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Role-Oriented Code Generation in an Engine for Solving Hyperbolic PDE Systems

  • Jean-Matthieu GallardEmail author
  • Lukas Krenz
  • Leonhard Rannabauer
  • Anne Reinarz
  • Michael Bader
Conference paper
  • 5 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1190)

Abstract

The development of a high performance PDE solver requires the combined expertise of interdisciplinary teams with respect to application domain, numerical scheme and low-level optimization. In this paper, we present how the ExaHyPE engine facilitates the collaboration of such teams by isolating three roles: application, algorithms, and optimization expert. We thus support team members in letting them focus on their own area of expertise while integrating their contributions into an HPC production code.

Inspired by web application development practices, ExaHyPE relies on two custom code generation modules, the Toolkit and the Kernel Generator, which follow a Model-View-Controller architectural pattern on top of the Jinja2 template engine library. Using Jinja2’s templates to abstract the critical components of the engine and generated glue code, we isolate the application development from the engine. The template language also allows us to define and use custom template macros that isolate low-level optimizations from the numerical scheme described in the templates.

We present three use cases, each focusing on one of our user roles, showcasing how the design of the code generation modules allows to easily expand the solver schemes to support novel demands from applications, to add optimized algorithmic schemes (with reduced memory footprint, e.g.), or provide improved low-level SIMD vectorization support.

Keywords

ExaHyPE Code generation High-order discontinuous Galerkin Hyperbolic PDE systems Model-View-Controller Jinja2 

Notes

Acknowledgements and Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671698. We thank the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for providing computing resources on the GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre (www.lrz.de).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jean-Matthieu Gallard
    • 1
    Email author
  • Lukas Krenz
    • 1
  • Leonhard Rannabauer
    • 1
  • Anne Reinarz
    • 1
  • Michael Bader
    • 1
  1. 1.Department of InformaticsTechnical University of MunichMunichGermany

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