PPAPI: A Program for Groundwater Modeling Tasks in Distributed Parallel Computing Environments

Schumacher, Jens; Hayley, Kevin; Boutin, Louis-Charles; White, Emma

doi:10.1111/gwat.12623

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…The Amazon EC2 cloud computing service was used to provide computational resources for the IES analysis. Distribution of model resource files and initiation of PEST++ slave processes was automated using PPAPI software [27]. Based on previous experience [16], fewer high CPU instances were selected for the analysis, and the computation was distributed over a network of up to nine cloud computing instances with thirty-six CPUs each for a total maximum of 324 CPUs.…”

Section: Cloud Computing Implementationmentioning

confidence: 99%

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Hayley

Valenza²,

White

et al. 2019

Water

Self Cite

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Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or prediction requires models that include unsaturated flow, large magnitude hydraulic gradients and often require transient simulations with time varying material properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, long run times and instability can result in reduced effort for predictive uncertainty analysis, and ultimately decrease the value of the decision-support modelling. This study presents an early application of the Iterative Ensemble Smoother (IES) method of calibration-constrained uncertainty analysis to a mining groundwater flow model. The challenges of mining models and uncertainty quantification were addressed using the IES method and facilitated by highly parallelized cloud computing. The project was an open pit mine in South Australia that required predictions of pit water levels and inflow rates to guide the design of a proposed pumped hydro energy storage system. The IES calibration successfully produced 150 model parameter realizations that acceptably reproduced groundwater observations. The flexibility of the IES method allowed for the inclusion of 1493 adjustable parameters and geostatistical realizations of hydraulic conductivity fields to be included in the analysis. Through the geostatistical realizations and IES analysis, alternative conceptual models of fractured rock aquifer orientation and connections could be conditioned to observation data and used for predictive uncertainty analysis. Importantly, the IES method out-performed finite difference methods when model simulations contained small magnitude numerical instabilities.

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Section: Cloud Computing Implementationmentioning

confidence: 99%

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Hayley

Valenza²,

White

et al. 2019

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…Parallel computing solves large-scale or multimillion-cell simulation problems by allocating and resolving tasks to available multiprocessors simultaneously [ 16 ]. Parallel computing has been proved to be a more efficient approach that overcomes the limitations (i.e., constraints on problem size, CPU time, and space resolution) of groundwater simulation [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Parallel Processing Transport Model MT3DMS by Using OpenMP

Huang

Wang

Liu

et al. 2018

IJERPH

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Solute transport modeling resolves advection, dispersion, and chemical reactions in groundwater systems with its accuracy depending on the resolution of domain at all scales, thus the computational efficiency of a simulator becomes a bottleneck for the wide application of numerical simulations. However, the traditional serial numerical simulators have reached their limits for the prohibitive computational time and memory requirement in solving large-scale problems. These limitations have greatly hindered the wide application of groundwater solute transport modeling. Thus, the development of an efficient method for handling large-scale groundwater solute transport simulation is urgently required. In this study, we developed and assessed a parallelized MT3DMS (Modular Three-Dimensional Multispecies Transport Model) by using OpenMP (Open specifications for Multi-Processing) to accelerate the solute transport simulation process. The parallelization was achieved by adding OpenMP compile directives (i.e., defining various types of parallel regions) into the most time-consuming packages, including the Advection package (ADV), Dispersion package (DSP), and Generalized Conjugate Gradient Solver package (GCG). This allows parallel processing on shared-memory multiprocessors, i.e., both the memory requirement and computing efforts are automatically distributed among all processors. Moreover, we discussed two different parallelization strategies for handling numerical models with either many layers or few layers. The performance of parallelized MT3DMS was assessed by two benchmark numerical models with different model domain sizes via a workstation with two quad-core processors. Results showed that the running time of parallelized MT3DMS can be 4.15 times faster than that using sequential MT3DMS. The effects of using different preconditioners (procedures that transform a given problem into a form that is more suitable for numerical solving methods) in the GCG package were additionally evaluated. The modified strategy for handling numerical models with few layers also achieved satisfactory results with running time two times faster than that via sequential simulation. Thus, the proposed parallelization allows high-resolution groundwater transport simulation with higher efficiency for large-scale or multimillion-cell simulation problems.

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Deep Learning Optimization for Many-Core Virtual Platforms

Cai

Ning

Zheng

2021

Parallel Architectures, Algorithms and Programming

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PPAPI: A Program for Groundwater Modeling Tasks in Distributed Parallel Computing Environments

Abstract: Article impact statement: The software presented in the article helps to run highly parameterized groundwater model calibrations and uncertainty analysis.

Cited by 3 publications

References 5 publications

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study

Parallel Processing Transport Model MT3DMS by Using OpenMP

Deep Learning Optimization for Many-Core Virtual Platforms

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