Today, parallel selection algorithms that run on Graphical Processing Units (GPUs) hold great promise in providing even more computational power than that of conventional CPUs. To quantify these gains, we examined a new parallel selection algorithm to see exactly what its vast number of simple, data parallel, multithreaded cores meant for performance times, using the current generation of NVIDIA GPUs. Specifically, our team tested how we could utilize a GPU to select elements from a massive array that met specific criteria and store their indices in a target array for additional processing. In this paper, we report optimization techniques and road blocks encountered. Overall, the experimental results demonstrate that our implementation performs an average of 3.67 times faster than Thrust, an open-source parallel algorithms library.