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Implementation of Kernel Methods on the GPU

Julius Ohmer, Frederic Maire, Ross Brown
Queensland University of Technology, School of Software Engineering and Data Communication, 126 Margaret Street, Brisbane QLD 4000, Australia
DICTA 05 Proceedings Digital Image Computing Technqiues and Applications 2005 (2005) Issue: Dicta, Publisher: Ieee, Pages: 543-550

@article{ohmer2005implementation,

   title={Implementation of Kernel Methods on the GPU},

   author={Ohmer, J. and Maire, F. and Brown, R.},

   year={2005},

   publisher={IEEE Computer Society}

}

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Kernel methods such as kernel principal component analysis and support vector machines have become powerful tools for pattern recognition and computer vision. Unfortunately the high computational cost of kernel methods is a limiting factor for real-time classification tasks when running on the CPU of a standard PC. Over the last few years, commodity Graphics Processing Units (GPU) have evolved from fixed graphics pipeline processors into more flexible and powerful data-parallel processors. These stream processors are capable of sustaining computation rates of greater than ten times that of a single CPU. GPUs are inexpensive and are becoming ubiquitous (desktops, laptops, PDAs, cell phones). In this paper, we present a face recognition system based on kernel methods running on the GPU. This GPU implementation is twenty eight times faster than the same optimized application running on the CPU.
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