High Performance Multivariate Spatial Modeling for Geostatistical Data on Manycore Systems

The authors would like to thank NVIDIA Inc., Cray Inc., and Intel Corp., the Cray Center of Excellence and Intel Parallel Computing Center awarded to the Extreme Computing Research Center (ECRC) at KAUST. For computer time, this research used GPU-based systems as well as Shaheen supercomputer hosted at the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST).

[1]  Michele Bottazzi,et al.  The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8 , 2018, Geoscientific Model Development.

[2]  Hatem Ltaief,et al.  Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications , 2020, PASC.

[3]  Huang Huang,et al.  Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets , 2016, 1605.08898.

[4]  Michael L. Stein,et al.  Computationally efficient spatial modeling using recursive skeletonization factorizations , 2018, Spatial Statistics.

[5]  A. Gelfand,et al.  Gaussian predictive process models for large spatial data sets , 2008, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[6]  Julien Langou,et al.  A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures , 2007, Parallel Comput..

[7]  Théo Mary,et al.  Block Low-Rank multifrontal solvers: complexity, performance, and scalability. (Solveurs multifrontaux exploitant des blocs de rang faible: complexité, performance et parallélisme) , 2017 .

[8]  D. Nychka,et al.  Covariance Tapering for Interpolation of Large Spatial Datasets , 2006 .

[9]  April Morton,et al.  High performance Data Driven Agent-based Modeling Framework for Simulation of Commute Mode Choices in Metropolitan Area , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[10]  K. Mardia,et al.  Maximum likelihood estimation of models for residual covariance in spatial regression , 1984 .

[11]  David E. Keyes,et al.  Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations , 2018, 2018 IEEE International Conference on Cluster Computing (CLUSTER).

[12]  Matthias Katzfuss,et al.  A Multi-Resolution Approximation for Massive Spatial Datasets , 2015, 1507.04789.

[13]  Ian T. Foster,et al.  Introduction to the Special Issue on Parallel Computing in Climate and Weather Modeling , 1995, Parallel Comput..

[14]  Nicolas Doucet,et al.  Mixed-Precision Tomographic Reconstructor Computations on Hardware Accelerators , 2019, 2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3).

[15]  Cédric Augonnet,et al.  StarPU: a unified platform for task scheduling on heterogeneous multicore architectures , 2011, Concurr. Comput. Pract. Exp..

[16]  Ying Sun,et al.  Efficiency Assessment of Approximated Spatial Predictions for Large Datasets , 2019 .

[17]  Jeffrey W. White,et al.  Interpolation techniques for climate variables , 1999 .

[18]  Bo Li,et al.  An approach to modeling asymmetric multivariate spatial covariance structures , 2011, J. Multivar. Anal..

[19]  Douglas W. Nychka,et al.  Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets , 2008 .

[20]  Hao Zhang,et al.  When Doesn't Cokriging Outperform Kriging? , 2015, 1507.08403.

[21]  W. Hackbusch,et al.  Hierarchical Matrices: Algorithms and Analysis , 2015 .

[22]  Thomas Hérault,et al.  PaRSEC: Exploiting Heterogeneity to Enhance Scalability , 2013, Computing in Science & Engineering.

[23]  Eric Darve,et al.  A fast block low-rank dense solver with applications to finite-element matrices , 2014, J. Comput. Phys..

[24]  Chak Man Andrew Yip Statistical characteristics and mapping of near-surface and elevated wind resources in the Middle East , 2018 .

[25]  David E. Keyes,et al.  Exploiting Data Sparsity for Large-Scale Matrix Computations , 2018, Euro-Par.

[26]  Marc G. Genton,et al.  Gaussian likelihood inference on data from trans‐Gaussian random fields with Matérn covariance function , 2018 .

[27]  Susan Ostrouchov,et al.  LAPACK Working Note 41: Installation Guide for LAPACK , 1992 .

[28]  Jing Ren,et al.  Moving from exascale to zettascale computing: challenges and techniques , 2018, Frontiers of Information Technology & Electronic Engineering.

[29]  Marc G. Genton,et al.  Cross-Covariance Functions for Multivariate Geostatistics , 2015, 1507.08017.

[30]  David E. Keyes,et al.  ExaGeoStat: A High Performance Unified Software for Geostatistics on Manycore Systems , 2017, IEEE Transactions on Parallel and Distributed Systems.

[31]  N. Cressie,et al.  Multivariate Spatial Covariance Models: A Conditional Approach , 2015, 1504.01865.

[32]  T. Gneiting,et al.  Matérn Cross-Covariance Functions for Multivariate Random Fields , 2010 .

[33]  David E. Keyes,et al.  Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures , 2017, ISC.

[34]  Ying Sun,et al.  A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components , 2012 .

[35]  David E. Keyes,et al.  ExaGeoStatR: A Package for Large-Scale Geostatistics in R , 2019, ArXiv.

[36]  Hsueh-Ting Chu High-Performance Computing for Measurement of Cancer Gene Signatures , 2019 .

[37]  Dionissios T. Hristopulos,et al.  Random Fields for Spatial Data Modeling: A Primer for Scientists and Engineers , 2020 .

[38]  Chao Zeng,et al.  Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[39]  G. Turkiyyah,et al.  Hierarchical algorithms on hierarchical architectures , 2020, Philosophical Transactions of the Royal Society A.

[40]  T. Severini Likelihood Methods in Statistics , 2001 .