×

ExaGeoStatR

swMATH ID: 30063
Software Authors: Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun
Description: ExaGeoStatR: A Package for Large-Scale Geostatistics in R. Parallel computing in Gaussian process calculation becomes a necessity for avoiding computational and memory restrictions associated with Geostatistics applications. The evaluation of the Gaussian log-likelihood function requires O(n^2) storage and O(n^3) operations where n is the number of geographical locations. In this paper, we present ExaGeoStatR, a package for large-scale Geostatistics in R that supports parallel computation of the maximum likelihood function on shared memory, GPU, and distributed systems. The parallelization depends on breaking down the numerical linear algebra operations into a set of tasks and rendering them for a task-based programming model. ExaGeoStatR supports several maximum likelihood computation variants such as exact, Diagonal Super Tile (DST), and Tile Low-Rank (TLR) approximation besides providing a tool to generate large-scale synthetic datasets which can be used to test and compare different approximations methods. The package can be used directly through the R environment without any C, CUDA, or MPIknowledge. Here, we demonstrate the ExaGeoStatR package by illustrating its implementation details, analyzing its performance on various parallel architectures, and assessing its accuracy using both synthetic datasets and a sea surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations.
Homepage: https://arxiv.org/abs/1908.06936
Source Code: https://github.com/ecrc/exageostatR
Dependencies: R
Related Software: RandomFields; spBayes; geoR; fields; R; ggplot2; optimx; NLopt; STARS-H; Chameleon; StarPU; HiCMA; bigGP; R-INLA; pbdR; ExaGeoStat; geofd; Manifoldgstat; fdagstat; fdaPDE
Cited in: 1 Publication

Standard Articles

1 Publication describing the Software Year
ExaGeoStatR: A Package for Large-Scale Geostatistics in R
Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun
2019

Citations by Year