Accessible extreme-scale computing with Trixi.jl and the Julia programming language

使用 Trixi.jl 和 Julia 编程语言进行可访问的超大规模计算

• 批准号: 528753982
• 负责人: Professor Dr. Hendrik Ranocha
• 金额: --
• 依托单位: Center for Computational Engineering Science - Mathematics Division (MathCCES)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/528753982?language=en
• 关键词: Accessible; extreme; scale; computing; Trixi.jl

Developing numerical simulation software for efficient high-performance computing (HPC) is difficult, especially when considering mesh adaptivity and heterogeneous hardware systems. A key issue is the need to balance the ability to add new functionality to the existing code base with the requirements of a scalable and robust implementation. The Julia programming language is uniquely positioned to bridge the gap between ease of use and efficiency in high-performance computing. However, a lot of this potential is still unlocked, since few HPC applications written in Julia exist and little documentation is available on best practices for Julia in the HPC context. A central aim of this project is therefore to extend the usability and impact of our Julia-based research software Trixi.jl by making extreme-scale computing as accessible as the rest of the Julia experience. Trixi.jl is a Julia package for adaptive numerical simulations of transport-dominated phenomena in science and engineering, with a focus on extensibility, ease of use, and high performance. To achieve our goal, we will first improve the modularization of the package and introduce new HPC-aware data structures that encapsulate some of the complexities of parallel data exchange and mesh adaptivity. A C-compatible application programming interface will then be introduced to allow Trixi.jl being used from other HPC simulation programs. To further ensure that the implementation retains a good code quality and high computational performance, we will integrate HPC hardware into our continuous testing infrastructure and create an HPC benchmarking suite for continuous performance monitoring. Furthermore, we will document all steps of the research software development process and publish best practices of Julia for HPC. Finally, we will promote the further development of Trixi.jl by providing tutorials and hands-on workshops for other scientists, and actively engage with the Julia HPC community.

开发用于高效高性能计算（HPC）的数值仿真软件很困难，尤其是在考虑网格适应性和异质硬件系统时。一个关键问题是需要平衡将新功能添加到现有代码库的能力与可扩展和强大实现的要求。朱莉娅编程语言是独特的，可以弥合高性能计算中易用性和效率之间的差距。但是，由于很少有朱莉娅（Julia）编写的HPC应用程序，因此在HPC环境中，朱莉娅（Julia）的最佳实践几乎没有文档。因此，该项目的主要目的是通过制造与朱莉娅其他经验的极端计算来访问我们的朱莉娅研究软件Trixi.jl的可用性和影响。 Trixi.jl是朱莉娅（Julia）包装，用于对科学和工程中运输主导现象的自适应数值模拟，重点是扩展性，易用性和高性能。为了实现我们的目标，我们将首先改善包装的模块化，并引入新的HPC感知数据结构，以封装并行数据交换和网格自适应的某些复杂性。然后将引入C兼容的应用程序编程界面，以允许在其他HPC仿真程序中使用Trixi.jl。为了进一步确保实施保持良好的代码质量和高计算性能，我们将将HPC硬件集成到我们的连续测试基础架构中，并创建HPC基准测试套件，以进行连续性能监视。此外，我们将记录研究软件开发过程的所有步骤，并为HPC发布朱莉娅的最佳实践。最后，我们将通过为其他科学家提供教程和动手讲习班，并积极与Julia HPC社区互动，从而促进Trixi.jl的进一步发展。