OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
Patterns for parallel programming
Patterns for parallel programming
A SGeMS code for pattern simulation of continuous and categorical variables: FILTERSIM
Computers & Geosciences
Considering complex training images with search tree partitioning
Computers & Geosciences
Parallel Programming: for Multicore and Cluster Systems
Parallel Programming: for Multicore and Cluster Systems
A general parallelization strategy for random path based geostatistical simulation methods
Computers & Geosciences
Parallelization of sequential Gaussian, indicator and direct simulation algorithms
Computers & Geosciences
Parallel implementation of simulated annealing to reproduce multiple-point statistics
Computers & Geosciences
Accelerating geostatistical simulations using graphics processing units (GPU)
Computers & Geosciences
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Among techniques applied to categorical variables simulation, multiple-point statistical simulation is widely used because of its non-iterative characteristic and powerful capability of curvilinear features reproduction. In current implementations, the multiple-point statistics (MPS) are inferred from the training image by storing all the observed patterns scanned by data templates of a certain size within a data structure, either a tree used by Single Normal Equation Simulation Algorithm (SNESIM) or a list used by IMPALA. This type of algorithms has the advantage of being fast to be applied, but it presents some critical limitations. In particular, the data structure is extremely memory demanding. For large-scale problems with numerous patterns, large data templates cannot be used. Therefore, complex structures are then difficult to be simulated. A GPU computing scheme for SNESIM is proposed for multiple point statistical simulation in this paper. Taking advantage of powerful computing capability of GPU's many-core architecture, parallel operations are applied to each simulation grid node, which is the most time-consuming portion among entire simulation process. This scheme requires fixed size memory, so it is independent of inference of data template size, which is especially important for large-scale problems. The simulation results based on a NVIDIAGTX680 device can obtain about 15x speedup than on an Intel Core i3 540 CPU, which demonstrates the efficiency of the scheme against search-tree based implementation of SNESIM contained in SGeMS software.