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Toward Performance-Portable Finite Element Methods on High-Performance Systems

机译:面向高性能系统中性能可移植的有限元方法

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We aim at performance portability of finite-element methods for solving PDE (Partial Differential Equations) on high-performance systems, i.e., ensuring that the same application code achieves good performance across a broad class of parallel architectures, including multi-core CPUs and many-core GPUs from different vendors. We achieve this goal by extending the popular PDE solving framework DUNE and integrating it with our parallel programming framework PACXX. We describe the implementation of our approach and we show how it allows the application programmers to use single C++ source code on a variety of target architectures by compiling it with different back-ends of the PACXX framework, achieving competitive performance against manually optimized original DUNE kernels.
机译:我们的目标是在高性能系统上求解PDE(偏微分方程)的有限元方法的性能可移植性,即,确保相同的应用程序代码在包括多核CPU和许多其他并行架构在内的广泛类别中都可以实现良好的性能。不同供应商提供的核心GPU。通过扩展流行的PDE解决方案框架DUNE并将其与我们的并行编程框架PACXX集成,我们实现了这一目标。我们描述了该方法的实现,并展示了它如何通过与PACXX框架的不同后端进行编译,使应用程序程序员如何在各种目标体系结构上使用单个C ++源代码,与手动优化的原始DUNE内核相比,具有竞争优势。

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