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\textbf{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 found (X) speedup. also discovered some issues associated with our attempts to parallelize the critical sections.  We also utilized libraries a CUDA Linear Algebra Library  that target targets  speeding up linear algebra computations by moving the work to the GPUs and examined how that impacted the run time.