Flexible and scalable particle-in-cell methods for massively parallel computations

TL;DR

This paper presents scalable, efficient algorithms for particle-in-cell methods integrated with dynamic mesh-based finite element codes, enabling large-scale scientific simulations with optimal complexity.

Contribution

It introduces novel data structures and algorithms for particle-in-cell methods compatible with dynamically changing, parallel meshes in large-scale finite element codes.

Findings

Algorithms achieve optimal complexity.

Suitable for very large-scale applications.

Demonstrated with practical numerical tests.

Abstract

Particle-in-cell methods couple mesh-based methods for the solution of continuum mechanics problems, with the ability to advect and evolve particles. They have a long history and many applications in scientific computing. However, they have most often only been implemented for either sequential codes, or parallel codes with static meshes that are statically partitioned. In contrast, many mesh-based codes today use adaptively changing, dynamically partitioned meshes, and can scale to thousands or tens of thousands of processors. Consequently, there is a need to revisit the data structures and algorithms necessary to use particle methods with modern, mesh-based methods. Here we review commonly encountered requirements of particle-in-cell methods, and describe efficient ways to implement them in the context of large-scale parallel finite-element codes that use dynamically changing meshes. We also provide practical experience for how to address bottlenecks that impede the efficient implementation of these algorithms and demonstrate with numerical tests both that our algorithms can be implemented with optimal complexity and that they are suitable for very large-scale, practical applications. We provide a reference implementation in ASPECT, an open source code for geodynamic mantle-convection simulations built on the deal.II library.