HPC

Scalable Methods for Multivariate Normal Probability Estimation with Applications in Confidence Region Detection, Transport Phenomena, and Parallel Computing Using RCOMPSs

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B2 R5209

This thesis addresses computing high-dimensional multivariate normal (MVN) probabilities in environmental and geospatial applications by combining high-performance computing, numerical approximation, and transport-based covariance modeling to make several important spatial procedures usable at larger scales.

KAUST researchers define the emerging field of high-performance statistical computing (HPSC)

4 min read ·
KAUST researchers outline a new discipline for statistical computing at supercomputing scale.

Amal Alghamdi

Saverio Pasqualoni

M.S. Student, Computer Science
Saverio is a Master’s student pursuing graduate studies (M.S. and Ph.D.) in Computer Science at KAUST, working in the SANDS research group on high-performance computing and collective communications, with research interests that bridge systems-level computer science and large-scale simulation through a strong mathematical foundation.
ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read ·
KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

Xiran Zhang

Ph.D. Student, Statistics
Xiran Zhang research investigates statistics and high-performance computing, with a particular focus on scalable methods for large-scale geostatistical and spatio-temporal problems.