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Activity Number: 581
Type: Invited
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #310934 View Presentation
Title: Massively Parallel Approximate Gaussian Process Regression
Author(s): Jarad Niemi*+ and Robert B. Gramacy and Robin Weiss
Companies: Iowa State University and University of Chicago and University of Chicago
Keywords: Gaussian process regression ; GPUs ; computer experiments ; parallel computing ; statistical computing

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 carefully 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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