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GPU-accelerated simulation of massive spatial data based on the modified planar rotator model

Žukovič, Milan, Borovský Michal, Lach Matúš, Christopoulos Dionysios

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URIhttp://purl.tuc.gr/dl/dias/DD76CC54-26C3-4A63-9478-0006E2296555-
Identifierhttps://doi.org/10.1007/s11004-019-09835-3-
Identifierhttps://link.springer.com/article/10.1007/s11004-019-09835-3-
Languageen-
Extent21 pagesen
TitleGPU-accelerated simulation of massive spatial data based on the modified planar rotator modelen
CreatorŽukovič, Milanen
CreatorBorovský Michalen
CreatorLach Matúšen
CreatorChristopoulos Dionysiosen
CreatorΧριστοπουλος Διονυσιοςel
PublisherSpringeren
Content SummaryA novel Gibbs Markov random field for spatial data on Cartesian grids based on the modified planar rotator (MPR) model of statistical physics has been recently introduced for efficient and automatic interpolation of big data sets, such as satellite and radar images. The MPR model does not rely on Gaussian assumptions. Spatial correlations are captured via nearest-neighbor interactions between transformed variables. This allows vectorization of the model which, along with an efficient hybrid Monte Carlo algorithm, leads to fast execution times that scale approximately linearly with system size. The present study takes advantage of the short-range nature of the interactions between the MPR variables to parallelize the algorithm on graphics processing units (GPUs) in the Compute Unified Device Architecture programming environment. It is shown that, for the processors employed, the GPU implementation can lead to impressive computational speedups, up to almost 500 times on large grids, compared to single-processor calculations. Consequently, massive data sets comprising millions of data points can be automatically processed in less than one second on an ordinary GPU.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-06-29-
Date of Publication2020-
SubjectSpatial interpolationen
SubjectHybrid Monte Carloen
SubjectNon-Gaussian modelen
SubjectConditional simulationen
SubjectGPU parallel computingen
SubjectCUDAen
Bibliographic CitationM. Žukovič, M. Borovský, M. Lach, and D. T. Hristopoulos, “GPU-accelerated simulation of massive spatial data based on the modified planar rotator model,” Math. Geosci., vol. 52, no. 1, pp. 123–143, Jan. 2020, doi: 10.1007/s11004-019-09835-3.en

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