An Out-of-core GPU Approach for Accelerating Geostatistical Interpolation

Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70's, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Indus...

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Bibliographic Details
Published in:Procedia computer science 2014, Vol.29, p.888-896
Main Authors: Allombert, Victor, Michea, David, Dupros, Fabrice, Bellier, Christian, Bourgine, Bernard, Aochi, Hideo, Jubertie, Sylvain
Format: Article
Language:English
Subjects:
Computer Science
Distributed, Parallel, and Cluster Computing
Formal Languages and Automata Theory
Geological Data Management (GDM)
Geostatistics
Graphic processing units (GPU)
Linear algebra
Out-of-core
Programming Languages
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Summary:Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70's, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Industries. Considering the huge amount of data available, standard implementations of these numerical methods are not efficient enough to tackle current challenges in geosciences. Moreover, most of the software packages available for geostatisticians are designed for a usage on a desktop computer due to the trial and error procedure used during the interpolation. The Geological Data Management (GDM) software package developed by the French geological survey (BRGM) is widely used to build reliable three-dimensional geological models that require a large amount of memory and computing resources. Considering the most time-consuming phase of kriging methodology, we introduce an efficient out-of-core algorithm that fully benefits from graphics cards acceleration on desktop computer. This way we are able to accelerate kriging on GPU with data 4 times bigger than a classical in-core GPU algorithm, with a limited loss of performances.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2014.05.080