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dc.contributor.authorSalvaña, Mary Lai O.
dc.contributor.authorAbdulah, Sameh
dc.contributor.authorHuang, Huang
dc.contributor.authorLtaief, Hatem
dc.contributor.authorSun, Ying
dc.contributor.authorGenton, Marc G.
dc.contributor.authorKeyes, David E.
dc.identifier.citationSalvana, M., Abdulah, S., Huang, H., Ltaief, H., Sun, Y., Genton, M. M., & Keyes, D. (2021). High Performance Multivariate Geospatial Statistics on Manycore Systems. IEEE Transactions on Parallel and Distributed Systems, 1–1. doi:10.1109/tpds.2021.3071423
dc.description.abstractModeling and inferring spatial relationships and predicting missing values of environmental data are some of the main tasks of geospatial statisticians. These routine tasks are accomplished using multivariate geospatial models and the cokriging technique, which requires the evaluation of the expensive Gaussian log-likelihood function. This large-scale cokriging challenge provides a fertile ground for supercomputing implementations for the geospatial statistics community as it is paramount to scale computational capability to match the growth in environmental data. In this paper, we develop large-scale multivariate spatial modeling and inference on parallel hardware architectures. To tackle the increasing complexity in matrix operations and the massive concurrency in parallel systems, we leverage low-rank matrix approximation techniques with task-based programming models and schedule the asynchronous computational tasks using a dynamic runtime system. The proposed framework provides both the dense and approximated computations of the Gaussian log-likelihood function. It demonstrates accuracy robustness and performance scalability on a variety of computer systems. Using both synthetic and real datasets, the low-rank matrix approximation shows better performance compared to exact computation, while preserving the application requirements in both parameter estimation and prediction accuracy. We also propose a novel algorithm to assess the prediction accuracy after the online parameter estimation.
dc.description.sponsorshipThe 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, both hosted at the Supercomputing Laboratory at King Abdullah University of Science and Technology (KAUST).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectGaussian log-likelihood
dc.subjectgeospatial statistics
dc.subjecthigh-performance computing
dc.subjectlarge multivariate spatial data
dc.subjectlow-rank approximation
dc.subjectmultivariate modeling/prediction
dc.titleHigh Performance Multivariate Geospatial Statistics on Manycore Systems
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentOffice of the President
dc.identifier.journalIEEE Transactions on Parallel and Distributed Systems
kaust.personSalvana, Mary Lai
kaust.personAbdulah, Sameh
kaust.personHuang, Huang
kaust.personLtaief, Hatem
kaust.personSun, Ying
kaust.personGenton, Marc G.
kaust.personKeyes, David E.
kaust.acknowledged.supportUnitExtreme Computing Research Center (ECRC) at KAUST
kaust.acknowledged.supportUnitSupercomputing Laboratory at King Abdullah University of Science and Technology (KAUST)
kaust.acknowledged.supportUnitShaheen supercomputer

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