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Activity Number: 6
Type: Invited
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #314614 View Presentation
Title: Parallelizing Gaussian Process Calculations in R
Author(s): Christopher J. Paciorek* and Benjamin Lipshitz and Wei Zhu and Mr. Prabhat and Cari Kaufman and Rollin Thomas
Companies: UC Berkeley and UC Berkeley and IBM and Lawrence Berkeley National Laboratory and UC Berkeley and Lawrence Berkeley National Laboratory
Keywords: distributed computing ; kriging ; linear algebra
Abstract:

We consider parallel computation for Gaussian process calculations to overcome computational and memory constraints on the size of data sets 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 data set with 67,275 observations.


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