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

TL;DR

This paper introduces ExaHyPE, a code generation engine that supports interdisciplinary teams in developing high-performance PDE solvers by isolating roles such as application, algorithms, and optimization, using template-based code generation.

Contribution

The paper presents a role-oriented code generation framework for PDE solvers that simplifies collaboration and extension through modular templates and role separation.

Findings

Supports easy expansion of solver schemes

Enables integration of optimized algorithms

Improves low-level SIMD vectorization

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.