Hierarchical algorithms on hierarchical architectures
A traditional goal of algorithmic optimality, squeezing out flops, has been superseded by
evolution in architecture. Flops no longer serve as a reasonable proxy for all aspects of …
evolution in architecture. Flops no longer serve as a reasonable proxy for all aspects of …
Accelerating geostatistical modeling and prediction with mixed-precision computations: A high-productivity approach with parsec
Geostatistical modeling, one of the prime motivating applications for exascale computing, is
a technique for predicting desired quantities from geographically distributed data, based on …
a technique for predicting desired quantities from geographically distributed data, based on …
High performance multivariate geospatial statistics on manycore systems
Modeling and inferring spatial relationships and predicting missing values of environmental
data are some of the main tasks of geospatial statisticians. These routine tasks are …
data are some of the main tasks of geospatial statisticians. These routine tasks are …
Tile low-rank approximations of non-Gaussian space and space-time Tukey g-and-h random field likelihoods and predictions on large-scale systems
Large-scale statistical modeling has become necessary with the vast flood of geospace data
coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) …
coming from various sources. In space statistics, the Maximum Likelihood Estimation (MLE) …
Portability and scalability evaluation of large-scale statistical modeling and prediction software through HPC-ready containers
HPC-based applications often have complex workflows with many software dependencies
that hinder their portability on contemporary HPC architectures. In addition, these …
that hinder their portability on contemporary HPC architectures. In addition, these …
Parallel approximations of the Tukey g-and-h likelihoods and predictions for non-Gaussian geostatistics
Maximum likelihood estimation is an essential tool in the procedure to impute missing data
in climate/weather applications. By defining a particular statistical model, the maximum …
in climate/weather applications. By defining a particular statistical model, the maximum …
Efficiency assessment of approximated spatial predictions for large datasets
Due to the well-known computational showstopper of the exact Maximum Likelihood
Estimation (MLE) for large geospatial observations, a variety of approximation methods have …
Estimation (MLE) for large geospatial observations, a variety of approximation methods have …
Large‐scale environmental data science with ExaGeoStatR
Parallel computing in exact Gaussian process (GP) calculations becomes necessary for
avoiding computational and memory restrictions associated with large‐scale environmental …
avoiding computational and memory restrictions associated with large‐scale environmental …
Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications
Addressing the statistical challenge of computing the multivariate normal (MVN) probability
in high dimensions holds significant potential for enhancing various applications. One …
in high dimensions holds significant potential for enhancing various applications. One …
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Spatial processes observed in various fields, such as climate and environmental science,
often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a …
often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a …