGA co-processor architectures have been shown to effectively speed up the performance of software applications by performing the most time-consuming parts of the algorithm on custom hardware. The goal of this thesis is to investigate a reconfigurable hardware implementation of the popular phylogenetic inference software MrBayes. This work details the development of an FPGA design that works with the MrBayes software to perform tree likelihood calculations that are needed for the algorithm. The design is implemented and tested in order to study the potential of reconfigurable hardware to accelerate MrBayes.
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