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Abstract:
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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 greater complexity of operations and storage, and pushing geostatistical modeling even further into the arms of high performance computing. We propose a high-performance 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 such as 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 sub-communicator perform a single MLE iteration at a time. We evaluate the performance and the accuracy of the proposed implementation using synthetic datasets and a real particulate matter (PM) dataset illustrating the application of the technique to air pollution.
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