A generic finite element framework on parallel tree-based adaptive meshes

TL;DR

This paper presents a parallel, adaptive finite element framework built on forest-of-trees data structures, demonstrating high scalability and competitive performance for large-scale scientific computations.

Contribution

It introduces a generic, parallel finite element framework with a novel mesh representation and algorithms, validated through implementation and performance studies.

Findings

Scales efficiently up to 32,200 CPU cores.

Achieves 2-3 times better performance than deal.ii in adaptive Poisson problems.

Supports both subassembled and fully-assembled linear system layouts.

Abstract

In this work we formally derive and prove the correctness of the algorithms and data structures in a parallel, distributed-memory, generic finite element framework that supports h-adaptivity on computational domains represented as forest-of-trees. The framework is grounded on a rich representation of the adaptive mesh suitable for generic finite elements that is built on top of a low-level, light-weight forest-of-trees data structure handled by a specialized, highly parallel adaptive meshing engine, for which we have identified the requirements it must fulfill to be coupled into our framework. Atop this two-layered mesh representation, we build the rest of data structures required for the numerical integration and assembly of the discrete system of linear equations. We consider algorithms that are suitable for both subassembled and fully-assembled distributed data layouts of linear system matrices. The proposed framework has been implemented within the FEMPAR scientific software library, using p4est as a practical forest-of-octrees demonstrator. A strong scaling study of this implementation when applied to Poisson and Maxwell problems reveals remarkable scalability up to 32.2K CPU cores and 482.2M degrees of freedom. Besides, a comparative performance study of FEMPAR and the state-of-the-art deal.ii finite element software shows at least comparative performance, and at most factor 2-3 improvements in the h-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively.