2018
DOI: 10.1016/j.advengsoft.2018.05.008
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Modelling fracture in heterogeneous materials on HPC systems using a hybrid MPI/Fortran coarray multi-scale CAFE framework

Abstract: A 3D multi-scale cellular automata finite element (CAFE) framework for modelling fracture in heterogeneous materials is described. The framework is implemented in a hybrid MPI/Fortran coarray code for efficient parallel execution on HPC platforms. Two open source BSD licensed libraries developed by the authors in modern Fortran were used: CGPACK, implementing cellular automata (CA) using Fortran coarrays, and ParaFEM, implementing finite elements (FE) using MPI. The framework implements a two-way concurrent hi… Show more

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Cited by 13 publications
(13 citation statements)
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“…A key advantage of the framework is its highly parallel implementation. In 2018 Shterenlikht et al [34] highlighted the applicability It is therefore suitable to highlight the numerical method- [35]. Each mini-app [36] 131 solves a specific engineering problem, some examples in-132 clude: heat transfer [37], stochastic finite element mod-133 elling [38], geometric nonlinearity [39], material nonlin-134 earity [40], image-based modelling [41] and fluid structure 135 interaction [42].…”
Section: Introductionmentioning
confidence: 99%
“…A key advantage of the framework is its highly parallel implementation. In 2018 Shterenlikht et al [34] highlighted the applicability It is therefore suitable to highlight the numerical method- [35]. Each mini-app [36] 131 solves a specific engineering problem, some examples in-132 clude: heat transfer [37], stochastic finite element mod-133 elling [38], geometric nonlinearity [39], material nonlin-134 earity [40], image-based modelling [41] and fluid structure 135 interaction [42].…”
Section: Introductionmentioning
confidence: 99%
“…In the future, it is worth exploring different numerical methods to ease the requirements on time-steps. In order to boost the simulation speed, one possible solution is parallel computing (Owen and Feng, 2001;Frantík et al, 2013;Shterenlikht et al, 2018). Another possible solution is the multiscale method, which can reduce computational cost for large-scale problems (Beex et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…Reid, Long, and Steidel (2020) documented the evolution of Fortran's parallel features. Although these features have seen relatively slow implementation by the compiler vendors, applications that use native parallel features are becoming more common (e.g., Garain, Balsara, and Reid 2015;Mozdzynski, Hamrud, and Wedi 2015;Shterenlikht, Margetts, and Cebamanos 2018;Curcic 2019;Diaz et al 2021). Today, Fortran remains the dominant language used on top High Performance Computing (HPC) systems, with applications to weather and climate (Powers et al 2017;Skamarock, Ong, and Klemp 2021), computational chemistry ( Čertík, Pask, and Vackář 2013;Apra et al 2020), computational fluid dynamics (Sharma and Moulitsas 2017), life science (Aguilar et al 2018;Vandenplas et al 2020), and economics (Harrison and Pearson 1996;Fehr and Kindermann 2018).…”
Section: Introductionmentioning
confidence: 99%