{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-1174"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-1174","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"Accelerating Finite State Projection through General Purpose Graphics Processing","abstract":"The finite state projection algorithm provides modelers a new way of directly solving the chemical master equation. The algorithm utilizes the matrix exponential function, and so the algorithm’s performance suffers when it is applied to large problems. Other work has been done to reduce the size of the exponentiation through mathematical simplifications, but efficiently exponentiating a large matrix has not been explored. This work explores implementing the finite state projection algorithm on several different high-performance computing platforms as a means of efficiently calculating the matrix exponential function for large systems. This work finds that general purpose graphics processing can accelerate the finite state projection algorithm by several orders of magnitude. Specific biological models and modeling techniques are discussed as a demonstration of the algorithm implemented on a general purpose graphics processor. The results of this work show that general purpose graphics processing will be a key factor in modeling more complex biological systems.","abstract_html":"The finite state projection algorithm provides modelers a new way of directly solving the chemical master equation. The algorithm utilizes the matrix exponential function, and so the algorithm’s performance suffers when it is applied to large problems. Other work has been done to reduce the size of the exponentiation through mathematical simplifications, but efficiently exponentiating a large matrix has not been explored. This work explores implementing the finite state projection algorithm on several different high-performance computing platforms as a means of efficiently calculating the matrix exponential function for large systems. This work finds that general purpose graphics processing can accelerate the finite state projection algorithm by several orders of magnitude. Specific biological models and modeling techniques are discussed as a demonstration of the algorithm implemented on a general purpose graphics processor. The results of this work show that general purpose graphics processing will be a key factor in modeling more complex biological systems.","abstract_has_math":false,"creators":["Trimeloni, Thomas"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":["James McCollum"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-04-07T07:00:00Z","date_published":"2011-04-07T07:00:00Z","updated_at":"2026-07-24T05:53:26Z","subjects":["Finite State Projection","Systems Biology","High-Performance Computing","Graphics Processing","Modeling","Gillespie Algorithm","Chemical Master Equation","Differential Equations","Stochastic Simulation","Engineering"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/175"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/175","href":"https://scholarscompass.vcu.edu/etd/175","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/Z2F8-6156","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["James McCollum"]},{"key":"dc:creator","label":"Author","values":["Trimeloni, Thomas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-04-18T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Finite State Projection","Systems Biology","High-Performance Computing","Graphics Processing","Modeling","Gillespie Algorithm","Chemical Master Equation","Differential Equations","Stochastic Simulation","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["© The Author"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.25772/Z2F8-6156","https://scholarscompass.vcu.edu/etd/175"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The finite state projection algorithm provides modelers a new way of directly solving the chemical master equation. The algorithm utilizes the matrix exponential function, and so the algorithm’s performance suffers when it is applied to large problems. Other work has been done to reduce the size of the exponentiation through mathematical simplifications, but efficiently exponentiating a large matrix has not been explored. This work explores implementing the finite state projection algorithm on several different high-performance computing platforms as a means of efficiently calculating the matrix exponential function for large systems. This work finds that general purpose graphics processing can accelerate the finite state projection algorithm by several orders of magnitude. Specific biological models and modeling techniques are discussed as a demonstration of the algorithm implemented on a general purpose graphics processor. The results of this work show that general purpose graphics processing will be a key factor in modeling more complex biological systems."]},{"key":"dc:title","label":"Title","values":["Accelerating Finite State Projection through General Purpose Graphics Processing"]}]}],"canonical_facts":{"dc:contributor":["James McCollum"],"dc:creator":["Trimeloni, Thomas"],"dc:date.available":["2016-04-18T07:00:00Z"],"dc:description.abstract":["The finite state projection algorithm provides modelers a new way of directly solving the chemical master equation. The algorithm utilizes the matrix exponential function, and so the algorithm’s performance suffers when it is applied to large problems. Other work has been done to reduce the size of the exponentiation through mathematical simplifications, but efficiently exponentiating a large matrix has not been explored. This work explores implementing the finite state projection algorithm on several different high-performance computing platforms as a means of efficiently calculating the matrix exponential function for large systems. This work finds that general purpose graphics processing can accelerate the finite state projection algorithm by several orders of magnitude. Specific biological models and modeling techniques are discussed as a demonstration of the algorithm implemented on a general purpose graphics processor. The results of this work show that general purpose graphics processing will be a key factor in modeling more complex biological systems."],"dc:identifier":["https://doi.org/10.25772/Z2F8-6156","https://scholarscompass.vcu.edu/etd/175"],"dc:rights":["© The Author"],"dc:subject":["Finite State Projection","Systems Biology","High-Performance Computing","Graphics Processing","Modeling","Gillespie Algorithm","Chemical Master Equation","Differential Equations","Stochastic Simulation","Engineering"],"dc:title":["Accelerating Finite State Projection through General Purpose Graphics Processing"],"thesis:degree_discipline":["Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:53:26Z"}