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

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


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
Issue number12
StatePublished - Jun 26 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). We would like to thank NVIDIA for hardware donations in the context of a GPU Research Center, Intel for support in the form of an Intel Parallel Computing Center award, Cray for support provided during the Center of Excellence award to the Extreme Computing Research Center at KAUST, and KAUST IT Research Computing for their hardware support on the GPU-based system. This research made use of the resources of the KAUST Supercomputing Laboratory. Finally, the authors would like to thank Alexander Litvinenko from the Extreme Computing Research Center for his valuable help.


Dive into the research topics of 'ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems'. Together they form a unique fingerprint.

Cite this