PyFR v2.0.3：面向工业采用规模解析模拟

IF 7.2 2区物理与天体物理 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Physics Communications Pub Date : 2025-02-28 DOI:10.1016/j.cpc.2025.109567

Freddie D. Witherden , Peter E. Vincent , Will Trojak , Yoshiaki Abe , Amir Akbarzadeh , Semih Akkurt , Mohammad Alhawwary , Lidia Caros , Tarik Dzanic , Giorgio Giangaspero , Arvind S. Iyer , Antony Jameson , Marius Koch , Niki Loppi , Sambit Mishra , Rishit Modi , Gonzalo Sáez-Mischlich , Jin Seok Park , Brian C. Vermeire , Lai Wang

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引用次数: 0

摘要

PyFR是一个基于高阶通量重建方法的开源跨平台计算流体动力学框架，专为在复杂工程几何形状附近进行高精度尺度解析模拟而设计。自2013年PyFR v0.1.0首次发布以来，该框架中添加了一系列新功能，以期实现工业应用。在这项工作中，我们详细介绍了PyFR v2.0.3中发布的这些增强功能，包括跨平台性能的改进（新的后端、DSL的扩展、新的矩阵乘法提供程序、数据布局的改进、任务图的使用）和数值稳定性的改进（模态滤波、抗混叠、人工粘度、熵滤波），以及棱柱形、四面体和金字塔形元素的添加。改进了对混合元素网格的域分解支持，改进了对弯曲元素网格的处理，增加了自适应时间步进功能，增加了不可压缩的Euler和Navier-Stokes解算器，改进了文件格式，并开发了插件架构。我们还解释了开发人员和用户社区发展的努力，并提供了一系列示例，展示了我们的用户群如何应用PyFR来解决广泛的基础、应用和工业流程问题。最后，我们展示了PyFR v2.0.3在具有冲击和湍流的超音速Taylor-Green涡旋情况下的准确性，并在ORNL的多达1024台AMD Instinct MI250X加速器（每个加速器有两个gcd）和CSCS的多达2048台Nvidia GH200 gpu上提供了最新的性能和缩放结果。我们注意到，考虑到硬件和软件改进的总和，PyFR的绝对性能在过去十年中保守地增长了近50倍。程序摘要程序标题：PyFRCPC库链接到程序文件：https://doi.org/10.17632/vmgh4kfjk6.1Developer's存储库链接：https://github.com/PyFR/PyFRLicensing条款：BSD 3- clause编程语言：Python（生成C/OpenMP， CUDA, OpenCL， HIP, Metal）问题性质：工业流程的准确和有效的规模解决模拟。解决方法：大规模并行跨平台实现高阶精确通量重建方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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PyFR v2.0.3: Towards industrial adoption of scale-resolving simulations

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption. In this work, we provide details of these enhancements as released in PyFR v2.0.3, including improvements to cross-platform performance (new backends, extensions of the DSL, new matrix multiplication providers, improvements to the data layout, use of task graphs) and improvements to numerical stability (modal filtering, anti-aliasing, artificial viscosity, entropy filtering), as well as the addition of prismatic, tetrahedral and pyramid shaped elements, improved domain decomposition support for mixed element grids, improved handling of curved element meshes, the addition of an adaptive time-stepping capability, the addition of incompressible Euler and Navier-Stokes solvers, improvements to file formats and the development of a plugin architecture. We also explain efforts to grow an engaged developer and user community and provided a range of examples that show how our user base is applying PyFR to solve a wide range of fundamental, applied and industrial flow problems. Finally, we demonstrate the accuracy of PyFR v2.0.3 for a supersonic Taylor-Green vortex case, with shocks and turbulence, and provided latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs) and up to 2048 Nvidia GH200 GPUs of Alps at CSCS. We note that absolute performance of PyFR accounting for the totality of both hardware and software improvements has, conservatively, increased by almost 50× over the last decade.

Program summary

Program Title: PyFR

CPC Library link to program files: https://doi.org/10.17632/vmgh4kfjk6.1

Developer's repository link: https://github.com/PyFR/PyFR

Licensing provisions: BSD 3-clause

Programming language: Python (generating C/OpenMP, CUDA, OpenCL, HIP, Metal)

Nature of problem: Accurate and efficient scale-resolving simulation of industrial flows.

Solution method: Massively parallel cross-platform implementation of high-order accurate Flux Reconstruction schemes.

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来源期刊

Computer Physics Communications 物理-计算机：跨学科应用

CiteScore

12.10

自引率

3.20%

发文量

287

审稿时长

5.3 months

期刊介绍： The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper. Computer Programs in Physics (CPiP) These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged. Computational Physics Papers (CP) These are research papers in, but are not limited to, the following themes across computational physics and related disciplines. mathematical and numerical methods and algorithms; computational models including those associated with the design, control and analysis of experiments; and algebraic computation. Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.