10774

Massively parallel approximate Gaussian process regression

Robert B. Gramacy, Jarad Niemi, Robin Weiss
Booth School of Business, The University of Chicago
arXiv:1310.5182 [stat.CO], (18 Oct 2013)
@article{2013arXiv1310.5182G,

   author={Gramacy}, R.~B. and {Niemi}, J. and {Weiss}, R.},

   title={"{Massively parallel approximate Gaussian process regression}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1310.5182},

   primaryClass={"stat.CO"},

   keywords={Statistics – Computation, Computer Science – Distributed, Parallel, and Cluster Computing},

   year={2013},

   month={oct},

   adsurl={http://adsabs.harvard.edu/abs/2013arXiv1310.5182G},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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We explore how the big-three computing paradigms — symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing — can together be brought to bare on large-data Gaussian processes (GP) regression problems via a careful implementation of a newly developed local approximation scheme. Our methodological contribution focuses primarily on GPU computation, as this requires the most care and also provides the largest performance boost. However, in our empirical work we study the relative merits of all three paradigms to determine how best to combine them. The paper concludes with two case studies. One is a real data fluid-dynamics computer experiment which benefits from the local nature of our approximation; the second is a synthetic data example designed to find the largest design for which (accurate) GP emulation can performed on a commensurate predictive set under an hour.
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