Large-Scale Spatial Data Science

Music City Center 

Room: CC-107A 

The course, designed for data scientists, geospatial analysts, and researchers, will provide a comprehensive understanding of advanced methods in large-scale geospatial data science. The focus will be on three key topics: large-scale data modeling and prediction, accelerating geospatial data processing with multi- and mixed-precision techniques on modern hardware architectures, and parallelizing related R codes using the first parallel runtime system package in R. Participants will first explore ExaGeoStatCPP, a parallel framework for high-performance geostatistical computations. It enables efficient modeling and prediction of large-scale geospatial datasets within C++ and R environments. The course will also focus on the MPCR package, which provides multi- and mixed-precision support on CPUs and GPUs. Attendees will learn to integrate MPCR functions into their R workflows to optimize performance and precision trade-offs in computational tasks. Participants will also be introduced to RCOMPSs, a new runtime system designed to parallelize R code across HPC systems. The course will demonstrate how RCOMPSs can be used to accelerate R code execution in high-performance computing environments, providing hands-on experience in parallelizing computations effectively. Hands-on sessions will provide practical examples of parallelizing computations. By the end of the course, participants will have gained advanced skills in large-scale geospatial data science and be ready to apply them in their professional roles.