12310

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

Victor Allombert, David Michea, Fabrice Dupros, Christian Bellier, Bernard Bourgine, Hideo Aochi, Sylvain Jubertie
LACL, Universit de Paris-Est, France
Procedia Computer Science, Volume 29, Pages 888-896, 2014

@article{Allombert2014888,

   title={An Out-of-core {GPU} Approach for Accelerating Geostatistical Interpolation},

   journal={Procedia Computer Science},

   volume={29},

   number={0},

   pages={888 – 896},

   year={2014},

   note={2014 International Conference on Computational Science},

   issn={1877-0509},

   doi={http://dx.doi.org/10.1016/j.procs.2014.05.080},

   url={http://www.sciencedirect.com/science/article/pii/S1877050914002579},

   author={Victor Allombert and David Michea and Fabrice Dupros and Christian Bellier and Bernard Bourgine and Hideo Aochi and Sylvain Jubertie},

   keywords={Geostatistics, Graphic processing units (GPU), Linear algebra, Out-of-core, Geological Data Management (GDM)}

}

Download Download (PDF)   View View   Source Source   

763

views

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.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: