A parallel computing strategy for Monte Carlo simulation using groundwater models

Leyva-Suárez, Esther; Herrera, Graciela S.; Cruz, Luis M. de la

doi:10.1016/j.gi.2015.04.020

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Running the model for multiple simulated realities resulting from step 2 may be computationally demanding, particularly when the model is complex and requires much computing time. The computation time can be reduced, as the MC method is very suited for parallel computing with multi-core machines and for grid computing technology (Leyva-Suarez et al, 2015). In this way, computation times can be dramatically reduced.…”

Section: Step 1 -Characterise Uncertain Model Inputs With Pdfsmentioning

confidence: 99%

Spatial Uncertainty Propagation Analysis with the spup R Package

Sawicka¹,

Heuvelink²,

Walvoort³

2019

The R Journal

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Many environmental and geographical models, such as those used in land degradation, agroecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs via the environmental model. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. The package also accommodates spatial auto-correlation within a variable and cross-correlation between variables. The MC realizations may be used as input to the environmental models written in or called from R. This article provides theoretical background and three worked examples that guide users through the application of spup.Uncertainty propagation aims to analyse how uncertainty (e.g. from measurement error, sampling, or interpolation) in spatial data, combined with modeling uncertainty (e.g. in model parameters and structure) propagate through the model . Many environmental and geographical phenomena of interest are spatial in nature and often have strong spatial correlations imposed by the physics and dynamics of the natural systems, making uncertainty evaluation difficult.A common approach to uncertainty propagation analysis makes use of MC stochastic simulation (Hammersley and Handscomb, 1979; Lewis and Orav, 1989). The MC approach is very flexible and can reach an arbitrary level of accuracy, and is therefore generally preferred over analytical methods such as the Taylor series method (Heuvelink et al., 1989). The MC method for uncertainty propagation analysis consists of the following main steps: (i) uncertainty about the variables is expressed by probability distribution functions (pdfs); (ii) many sets of possible uncertain inputs are generated from their marginal or joint probability distribution using a pseudo-random number generator; (iii) the model is run for each of the simulated input sets; (iv) the set of model outputs forms a random sample from the output pdf, so that the parameters of the output distribution, such as the mean, standard deviation and quantiles, can be estimated from the sample. In other words, the spread in the model outputs characterises how uncertainty about the model inputs has propagated to the model output. Note that the above ignores uncertainty in model parameters and model structure, but these can easily be included if available as pdfs. For example, these might have been obtained through Bayesian calibration (Van Oijen et al., 2005). The above four steps are described in detail further below.

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Section: Step 1 -Characterise Uncertain Model Inputs With Pdfsmentioning

confidence: 99%

Spatial Uncertainty Propagation Analysis with the spup R Package

Sawicka¹,

Heuvelink²,

Walvoort³

2019

The R Journal

View full text Add to dashboard Cite

show abstract

“…When a Monte Carlo (MC) scheme is implemented, parallel execution of the multiple realizations may significantly reduce simulation times and some studies have focused on MC simulation parallelization (Dong et al 2013; Leyva‐Suárez et al 2015). Inverse modeling and optimization also require a large number of model runs to obtain an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Parallel Simulation in Subsurface Hydrology: Evaluating the Performance of Modeling Computers

Sarris¹,

Scott

2020

Groundwater

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Monte Carlo uncertainty analysis, model calibration and optimization applications in hydrology, usually involve a very large number of forward transient model solutions, often resulting in computational bottlenecks. Parallel processing can significantly reduce overall simulation time, benefiting from the architecture of modern computers. This work investigates system performance using two realistic flow and transport modeling scenarios, applied to various modeling hardware, to provide information on the expected performance of parallel simulations and inform investment decisions. We investigate how performance, measured in terms of speedup and efficiency, changes with increasing number of parallel processes. We conclude that the maximum performance achieved by parallelization can range from 40% to 100% of the theoretical limit, with the lower increases associated with multi‐CPU servers. The number of parallel processes required to maximize performance is application dependent, and in contrast to common practice, often needs to be significantly larger than the total number of system CPU cores. Further testing is required to better understand how the physical problem being simulated affects the optimal number of parallel processes needed. Finally, when laptops are considered for modeling applications, careful consideration should be given not only to the specifications but also to the intended use designated by the manufacturer.

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Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project

Zhu

Geng

2016

Cluster Comput

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A parallel computing strategy for Monte Carlo simulation using groundwater models

Cited by 3 publications

References 14 publications

Spatial Uncertainty Propagation Analysis with the spup R Package

Spatial Uncertainty Propagation Analysis with the spup R Package

Parallel Simulation in Subsurface Hydrology: Evaluating the Performance of Modeling Computers

Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project

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