Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

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

Full record


URI: http://purl.tuc.gr/dl/dias/DD76CC54-26C3-4A63-9478-0006E2296555
Year 2020
Type of Item Peer-Reviewed Journal Publication
License
Details
Bibliographic Citation M. 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. https://doi.org/10.1007/s11004-019-09835-3
Appears in Collections

Summary

A 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.

Services

Statistics