Matrix-free approaches for GPU acceleration of a high-order finite element hydrodynamics application using MFEM, Umpire, and RAJA

TL;DR

This paper presents a co-design strategy using GPU-accelerated, high-order finite element methods with abstraction layers to achieve scalable, portable performance in a multi-physics simulation code, contributing to open-source libraries.

Contribution

The work introduces a novel co-design approach combining new algorithms and abstraction layers for GPU acceleration in a multi-physics code, with contributions to open-source libraries.

Findings

Achieved scalability of the hydrodynamics package on different platforms.

Developed a single source code for diverse hardware architectures.

Contributed new algorithms and abstractions to open-source libraries.

Abstract

With the introduction of advanced heterogeneous computing architectures based on GPU accelerators, large-scale production codes have had to rethink their numerical algorithms and incorporate new programming models and memory management strategies in order to run efficiently on the latest supercomputers. In this work we discuss our co-design strategy to address these challenges and achieve performance and portability with MARBL, a next-generation multi-physics code in development at Lawrence Livermore National Laboratory. We present a two-fold approach, wherein new hardware is used to motivate both new algorithms and new abstraction layers, resulting in a single source application code suitable for a variety of platforms. Focusing on MARBL's ALE hydrodynamics package, we demonstrate scalability on different platforms and highlight that many of our innovations have been contributed back to open-source software libraries, such as MFEM (finite element algorithms) and RAJA (kernel abstractions).