Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

9-30-2014

Journal or Book Title

SIAM/ASA Journal on Uncertainty Quantification

Volume

2

Issue

1

First Page

564

Last Page

584

DOI

10.1137/130941912

Abstract

We explore how the big-three computing paradigms---symmetric multiprocessor, graphical processing units (GPUs), and cluster computing---can together be brought to bear 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 example designed to find the largest data set for which (accurate) GP emulation can be performed on a commensurate predictive set in under an hour.

Comments

This is an article from SIAM/ASA Journal on Uncertainty Quantification 2 (2014): 564, doi: 10.1137/130941912. Posted with permission.

Copyright Owner

2014

Language

en

File Format

application/pdf

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