Date of Award
Fall 2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematical and Statistical Sciences
Program
Bioinformatics
First Advisor
Scharer, Gunter
Second Advisor
Nie, Qian
Third Advisor
Bozdag, Serdar
Abstract
In recent years, high performance computing (HPC) has begun to revolutionize the architecture of software and servers to meet the ever-increasing demand for speed & efficiency. One of the ways this change is manifesting is the adoption of graphics processor units (GPUs). Used correctly, GPUS can increase throughput and decrease compute time for certain computational problems. Bioinformatics, an HPC dependent discipline, is no exception. As bioinformatics continues advance clinical care by sequencing patient’s DNA and RNA for diagnosis of diseases, there is an ever-increasing demand for faster data processing to improve clinical sequencing turnaround time. Parabricks, a GPU enabled bioinformatics software is one of the leaders in ‘lifting over’ common CPU bioinformatics tools to GPU architectures. In the present study, bioinformatics pipelines built with Parabricks GPU enabled software are compared with standard CPU bioinformatics software. Pipeline results and run performance comparisons are performed to show the impact this technology change can have for a medium sized computational cluster. The present study finds that Parabricks’ GPU workflows show a massive increase in overall efficiency by cutting overall run time by roughly 21x, cutting overall computational hours needed by 650x. Parabricks GPU workflows show a 99.5% variant call concordance rate when compared to clinically validated CPU workflows. Substitution of Parabricks GPU alignment into a clinically validated CPU based pipeline reduces the number of compute hours from 836 hours to 727 hours and returns the same results, showing CPU and GPU’s can be used together to reduce pipeline turnaround time & compute resource burden. Overall, integration of GPUs into bioinformatic pipelines leads to massive reduction of turnaround time, reduction of computation times, and increased throughput, with little to no sacrifice in overall output quality. The findings of this study show GPU based bioinformatic workflows, like Parabricks, could greatly improve whole genome sequencing accessibility for clinical use by reduction of testing turnaround time.