Quail: A lightweight open-source discontinuous Galerkin code in Python for teaching and prototyping

Ching, Eric J.; Bornhoft, Brett; Lasemi, Ali; Ihme, Matthias

doi:10.1016/j.softx.2022.100982

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…At the same time, it serves as an advanced research tool, capable to run in different architectures with multiple processors and with suitable scalability properties, thus allowing to tackle complex problems that are beyond the capabilities of existing codes [38,40]. Indeed, whereas the implicit-in-time matrix-free approach shares some common features with some available DG codes [41][42][43], it contrasts with most of the available open-source DG approaches, such as [44][45][46][47][48][49][50][51], which are CPU-based, and also with other codes running on GPUs which rely on explicit time-marching schemes, like [52,53].…”

Section: Motivation and Significancementioning

confidence: 99%

Exasim: Generating Discontinuous Galerkin Codes for Numerical Solutions of Partial Differential Equations on Graphics Processors

Vila-Pérez¹,

Heyningen²,

Nguyen³

et al. 2022

Preprint

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This paper presents an overview of the functionalities and applications of Exasim, an open-source code for generating high-order discontinuous Galerkin codes to numerically solve parametrized partial differential equations (PDEs). The software combines high-level and low-level languages to construct parametrized PDE models via Julia, Python or Matlab scripts and produce high-performance C++ codes for solving the PDE models on CPU and Nvidia GPU processors with distributed memory. Exasim provides matrix-free discontinuous Galerkin discretization schemes together with scalable reduced basis preconditioners and Newton-GMRES solvers, making it suitable for accurate and efficient approximation of wide-ranging classes of PDEs.

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