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.
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