Massively parallel approximate Gaussian process regression

Robert B. Gramacy; Jarad Niemi; Robin M. Weiss

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arXiv · 1310.5182

Massively parallel approximate Gaussian process regression

Robert B. Gramacy·Jarad Niemi·Robin M. Weiss

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Abstract

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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Robert B. Gramacy, Jarad Niemi, Robin M. Weiss. 2014-06-04. Massively parallel approximate Gaussian process regression. https://arxiv.org/abs/1310.5182

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