Degree
Master of Science
Program
Computer Science
Supervisor
Dr. Peter K. Rogan
Abstract
Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes.
Recommended Citation
Li, Yanxin, "Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis" (2013). Electronic Thesis and Dissertation Repository. 1230.
https://ir.lib.uwo.ca/etd/1230
A pdf version with bookmarks and clear images
Included in
Biochemistry Commons, Other Computer Sciences Commons, Programming Languages and Compilers Commons, Software Engineering Commons