Graphics Processing Units and High-Dimensional Optimization

Hua Zhou, Kenneth Lange, Marc A. Suchard
Department of Human Genetics, University of California, Los Angeles
arXiv:1003.3272v1 [stat.CO] (16 Mar 2010)


   title={Graphics Processing Units and High-Dimensional Optimization},

   author={Zhou, H. and Lange, K. and Suchard, M.A.},

   journal={Arxiv preprint arXiv:1003.3272},



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This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of data. These criteria favor EM and MM algorithms that separate parameters and data. To a lesser extent block relaxation and coordinate descent and ascent also qualify. We demonstrate the utility of GPUs in nonnegative matrix factorization, PET image reconstruction, and multidimensional scaling. Speedups of 100 fold can easily be attained. Over the next decade, GPUs will fundamentally alter the landscape of computational statistics. It is time for more statisticians to get on-board.
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