Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation

TL;DR

This paper introduces a distributed memory approach for anisotropic mesh adaptation that avoids collective communication, leveraging speculative execution and shared memory software to efficiently generate large, high-quality meshes on HPC architectures.

Contribution

It presents a novel distributed memory method that separates meshing from performance, utilizing speculative execution to improve scalability and mesh quality without global synchronization.

Findings

Generated meshes of up to 1 billion elements with high quality.

Achieved performance comparable to state-of-the-art HPC meshing software.

Demonstrated effective use of speculative execution in distributed mesh adaptation.

Abstract

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the nodes of an HPC cluster so that their interior elements are adapted while interface elements (already adapted) remain frozen to maintain mesh conformity. Lessons are presented regarding some re-designs of the shared memory software and how its speculative execution model is utilized by the distributed memory method to achieve good performance. The presented method is shown to generate meshes (of up to approximately 1 billion elements) with comparable quality and performance to existing state-of-the-art HPC meshing software.