DOI: https://doi.org/10.15368/theses.2014.81
Available at: https://digitalcommons.calpoly.edu/theses/1236
Date of Award
6-2014
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
Chris Lupo
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.
Included in
Computer and Systems Architecture Commons, Other Computer Sciences Commons, Theory and Algorithms Commons