Abstract. NVidia’s CUDA framework has brought supercomputing to the masses allowing programmers to take advantage of the highly parallel capabilities of their Graphics Processing Units. We analyzed a popular Genomic Selection software’s codebase and identified key areas where it could benefit from parallelization. Using the CUDA C++ language extensions, we identified areas in GenSel that could be parallelized and also discovered some issues associated with our attempts to parallelize the critical sections. We also utilized a CUDA Linear Algebra Library that targets speeding up linear algebra computations by moving the work to the GPUs and examined how that impacted the run time.