What is ExaGeoStat?

The Exascale GeoStatistics project (ExaGeoStat) is a parallel high performance unified framework for computational geostatistics on many-core systems. The project aims at optimizing the likelihood function for a given spatial data to provide an efficient way to predict missing observations in the context of climate/weather forecasting applications. This machine learning framework proposes a unified simulation code structure to target various hardware architectures, from commodity x86 to GPU accelerator-based shared and distributed-memory systems. ExaGeoStat enables statisticians to tackle computationally challenging scientific problems at large-scale, while abstracting the hardware complexity, through state-of-the-art high-performance linear algebra software libraries.


  • Sameh Abdulah, Research Scientist, Hierarchical Computations on Manycore Architectures
  • Marc G. Genton, Al-Khawarzmi Distinguished Professor, Statistics
  • David Keyes, Professor, Applied Mathematics and Computational Sciences
  • Hatem Ltaief, Principal Research Scientist, KAUST HiCMA Research Group