Distributed $\mathcal{H}^2$-matrices for boundary element methods

TL;DR

This paper introduces distributed $ ext{H}^2$-matrices tailored for boundary element methods, enabling scalable and efficient handling of very large meshes on distributed systems by reducing global data dependencies.

Contribution

The paper presents a novel distributed $ ext{H}^2$-matrix approach that requires only local multilevel information, improving scalability over previous methods that stored global structures in every node.

Findings

Able to handle meshes with over 130 million triangles efficiently

Reduces data exchange by using local multilevel information

Demonstrates scalability on large distributed systems

Abstract

Standard discretization techniques for boundary integral equations, e.g., the Galerkin boundary element method, lead to large densely populated matrices that require fast and efficient compression techniques like the fast multipole method or hierarchical matrices. If the underlying mesh is very large, running the corresponding algorithms on a distributed computer is attractive, e.g., since distributed computers frequently are cost-effective and offer a high accumulated memory bandwidth. Compared to the closely related particle methods, for which distributed algorithms are well-established, the Galerkin discretization poses a challenge, since the supports of the basis functions influence the block structure of the matrix and therefore the flow of data in the corresponding algorithms. This article introduces distributed $\mathcal{H}^2$-matrices, a class of hierarchical matrices that is closely related to fast multipole methods and particularly well-suited for distributed computing. While earlier efforts required the global tree structure of the $\mathcal{H}^2$-matrix to be stored in every node of the distributed system, the new approach needs only local multilevel information that can be obtained via a simple distributed algorithm, allowing us to scale to significantly larger systems. Experiments show that this approach can handle very large meshes with more than $130$ million triangles efficiently.