  Partition refinement is a method for minimizing automata and transition
systems of various types. Recently, a new partition refinement algorithm and
associated tool CoPaR were developed that are generic in the transition type of
the input system and match the theoretical run time of the best known
algorithms for many concrete system types. Genericity is achieved by modelling
transition types as functors on sets and systems as coalgebras. Experimentation
has shown that memory consumption is a bottleneck for handling systems with a
large state space, while running times are fast. We have therefore extended an
algorithm due to Blom and Orzan, which is suitable for a distributed
implementation to the coalgebraic level of genericity, and implemented it in
CoPaR. Experiments show that this allows to handle much larger state spaces.
Running times are low in most experiments, but there is a significant penalty
for some.
