Parallel Adaptive Anisotropic Meshing on cc-NUMA Machines

TL;DR

This paper presents a parallel anisotropic mesh adaptation method optimized for cc-NUMA architectures, achieving high efficiency and supporting CAD data, crucial for advanced CFD simulations on supercomputers.

Contribution

It introduces a fine-grained speculative approach for anisotropic mesh operations with over 90% parallel efficiency on multi-core nodes, including CAD data support.

Findings

Achieves over 90% parallel efficiency on multi-core nodes.

Validates effectiveness across diverse test-cases including NASA data.

Supports CAD-based data for complex geometries.

Abstract

Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its effectiveness is demonstrated on one of NASA's High-Lift prediction workshop cases.