标题： 面向计算流体动力学代码的可移植高性能内核生成技术与DaCe 显示英文标题

标题： Portable High-Performance Kernel Generation for a Computational Fluid Dynamics Code with DaCe

摘要： 随着新型高性能计算（HPC）加速器的出现，如Nvidia和AMD的GPU，针对不同的硬件架构进行高效编程已成为HPC应用开发者的重大挑战。 HPC系统中日益增加的硬件多样性通常需要开发特定于架构的代码，这阻碍了大规模科学应用的可持续性。 在本工作中，我们利用DaCe，一种以数据为中心的并行编程框架，来自动化生成高性能内核。 DaCe能够为多核处理器和各种加速器自动生成代码，减轻了开发人员需要为每种新架构重写代码的负担。 我们的研究通过将DaCe的自动代码生成应用于计算流体动力学（CFD）中使用的关键计算内核来展示DaCe的能力。 具体来说，我们关注基于Fortran的求解器Neko，它采用谱元方法，该方法依赖于小张量操作。 我们详细说明了使用DaCe的状态化数据流多图（SDFG）表示法来构建此计算内核，并讨论了这种方法如何促进高性能代码的生成。 此外，我们概述了将DaCe生成的代码无缝集成到Neko求解器中的工作流程。 我们的结果突显了生成代码在多个平台上的可移植性和性能，包括Nvidia GH200、Nvidia A100和AMD MI250X GPU，并取得了具有竞争力的性能结果。 通过展示自动代码生成的潜力，我们强调了使用可移植解决方案确保大规模科学应用长期可持续性的可行性。 显示英文摘要

摘要： With the emergence of new high-performance computing (HPC) accelerators, such as Nvidia and AMD GPUs, efficiently targeting diverse hardware architectures has become a major challenge for HPC application developers. The increasing hardware diversity in HPC systems often necessitates the development of architecture-specific code, hindering the sustainability of large-scale scientific applications. In this work, we leverage DaCe, a data-centric parallel programming framework, to automate the generation of high-performance kernels. DaCe enables automatic code generation for multicore processors and various accelerators, reducing the burden on developers who would otherwise need to rewrite code for each new architecture. Our study demonstrates DaCe's capabilities by applying its automatic code generation to a critical computational kernel used in Computational Fluid Dynamics (CFD). Specifically, we focus on Neko, a Fortran-based solver that employs the spectral-element method, which relies on small tensor operations. We detail the formulation of this computational kernel using DaCe's Stateful Dataflow Multigraph (SDFG) representation and discuss how this approach facilitates high-performance code generation. Additionally, we outline the workflow for seamlessly integrating DaCe's generated code into the Neko solver. Our results highlight the portability and performance of the generated code across multiple platforms, including Nvidia GH200, Nvidia A100, and AMD MI250X GPUs, with competitive performance results. By demonstrating the potential of automatic code generation, we emphasise the feasibility of using portable solutions to ensure the long-term sustainability of large-scale scientific applications.