Many-core Graphics Processing Units (GPU) provide a high-performance parallel hardware platform on the desktop at an incredibly low cost. However, the widespread use of this computational capacity is hindered by the fact that programming GPUs is difficult. The state-of-the-art is to develop code utilizing the NVIDIA Compute Unified Device Architecture (CUDA). However, effective use of CUDA requires developers highly skilled in both low-level systems programming and parallel processing.