GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable.
This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.
","abstract_html":"<p>GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable.</p> <p>This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.</p>","abstract_has_math":false,"creators":["Winkleblack, Scott Kenneth Swinkleb"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chris Lupo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-06-01T07:00:00Z","date_published":"2014-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:55Z","subjects":["Heterogeneous computing","Bayes methods","Bioinformatics","Computer and Systems Architecture","Other Computer Sciences","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.81"],"render_values":[{"text":"10.15368/theses.2014.81","href":"https://doi.org/10.15368/theses.2014.81","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1236","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chris Lupo"]},{"key":"dc:creator","label":"Author","values":["Winkleblack, Scott Kenneth Swinkleb"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-06-12T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Heterogeneous computing","Bayes methods","Bioinformatics","Computer and Systems Architecture","Other Computer Sciences","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1236","10.15368/theses.2014.81"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable.
This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.
"]},{"key":"dc:title","label":"Title","values":["ReGen: Optimizing Genetic Selection Algorithms for Heterogeneous Computing"]}]}],"canonical_facts":{"dc:contributor":["Chris Lupo"],"dc:creator":["Winkleblack, Scott Kenneth Swinkleb"],"dc:date.available":["2014-06-12T07:00:00Z"],"dc:description.abstract":["GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable.
This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1236","10.15368/theses.2014.81"],"dc:subject":["Heterogeneous computing","Bayes methods","Bioinformatics","Computer and Systems Architecture","Other Computer Sciences","Theory and Algorithms"],"dc:title":["ReGen: Optimizing Genetic Selection Algorithms for Heterogeneous Computing"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:55Z"}