In the past two years the processing power of video graphics cards has quadrupled and is approaching super computer levels. State-of-the-art graphical processing units (GPU) boast of theoretical computational performance in the range of 1.5 trillion floating point operations per second (1.5 Teraflops). This processing power is readily accessible to the scientific community at a relatively small cost. High level programming languages are now available that give access to the internal architecture of the graphics card allowing greater algorithm optimization. This research takes memory access expensive portions of an image-based iris identification algorithm and hosts it on a GPU using the C++ compatible CUDA language. The selected segmentation algorithm uses basic image processing techniques such as image inversion, value squaring, thresholding, dilation, erosion and memory/computationally intensive calculations such as the circular Hough transform. Portions of the iris segmentation algorithm were accelerated by a factor of 77 over the 2008 GPU results. Some parts of the algorithm ran at speeds that were over 1600 times faster than their CPU counterparts. Strengths and limitations of the GPU Single Instruction Multiple Data architecture are discussed. Memory access times, instruction execution times, programming details and code samples are presented as part of the research.
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