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    Parallel Space-Time Likelihood Optimization for Air Pollution Prediction on Large-Scale Systems

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    Type
    Conference Paper
    Authors
    Salvaña, Mary Lai O.
    Abdulah, Sameh
    Ltaief, Hatem cc
    Sun, Ying cc
    Genton, Marc G. cc
    Keyes, David E. cc
    KAUST Department
    Extreme Computing Research Center (ECRC), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Applied Mathematics and Computational Science Program
    Office of the President
    Date
    2022
    Permanent link to this record
    http://hdl.handle.net/10754/676271
    
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    Abstract
    Gaussian geostatistical space-time modeling is an effective tool for performing statistical inference of field data evolving in space and time, generalizing spatial modeling alone at the cost of the greater complexity of operations and storage, and pushing geostatistical modeling even further into the arms of high-performance computing. It makes inferences for missing data by leveraging space-time measurements of one or more fields. We propose a highperformance implementation of a widely applied space-time model for large-scale systems using a two-level parallelization technique. At the inner level, we rely on state-of-the-art dense linear algebra libraries and parallel runtime systems to perform complex matrix operations required to evaluate the maximum likelihood estimation (MLE). At the outer level, we parallelize the optimization process using a distributed implementation of the particle swarm optimization (PSO) algorithm. At this level, parallelization is accomplished using MPI sub-communicators, such that the nodes in each subcommunicator perform a single MLE iteration at a time. To evaluate the effectiveness of the proposed methodology, we assess the accuracy of the newly implemented space-time model on a set of large-scale synthetic space-time datasets. Moreover, we use the proposed implementation to model two air pollution datasets from the Middle East and US regions with 550 spatial locations ×730 time slots and 945 spatial locations ×500 time slots, respectively. The evaluation shows that the proposed approach satisfies high prediction accuracy on both synthetic datasets and real particulate matter (PM) datasets in the context of the air pollution problem. We achieve up to 757.16 TFLOPS/s using 1024 nodes (75% of the peak performance) using 490𝐾 geospatial locations on a Cray XC40 system.
    Publisher
    ACM
    Conference/Event name
    The Platform for Advanced Scientific Computing (PASC) Conference
    Additional Links
    https://pasc22.pasc-conference.org/
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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