Mercury-Ar$\chi$es: a high-performance n-body code for planet formation studies

TL;DR

Mercury-Ar$ m ext{chi}$es is a high-performance, parallel n-body simulation code designed for detailed and efficient modeling of planet formation processes, including planetary growth, migration, and disk interactions.

Contribution

This paper introduces Mercury-Ar$ m ext{chi}$es, a new high-performance, shared-memory parallel n-body code that enhances modeling of planet formation with advanced physical and computational features.

Findings

Efficient implementation using OpenMP for shared memory systems.

Capability to simulate planetary growth, migration, and disk interactions.

Applicable to both Solar System and exoplanetary studies.

Abstract

Forming planetary systems are populated by large numbers of gravitationally interacting planetary bodies, spanning from massive giant planets to small planetesimals akin to present-day asteroids and comets. All these planetary bodies are embedded in the gaseous embrace of their native protoplanetary disks, and their interactions with the disk gas play a central role in shaping their dynamical evolution and the outcomes of planet formation. These factors make realistic planet formation simulations extremely computationally demanding, which in turn means that accurately modeling the formation of planetary systems requires the use of high-performance methods. The planet formation code Mercury-Ar$\chi$es was developed to address these challenges and, since its first implementation, has been used in multiple exoplanetary and Solar System studies. Mercury-Ar$\chi$es is a parallel n-body code that builds on the widely used Mercury code and is capable of modeling the growth and migration of forming planets, the interactions between planetary bodies and the disk gas, as well as the evolving impact flux of planetesimals on forming planets across the different stages of their formation process. In this work we provide the up-to-date overview of its physical modeling capabilities and the first detailed description of its high-performance implementation based on the OpenMP directive-based parallelism for shared memory environments, to harness the multi-thread and vectorization features of modern processor architectures.