Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biological data. Some of these statistical models lend themselves nicely to standard high performance computing optimizations such as parallelism, while others do not. One such algorithm is Markov Chain Monte Carlo (MCMC). In this thesis, we present a heterogeneous compute solution for optimizing GenSel, a genetic selection analysis tool. GenSel utilizes a MCMC algorithm to perform Bayesian inference using Gibbs sampling.
Optimizing an MCMC algorithm is a difficult problem because it is inherently sequential, containing a loop carried dependence between each Markov Chain iteration. The optimization presented in this thesis utilizes GPU computing to exploit the data-level parallelism within each of these iterations. In addition, it allows for the efficient management of memory, the pipelining of CUDA kernels, and the use of multiple GPUs. The optimizations presented show performance improvements of up to 1.84 times that of the original algorithm.
","abstract_html":"<p>Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biological data. Some of these statistical models lend themselves nicely to standard high performance computing optimizations such as parallelism, while others do not. One such algorithm is Markov Chain Monte Carlo (MCMC). In this thesis, we present a heterogeneous compute solution for optimizing GenSel, a genetic selection analysis tool. GenSel utilizes a MCMC algorithm to perform Bayesian inference using Gibbs sampling.</p> <p>Optimizing an MCMC algorithm is a difficult problem because it is inherently sequential, containing a loop carried dependence between each Markov Chain iteration. The optimization presented in this thesis utilizes GPU computing to exploit the data-level parallelism within each of these iterations. In addition, it allows for the efficient management of memory, the pipelining of CUDA kernels, and the use of multiple GPUs. The optimizations presented show performance improvements of up to 1.84 times that of the original algorithm.</p>","abstract_has_math":false,"creators":["Devore, Trevor"],"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":"2026-07-24T01:32:37Z","subjects":["Bioinformatics","Multi-GPU","Markov Chain Monte Carlo"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.74"],"render_values":[{"text":"10.15368/theses.2014.74","href":"https://doi.org/10.15368/theses.2014.74","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1228","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":["Devore, Trevor"]}]},{"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":["Bioinformatics","Multi-GPU","Markov Chain Monte Carlo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1228","10.15368/theses.2014.74"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biological data. Some of these statistical models lend themselves nicely to standard high performance computing optimizations such as parallelism, while others do not. One such algorithm is Markov Chain Monte Carlo (MCMC). In this thesis, we present a heterogeneous compute solution for optimizing GenSel, a genetic selection analysis tool. GenSel utilizes a MCMC algorithm to perform Bayesian inference using Gibbs sampling.
Optimizing an MCMC algorithm is a difficult problem because it is inherently sequential, containing a loop carried dependence between each Markov Chain iteration. The optimization presented in this thesis utilizes GPU computing to exploit the data-level parallelism within each of these iterations. In addition, it allows for the efficient management of memory, the pipelining of CUDA kernels, and the use of multiple GPUs. The optimizations presented show performance improvements of up to 1.84 times that of the original algorithm.
"]},{"key":"dc:title","label":"Title","values":["A Multi-GPU Compute Solution for Optimized Genomic Selection Analysis"]}]}],"canonical_facts":{"dc:contributor":["Chris Lupo"],"dc:creator":["Devore, Trevor"],"dc:date.available":["2014-06-12T07:00:00Z"],"dc:description.abstract":["Many modern-day Bioinformatics algorithms rely heavily on statistical models to analyze their biological data. Some of these statistical models lend themselves nicely to standard high performance computing optimizations such as parallelism, while others do not. One such algorithm is Markov Chain Monte Carlo (MCMC). In this thesis, we present a heterogeneous compute solution for optimizing GenSel, a genetic selection analysis tool. GenSel utilizes a MCMC algorithm to perform Bayesian inference using Gibbs sampling.
Optimizing an MCMC algorithm is a difficult problem because it is inherently sequential, containing a loop carried dependence between each Markov Chain iteration. The optimization presented in this thesis utilizes GPU computing to exploit the data-level parallelism within each of these iterations. In addition, it allows for the efficient management of memory, the pipelining of CUDA kernels, and the use of multiple GPUs. The optimizations presented show performance improvements of up to 1.84 times that of the original algorithm.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1228","10.15368/theses.2014.74"],"dc:subject":["Bioinformatics","Multi-GPU","Markov Chain Monte Carlo"],"dc:title":["A Multi-GPU Compute Solution for Optimized Genomic Selection Analysis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:37Z"}