Document Type

Graduate Project

Date of Degree Completion

Spring 2020

Degree Name

Master of Science (MS)


Computational Science

Committee Chair

Donald Davendra

Second Committee Member

Alison Scoville

Third Committee Member

Szilard Vajda


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.