Poster 141: ExaGeoStatR: Harnessing HPC Capabilities for Large Scale Geospatial Modeling Using R

Authors: Sameh Abdulah (King Abdullah University of Science and Technology (KAUST)), Yuxiao Li (King Abdullah University of Science and Technology (KAUST)), Jian Cao (King Abdullah University of Science and Technology (KAUST)), Hatem Ltaief (King Abdullah University of Science and Technology (KAUST)), David Keyes (King Abdullah University of Science and Technology (KAUST)), Marc Genton (King Abdullah University of Science and Technology (KAUST)), Ying Sun (King Abdullah University of Science and Technology (KAUST))

Abstract: Large-scale simulations and parallel computing techniques are becoming essential in Gaussian process calculations to lessen the complexity of geostatistics applications. The log-likelihood function is used in such applications to evaluate the model associated with a given set of measurements in existing n geographic locations. The evaluation of such a function requires O(n^2) memory and O(n^3) computation, which is infeasible for large datasets with existing software tools.

We present ExaGeoStatR, a package for large-scale geostatistics in R that computes the log-likelihood function on shared and distributed-memory, possibly equipped with GPU, using advanced linear algebra techniques. The package provides a high-level abstraction of the underlying architecture while enhancing the R developers' productivity. We demonstrate ExaGeoStatR package by illustrating its implementation details, analyzing its performance on various parallel architectures, and assessing its accuracy using synthetic datasets and a sea surface temperature dataset. The performance evaluation involves spatial datasets with up to 250K observations.

Best Poster Finalist (BP): no

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