Massively parallel strategies for local spatial interpolation
Computers & Geosciences
FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems
Computers & Geosciences
Kriging Interpolation on High-Performance Computers
HPCN Europe 1998 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two approaches to direct block-support conditional co-simulation
Computers & Geosciences
Accelerating geostatistical simulations using graphics processing units (GPU)
Computers & Geosciences
GPU-based SNESIM implementation for multiple-point statistical simulation
Computers & Geosciences
Multiple-point geostatistical simulation using the bunch-pasting direct sampling method
Computers & Geosciences
Parallel scanline algorithm for rapid rasterization of vector geographic data
Computers & Geosciences
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The size of simulation grids used for numerical models has increased by many orders of magnitude in the past years, and this trend is likely to continue. Efficient pixel-based geostatistical simulation algorithms have been developed, but for very large grids and complex spatial models, the computational burden remains heavy. As cluster computers become widely available, using parallel strategies is a natural step for increasing the usable grid size and the complexity of the models. These strategies must profit from of the possibilities offered by machines with a large number of processors. On such machines, the bottleneck is often the communication time between processors. We present a strategy distributing grid nodes among all available processors while minimizing communication and latency times. It consists in centralizing the simulation on a master processor that calls other slave processors as if they were functions simulating one node every time. The key is to decouple the sending and the receiving operations to avoid synchronization. Centralization allows having a conflict management system ensuring that nodes being simulated simultaneously do not interfere in terms of neighborhood. The strategy is computationally efficient and is versatile enough to be applicable to all random path based simulation methods.