Abstract Details
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.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.