  Context. New-generation cosmological simulations are providing huge amounts
of data, whose analysis becomes itself a cutting-edge computational problem. In
particular, the identification of gravitationally bound structures, known as
halo finding, is one of the main analyses. A handful of codes developed to
tackle this task have been presented during the last years.
  Aims. We present a deep revision of the already existing code ASOHF. The
algorithm has been throughfully redesigned in order to improve its capabilities
to find bound structures and substructures, both using dark matter particles
and stars, its parallel performance, and its abilities to handle simulation
outputs with vast amounts of particles. This upgraded version of ASOHF is
conceived to be a publicly available tool.
  Methods. A battery of idealised and realistic tests are presented in order to
assess the performance of the new version of the halo finder.
  Results. In the idealised tests, ASOHF produces excellent results, being able
to find virtually all the structures and substructures placed within the
computational domain. When applied to realistic data from simulations, the
performance of our finder is fully consistent with the results from other
commonly used halo finders, with remarkable performance in substructure
detection. Besides, ASOHF turns out to be extremely efficient in terms of
computational cost.
  Conclusions. We present a public, deeply revised version of the ASOHF halo
finder. The new version of the code produces remarkable results finding haloes
and subhaloes in cosmological simulations, with an excellent parallel
performance and with a contained computational cost.
