A linear parallel algorithm to compute bisimulation and relational coarsest partitions

TL;DR

This paper introduces the first linear-time parallel algorithm for computing strong bisimulation on transition systems, suitable for modern parallel hardware like GPUs, significantly improving efficiency over previous methods.

Contribution

It presents a novel linear-time PRAM algorithm for strong bisimulation, optimized for massive parallel devices, and demonstrates its practical implementation on GPUs.

Findings

Linear time complexity $O(n+| ext{Act}|)$ achieved on PRAMs.

Implementation on GPU confirms practical efficiency.

Outperforms previous $O(n \log n)$ algorithms.

Abstract

The most efficient way to calculate strong bisimilarity is by calculation the relational coarsest partition on a transition system. We provide the first linear time algorithm to calculate strong bisimulation using parallel random access machines (PRAMs). More precisely, with $n$ states, $m$ transitions and $|\mathit{Act}|\leq m$ action labels, we provide an algorithm on $max(n,m)$ processors that calculates strong bisimulation in time $O(n+|\mathit{Act}|)$ and space $O(n+m)$. The best-known PRAM algorithm has time complexity $O(n\log n)$ on a smaller number of processors making it less suitable for massive parallel devices such as GPUs. An implementation on a GPU shows that the linear time-bound is achievable on contemporary hardware.