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Parallel computing in Gaussian process calculations becomes necessary foravoiding computational and memory restrictions associated with large-scaleenvironmental data science applications. The evaluation of the Gaussianlog-likelihood function requires O(n^2) storage and O(n^3) operations where nis the number of geographical locations. Thus, computing the log-likelihoodfunction with a large number of locations requires exploiting the power ofexisting parallel computing hardware systems, such as shared-memory, possiblyequipped with GPUs, and distributed-memory systems, to solve this computationalcomplexity. In this paper, we advocate the use of ExaGeoStatR, a package forexascale Geostatistics in R that supports a parallel computation of the exactmaximum likelihood function on a wide variety of parallel architectures.Parallelization in ExaGeoStatR depends on breaking down the numerical linearalgebra operations in the log-likelihood function into a set of tasks andrendering them for a task-based programming model. The package can be useddirectly through the R environment on parallel systems. Currently, ExaGeoStatRsupports several maximum likelihood computation variants such as exact,Diagonal Super Tile (DST), Tile Low-Rank (TLR) approximations, andMixed-Precision (MP). ExaGeoStatR also provides a tool to simulate large-scalesynthetic datasets. These datasets can help to assess different implementationsof the maximum log-likelihood approximation methods. Here, we demonstrateExaGeoStatR by illustrating its implementation details, analyzing itsperformance on various parallel architectures, and assessing its accuracy usingsynthetic datasets with up to 250K observations. We provide a hands-on tutorialto analyze a sea surface temperature real dataset. The performance evaluationinvolves comparisons with the popular packages geoR and fields for exactlikelihood evaluation.

Large-scale Environmental Data Science with ExaGeoStatR