ExaGeoStat
ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems. We present ExaGeoStat , a high performance software for geospatial statistics in climate and environment modeling. In contrast to simulation based on partial differential equations derived from first-principles modeling, ExaGeoStat employs a statistical model based on the evaluation of the Gaussian log-likelihood function, which operates on a large dense covariance matrix. Generated by the parametrizable Matérn covariance function, the resulting matrix is symmetric and positive definite. The computational tasks involved during the evaluation of the Gaussian log-likelihood function become daunting as the number $n$ of geographical locations grows, as ${mathcal O}(n^2)$ storage and ${mathcal O}(n^3)$ operations are required. While many approximation methods have been devised from the side of statistical modeling to ameliorate these polynomial complexities, we are interested here in the complementary approach of evaluating the exact algebraic result by exploiting advances in solution algorithms and many-core computer architectures. Using state-of-the-art high performance dense linear algebra libraries associated with various leading edge parallel architectures (Intel KNLs, NVIDIA GPUs, and distributed-memory systems), ExaGeoStat raises the game for statistical applications from climate and environmental science. ExaGeoStat provides a reference evaluation of statistical parameters, with which to assess the validity of the various approaches based on approximation. The software takes a first step in the merger of large-scale data analytics and extreme computing for geospatial statistical applications, to be followed by additional complexity reducing improvements from the solver side that can be implemented under the same interface. Thus, a single uncompromised statistical model can ultimately be executed in a wide variety of emerging exascale environments.
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References in zbMATH (referenced in 6 articles )
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Sorted by year (- Andrew Finley, Abhirup Datta, Sudipto Banerjee: R package for Nearest Neighbor Gaussian Process models (2020) arXiv
- Galeano, Pedro; Peña, Daniel: Rejoinder on “Data science, big data and statistics” (2019)
- Genton, Marc G.; Sun, Ying: Comments on “Data science, big data and statistics” (2019)
- Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.: Likelihood approximation with hierarchical matrices for large spatial datasets (2019)
- Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv
- Genton, Marc G.; Jeong, Jaehong: Comment to “Mission (\mathrmCO_2)ntrol: a statistical scientist’s role in remote sensing of atmospheric carbon dioxide” (2018)