ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems

Sameh Abdulah, Hatem Ltaief, Ying Sun, Marc M. Genton, David E. Keyes

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

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 $O(n^{2})$ storage and $O(n^{3})$ 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.
Original languageEnglish (US)
Pages (from-to)2771-2784
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume29
Issue number12
DOIs
StatePublished - Jun 26 2018

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