Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Christopher J. Paciorek, Benjamin Lipshitz, Wei Zhuo, . Prabhat, Cari G. G. Kaufman, Rollin C. Thomas
Title: Parallelizing Gaussian Process Calculations in R
Abstract: We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of datasets that can be analyzed. Using a hybrid parallelization approach that uses both threading (shared memory) and message-passing (distributed memory), we implement the core linear algebra operations used in spatial statistics and Gaussian process regression in an R package called bigGP that relies on C and MPI. The approach divides the covariance matrix into blocks such that the computational load is balanced across processes while communication between processes is limited. The package provides an API enabling R programmers to implement Gaussian process-based methods by using the distributed linear algebra operations without any C or MPI coding. We illustrate the approach and software by analyzing an astrophysics dataset with n = 67, 275 observations.

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Paper: Parallelizing Gaussian Process Calculations in R     Download PDF (Downloads: 2405)
bigGP_0.1-5.tar.gz: R source package Download (Downloads: 233; 1024KB)
v63i10.R: R example code from the paper Download (Downloads: 275; 3KB)

DOI: 10.18637/jss.v063.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.