Distributed Coalgebraic Partition Refinement

TL;DR

This paper extends a coalgebraic partition refinement algorithm to a distributed setting, enabling the handling of larger state spaces efficiently, with experimental validation demonstrating improved scalability and performance.

Contribution

It introduces a distributed version of a coalgebraic partition refinement algorithm and implements it in CoPaR, significantly increasing the capacity to process large systems.

Findings

Allows handling larger state spaces in partition refinement

Maintains low running times in most experiments

Experiences some performance penalties in certain cases

Abstract

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.