REBOUND: An open-source multi-purpose N-body code for collisional dynamics

TL;DR

REBOUND is an open-source, highly modular N-body simulation code designed for collisional dynamics and classical problems, supporting various integrators, boundary conditions, and parallel computing, with optimized collision detection algorithms.

Contribution

It introduces a flexible, open-source N-body code with multiple integrators, boundary conditions, and optimized collision detection, suitable for diverse astrophysical simulations.

Findings

Efficient performance on desktops and clusters

Superior collision detection with plane-sweep algorithm in specific scenarios

Accurate and scalable simulation results

Abstract

REBOUND is a new multi-purpose N-body code which is freely available under an open-source license. It was designed for collisional dynamics such as planetary rings but can also solve the classical N-body problem. It is highly modular and can be customized easily to work on a wide variety of different problems in astrophysics and beyond. REBOUND comes with three symplectic integrators: leap-frog, the symplectic epicycle integrator (SEI) and a Wisdom-Holman mapping (WH). It supports open, periodic and shearing-sheet boundary conditions. REBOUND can use a Barnes-Hut tree to calculate both self-gravity and collisions. These modules are fully parallelized with MPI as well as OpenMP. The former makes use of a static domain decomposition and a distributed essential tree. Two new collision detection modules based on a plane-sweep algorithm are also implemented. The performance of the plane-sweep algorithm is superior to a tree code for simulations in which one dimension is much longer than the other two and in simulations which are quasi-two dimensional with less than one million particles. In this work, we discuss the different algorithms implemented in REBOUND, the philosophy behind the code's structure as well as implementation specific details of the different modules. We present results of accuracy and scaling tests which show that the code can run efficiently on both desktop machines and large computing clusters.