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.

翻译：精细的分区是尽量减少各种类型的自动和过渡系统的一种方法。 最近,开发了一种新的分区精细算法和相关工具COPAR, 在输入系统的过渡类型中是通用的, 并与许多具体系统类型中最已知的算法的理论运行时间相匹配。 将过渡类型建模作为机组和煤层系统中的配方实现通用。 实验表明, 内存消耗是使用大型国家空间的处理系统的一个瓶颈, 而运行时间则很快。 因此, 我们扩展了布罗姆和奥赞的算法, 适合将算法分布到通用性的煤层水平, 并在COPAR中实施。 实验显示, 它可以处理大得多的州空间。 运行时间在大多数实验中是低的, 但对某些实验有相当大的惩罚。