  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.
