Acceleration of a 2D/3D finite-discrete element code for geomechanical simulations using General Purpose GPU computing

Lisjak, A.; Mahabadi, O. K.; Liu, He; Tatone, B. S. A.; Kaifosh, Patrick; Haque, Sardar Anisul; Grasselli, Giovanni

doi:10.1016/j.compgeo.2018.04.011

Cited by 99 publications

(44 citation statements)

References 39 publications

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“…Thus, the GPGPU accelerator must be kept busy to achieve its best performance. Even if different models and different architectures of the GPGPU accelerators are used to run the CUDA-based GPGPU-parallelized Y-HFDEM IDE discussed in this paper, the obtained speed-up time is quite competent compared with the performance of the OpenCL-based GPGPU code "IRAZU" reported by Lisjak et al 22 Finally, considering that the Quadro GP100 can be installed in an ordinary workstation, the demonstrated speed-up performances indicate that less space and time are required to solve large-scale hybrid FDEM simulations by applying the GPGPU-parallelized Y-HFDEM IDE. The presented speed-up list here can provide useful information for the application of GPGPU parallelization to the hybrid FDEM simulations.…”

Section: Performancementioning

confidence: 80%

“…Overall, it can be concluded that IRAZU, which is parallelized using OpenCL, is the only available GPGPU‐based hybrid FDEM code to date that is capable of modeling the rock fracture process . Thus, further studies are required to develop the GPGPU‐based hybrid FDEM.…”

Section: Introductionmentioning

confidence: 99%

“…Two main implementations of the hybrid FDEM include Y code [5][6][7][8] and the commercial code ELFEN. [9][10][11][12] Several attempts have been made to actively extend the Y code, such as Y-GEO, [13][14][15][16][17][18][19] IRAZU, [20][21][22] Solidity, [23][24][25][26][27][28][29] HOSS with MUNROU, 30,31 and Y-Flow. [32][33][34][35][36] In addition, the authors also developed the Y-HFDEM IDE (integrated development environment).…”

Section: Introductionmentioning

confidence: 99%

“…Overall, it can be concluded that IRAZU, which is parallelized using OpenCL, is the only available GPGPU-based hybrid FDEM code to date that is capable of modeling the rock fracture process. [20][21][22] Thus, further studies are required to develop the GPGPU-based hybrid FDEM. In this regard, we have developed free research code*, Y-HFDEM IDE, [37][38][39][40] and recently parallelized it using GPGPU with CUDA C/C++.…”

Section: Introductionmentioning

confidence: 99%

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Development of a GPGPU‐parallelized hybrid finite‐discrete element method for modeling rock fracture

Fukuda

Mohammadnejad

Liu

et al. 2019

Num Anal Meth Geomechanics

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Summary The hybrid finite‐discrete element method (FDEM) is widely used for engineering applications, which, however, is computationally expensive and needs further development, especially when rock fracture process is modeled. This study aims to further develop a sequential hybrid FDEM code formerly proposed by the authors and parallelize it using compute unified device architecture (CUDA) C/C++ on the basis of a general‐purpose graphics processing unit (GPGPU) for rock engineering applications. Because the contact detection algorithm in the sequential code is not suitable for GPGPU parallelization, a different contact detection algorithm is implemented in the GPGPU‐parallelized hybrid FDEM. Moreover, a number of new features are implemented in the hybrid FDEM code, including the local damping technique for efficient geostatic stress analysis, contact damping, contact friction, and the absorbing boundary. Then, a number of simulations with both quasi‐static and dynamic loading conditions are conducted using the GPGPU‐parallelized hybrid FDEM, and the obtained results are compared both quantitatively and qualitatively with those from either theoretical analysis or the literature to calibrate the implementations. Finally, the speed‐up performance of the hybrid FDEM is discussed in terms of its performance on various GPGPU accelerators and a comparison with the sequential code, which reveals that the GPGPU‐parallelized hybrid FDEM can run more than 128 times faster than the sequential code if it is run on appropriate GPGPU accelerators, such as the Quadro GP100. It is concluded that the GPGPU‐parallelized hybrid FDEM developed in this study is a valuable and powerful numerical tool for rock engineering applications.

