GPU 上的天体物理学：介绍 AGILE 1.0
Astrophysics on GPUs: introducing AGILE 1.0
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中文总结 AI 辅助
介绍用于天体和太阳物理应用的 AGILE 框架，通过 OpenACC 卸载在 GPU 上实现自适应网格细化模拟，有多个物理模块，经强缩放测试和科学应用展示其在多 GPU 及不同问题规模下的性能和能力。
中文摘要 AI 辅助
我们展示了 AGILE，这是一个用于解决天体和太阳物理应用中出现的（近）守恒定律的启用 GPU 的自适应网格细化（AMR）框架。AGILE 用现代 Fortran 2003 编写，部分模块和网格处理继承自 MPI - AMRVAC，并通过 OpenACC 卸载实现出色的 GPU 性能。我们讨论了使 AGILE 能够以适度的块大小（例如\(16^3\)个单元）进行经济高效且可扩展的深度嵌套 AMR 模拟的设计决策。AGILE 目前实现了多个物理模块，即流体动力学、冻结场流体动力学、磁流体动力学和狭义相对论流体动力学，并且通过其模块化设计可以轻松进一步扩展。除了对多达 2048 个 GPU 的强缩放测试以及在各种设备和问题规模上显示出一致性能的标准基准测试外，我们还通过使用所有当前可用物理模块的前沿科学应用展示了 AGILE 的能力。
英文摘要
We present AGILE, a GPU-enabled adaptive mesh refinement (AMR) framework for the solution of (near-) conservation laws which occur in astro- and solar-physical applications. AGILE is written in modern fortran 2003, inherits a part of its modules and mesh handling from MPI-AMRVAC, and achieves excellent GPU performance via OpenACC offloading. We here discuss the design decisions which enable AGILE to perform cost-efficient and scalable deeply nested AMR simulations with moderate block sizes of e.g. $16^3$ cells. AGILE currently implements several physics modules, ie. hydrodynamics, frozen-field hydrodynamics, magnetohydrodynamics and special-relativistic hydrodynamics and can easily be extended further through its modular design. Besides strong scaling tests to up to 2048 GPUs and standard benchmarks which show consistent performance across a large range of devices and problem sizes, we demonstrate AGILE's capabilities by means of state-of-the art science applications with all currently available physics modules.
