ScaLAPACK user's guide
Developments and trends in the parallel solution of linear systems
Parallel Computing - Special Anniversary issue
Grids, the TeraGrid, and Beyond
Computer
Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models
Statistics and Computing
Slice sampling for simulation based fitting of spatial data models
Statistics and Computing
A theoretical approach to the use of cyberinfrastructure in geographical analysis
International Journal of Geographical Information Science
TeraGrid GIScience Gateway: Bridging cyberinfrastructure and GIScience
International Journal of Geographical Information Science - Distributed Geographic Information Processing Research
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
Parallel multivariate slice sampling
Statistics and Computing
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
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When MCMC methods for Bayesian spatiotemporal modeling are applied to large geostatistical problems, challenges arise as a consequence of memory requirements, computing costs, and convergence monitoring. This article describes the parallelization of a reparametrized and marginalized posterior sampling (RAMPS) algorithm, which is carefully designed to generate posterior samples efficiently. The algorithm is implemented using the Parallel Linear Algebra Package (PLAPACK). The scalability of the algorithm is investigated via simulation experiments that are implemented using a cluster with 25 processors. The usefulness of the method is illustrated with an application to sulfur dioxide concentration data from the Air Quality System database of the U.S. Environmental Protection Agency.