Parallelization of sequential Gaussian, indicator and direct simulation algorithms

Nunes, Rúben; Almeida, José António de

doi:10.1016/j.cageo.2010.03.005

Cited by 54 publications

(19 citation statements)

References 4 publications

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“…To create average yearly images, a geostatistical approach was used, which included (Soares, 2000;Paralta and Ribeiro, 2003;Guimarães et al, 2014;Quental et al, 2012;Roxo et al, 2016) For geostatistical modelling purposes the geoMS software and a parallelised version of the sequential Gaussian simulation were used (Alexandre and Almeida, 1998;Nunes and Almeida, 2010). Due to the broad dispersion of data with no preferential orientation, it was decided to compute averages for the variograms encompassing all directions together (omnidirectional variograms) (Isaaks and Srivastava, 1989;Goovaerts, 1997).…”

Section: Methodsmentioning

confidence: 99%

Agricultural practices and geostatistical evaluation of nitrate pollution of groundwater in the Júcar River Basin District, Spain

Ferreira

Almeida

Legoinha

et al. 2016

Emir. J. Food Agric

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Section: Methodsmentioning

confidence: 99%

Agricultural practices and geostatistical evaluation of nitrate pollution of groundwater in the Júcar River Basin District, Spain

Ferreira

Almeida

Legoinha

et al. 2016

Emir. J. Food Agric

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“…In the same context, Mariethoz (2010) and posteriorly Tahmasebi et al (2012) developed a master-slave strategy that distributes the grid nodes among several processors, using a multi-core cluster or a graphical unit processor (GPU). Regarding the node-level parallelization, where the computation of each grid node is parallelized, two-point (Nunes & Almeida, 2010) and multi-point statistics (Straubhaar et al, 2011;Peredo & Ortiz, 2011Peredo et al, 2014) strategies have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A path-level exact parallelization strategy for sequential simulation

Peredo¹,

Baeza

Ortíz

et al. 2018

Computers & Geosciences

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Sequential Simulation is a well known method in geostatistical modelling. Following the Bayesian approach for simulation of conditionally dependent random events, Sequential Indicator Simulation (SIS) method draws simulated values for K categories (categorical case) or classes defined by K different thresholds (continuous case). Similarly, Sequential Gaussian Simulation (SGS) method draws simulated values from a multivariate Gaussian field. In this work, a path-level approach to parallelize SIS and SGS methods is presented. A first stage of rearrangement of the simulation path is performed, followed by a second stage of parallel simulation for non-conflicting nodes. A key advantage of the proposed parallelization method is to generate identical realizations as with the original non-parallelized methods. Case studies are presented using two sequential simulation codes from GSLIB: SISIM and SGSIM. Execution time and speedup results are shown for large-scale domains, with many categories and maximum kriging neighbours in each case, achieving high speedup results in the best scenarios using 16 threads of execution in a single machine.

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“…This strategy has been implemented on a patch-based SIMPAT method [28]. Parallel implements for other geostatistical algorithms, such as the parallel two-point geostatistical simulation [29], parallel pixel-based algorithms [30], and parallel optimal algorithms [31] have been proposed constantly.…”

Section: Introductionmentioning

confidence: 99%

Training Images-Based Stochastic Simulation on Many-Core Architectures

Huang¹,

Lu²

2016

Modeling and Simulation in Engineering Sciences

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In the past decades, multiple-point geostatistical methods (MPS) are increasing in popularity in various fields. Compared with the traditional techniques, MPS techniques have the ability to characterize geological reality that commonly has complex structures such as curvilinear and long-range channels by using high-order statistics for pattern reconstruction. As a result, the computational burden is heavy, and sometimes, the current algorithms are unable to be applied to large-scale simulations. With the continuous development of hardware architectures, the parallelism implementation of MPS methods is an alternative to improve the performance. In this chapter, we overview the basic elements for MPS methods and provide several parallel strategies on many-core architectures. The GPU-based parallel implementation of two efficient MPS methods known as SNESIM and Direct Sampling is detailed as examples.

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Parallelization of sequential Gaussian, indicator and direct simulation algorithms

Cited by 54 publications

References 4 publications

Agricultural practices and geostatistical evaluation of nitrate pollution of groundwater in the Júcar River Basin District, Spain

Agricultural practices and geostatistical evaluation of nitrate pollution of groundwater in the Júcar River Basin District, Spain

A path-level exact parallelization strategy for sequential simulation

Training Images-Based Stochastic Simulation on Many-Core Architectures

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