English

GPU-Accelerated Discontinuous Galerkin Methods: 30x Speedup on 345 Billion Unknowns

Computational Physics 2020-09-01 v3 Distributed, Parallel, and Cluster Computing Numerical Analysis Performance Numerical Analysis Fluid Dynamics

Abstract

A discontinuous Galerkin method for the discretization of the compressible Euler equations, the governing equations of inviscid fluid dynamics, on Cartesian meshes is developed for use of Graphical Processing Units via OCCA, a unified approach to performance portability on multi-threaded hardware architectures. A 30x time-to-solution speedup over CPU-only implementations using non-CUDA-Aware MPI communications is demonstrated up to 1,536 NVIDIA V100 GPUs and parallel strong scalability is shown up to 6,144 NVIDIA V100 GPUs for a problem containing 345 billion unknowns. A comparison of CUDA-Aware MPI communication to non-GPUDirect communication is performed demonstrating an additional 24% speedup on eight nodes composed of 32 NVIDIA V100 GPUs.

Keywords

Cite

@article{arxiv.2006.15698,
  title  = {GPU-Accelerated Discontinuous Galerkin Methods: 30x Speedup on 345 Billion Unknowns},
  author = {Andrew C. Kirby and Dimitri J. Mavriplis},
  journal= {arXiv preprint arXiv:2006.15698},
  year   = {2020}
}

Comments

7 pages, 4 figures, 40 references. Accepted to 2020 IEEE HPEC

R2 v1 2026-06-23T16:41:01.371Z