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Section: Performancementioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a GPGPU‐parallelized hybrid finite‐discrete element method for modeling rock fracture

Fukuda

Mohammadnejad

Liu

et al. 2019

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

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“…One of the main assumptions of the study presented in this paper is that the estimation of favourable areas can be obtained by cross-analysing results from separate single-physic models. Several authors use either one-way coupled or fully coupled models as an attempt to more accurately address the physical features involved in large-scale phenomena (>kilometric characteristic length) [71][72][73][74][75].…”

Section: Relevancy Of the Model Cross-analysismentioning

confidence: 99%

Locating Geothermal Resources: Insights from 3D Stress and Flow Models at the Upper Rhine Graben Scale

et al. 2019

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To be exploited, geothermal resources require heat, fluid, and permeability. These favourable geothermal conditions are strongly linked to the specific geodynamic context and the main physical transport processes, notably stresses and fluid circulations, which impact heat-driving processes. The physical conditions favouring the setup of geothermal resources can be searched for in predictive models, thus giving estimates on the so-called “favourable areas.” Numerical models could allow an integrated evaluation of the physical processes with adapted time and space scales and considering 3D effects. Supported by geological, geophysical, and geochemical exploration methods, they constitute a useful tool to shed light on the dynamic context of the geothermal resource setup and may provide answers to the challenging task of geothermal exploration. The Upper Rhine Graben (URG) is a data-rich geothermal system where deep fluid circulations occurring in the regional fault network are the probable origin of local thermal anomalies. Here, we present a current overview of our team’s efforts to integrate the impacts of the key physics as well as key factors controlling the geothermal anomalies in a fault-controlled geological setting in 3D physically consistent models at the regional scale. The study relies on the building of the first 3D numerical flow (using the discrete-continuum method) and mechanical models (using the distinct element method) at the URG scale. First, the key role of the regional fault network is taken into account using a discrete numerical approach. The geometry building is focused on the conceptualization of the 3D fault zone network based on structural interpretation and generic geological concepts and is consistent with the geological knowledge. This DFN (discrete fracture network) model is declined in two separate models (3D flow and stress) at the URG scale. Then, based on the main characteristics of the geothermal anomalies and the link with the physics considered, criteria are identified that enable the elaboration of indicators to use the results of the simulation and identify geothermally favourable areas. Then, considering the strong link between the stress, fluid flow, and geothermal resources, a cross-analysis of the results is realized to delineate favourable areas for geothermal resources. The results are compared with the existing thermal data at the URG scale and compared with knowledge gained through numerous studies. The good agreement between the delineated favourable areas and the locations of local thermal anomalies (especially the main one close to Soultz-sous-Forêts) demonstrates the key role of the regional fault network as well as stress and fluid flow on the setup of geothermal resources. Moreover, the very encouraging results underline the potential of the first 3D flow and 3D stress models at the URG scale to locate geothermal resources and offer new research opportunities.

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Parallelized combined finite‐discrete element (FDEM) procedure using multi‐GPU with CUDA

Liu

Wei-qin

2019

Num Anal Meth Geomechanics

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SummaryThis paper focuses on the efficiency of finite discrete element method (FDEM) algorithmic procedures in massive computers and analyzes the time‐consuming part of contact detection and interaction computations in the numerical solution. A detailed operable GPU parallel procedure was designed for the element node force calculation, contact detection, and contact interaction with thread allocation and data access based on the CUDA computing. The emphasis is on the parallel optimization of time‐consuming contact detection based on load balance and GPU architecture. A CUDA FDEM parallel program was developed with the overall speedup ratio over 53 times after the fracture from the efficiency and fidelity performance test of models of in situ stress, UCS, and BD simulations in Intel i7‐7700K CPU and the NVIDIA TITAN Z GPU. The CUDA FDEM parallel computing improves the computational efficiency significantly compared with the CPU‐based ones with the same reliability, providing conditions for achieving larger‐scale simulations of fracture.

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Acceleration of a 2D/3D finite-discrete element code for geomechanical simulations using General Purpose GPU computing

Cited by 99 publications

References 39 publications

Development of a GPGPU‐parallelized hybrid finite‐discrete element method for modeling rock fracture

Development of a GPGPU‐parallelized hybrid finite‐discrete element method for modeling rock fracture

Locating Geothermal Resources: Insights from 3D Stress and Flow Models at the Upper Rhine Graben Scale

Parallelized combined finite‐discrete element (FDEM) procedure using multi‐GPU with CUDA

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