Document Type
Graduate Project
Date of Degree Completion
Spring 2020
Degree Name
Master of Science (MS)
Department
Computational Science
Committee Chair
Donald Davendra
Second Committee Member
Alison Scoville
Third Committee Member
Szilard Vajda
Abstract
Utilizing the power of GPU parallel processing with CUDA can speed up the processing of Variant Call Format (VCF) files and statistical analysis of genomic data. A software package designed toward this purpose would be beneficial to genetic researchers by saving them time which they could spend on other aspects of their research. A data set containing genetics from a study of trichome production in Mimulus guttatus, or yellow monkey flower, was used to develop a package to test the effectiveness of GPU parallel processing versus serial executions. After a serial version of the code was generated and benchmarked, OpenACC with Portland Group’s PGI compiler using CUDA was applied to the parallelizable parts and the program execution time was recorded to be compared to the serial execution benchmarks. To make this program more accessible to researchers in the biological field, the accelerated functions of the program are written in the C language and compiled as a driver file to be used from R.
Recommended Citation
McKinnon, Heather, "Using CUDA to Enhance Data Processing of Variant Call Format Files for Statistical Genetic Analysis" (2020). All Graduate Projects. 183.
https://digitalcommons.cwu.edu/graduate_projects/183