Performance Enhancement of Tree-based Friends-of-friend Galaxy-finder for High-resolution Simulations of Galaxy Formation

TL;DR

This paper improves the efficiency of tree-based friends-of-friend galaxy finders in high-resolution cosmological simulations by implementing optimizations that significantly reduce computational costs while maintaining accuracy.

Contribution

The authors introduce novel implementations for tree-based FoF algorithms that drastically enhance performance in high-dimensional, high-resolution galaxy simulations.

Findings

Achieved a 2700-fold speed increase in 3D FoF searches.

Maintained identical galaxy detection results with optimized algorithms.

Demonstrated effectiveness in high-dimensional (6D) galaxy finding.

Abstract

Cosmological simulations are useful tools for studying the evolution of galaxies, and it is critical to accurately identify galaxies and their halos from raw simulation data. The friends-of-friend (FoF) algorithm has been widely adopted for this purpose because of its simplicity and expandability to higher dimensions. However, it is cost-inefficient when applied to high-resolution simulations because standard FoF implementation leads to too many distance calculations in dense regions. We confirm this through our exercise of applying the 6-dimensional (6D) FoF galaxy finder code, VELOCIraptor (Elahi et al. 2019), on the NewHorizon simulation (Dubois et al. 2021). The high particle resolution of NewHorizon ($M_{\rm star} \sim 10^4 M_{\odot}$) allows a large central number density ($10^{6}\,{\rm kpc}^{-3}$) for typical galaxies, resulting in a few days to weeks of galaxy searches for just one snapshot. Even worse, we observed a significant decrease in the FoF performance in the high-dimensional 6D searches: "the curse of dimensionality" problem. To overcome these issues, we have developed several implementations that can be readily applied to any tree-based FoF code. They include limiting visits to tree nodes, re-ordering the list of particles for searching neighbor particles, and altering the tree structure. Compared to the run with the original code, the new run with these implementations results in the identical galaxy detection with the ideal performance, $O(N \log{N})$, $N$ being the number of particles in a galaxy -- with a speed gain of a factor of 2700 in 3D or 12 in 6D FoF search.