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

DOI10.1007/s11004-019-09835-3zbMath1429.65330arXiv1811.01604OpenAlexW2982739983WikidataQ126853678 ScholiaQ126853678MaRDI QIDQ2284097

Michal Borovský, Dionissios T. Hristopulos, Matúš Lach, M. Žukovič

Publication date: 14 January 2020

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.01604

zbMATH Keywords

hybrid Monte Carloconditional simulationspatial interpolationCUDAGPU parallel computingnon-Gaussian model

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to physics (62P35) Parallel numerical computation (65Y05)

Related Items (1)

Spatial data modeling by means of Gibbs-Markov random fields based on a generalized planar rotator model

Uses Software

• CUDA
• GMRFLib

Cites Work

• Unnamed Item
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• Spartan Gibbs Random Field Models for Geostatistical Applications
• Gaussian Markov Random Fields
• Equation of State Calculations by Fast Computing Machines
• Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

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