A Parallel Linear Temporal Logic Tableau

TL;DR

This paper presents a simple partitioning technique for parallelizing Linear Temporal Logic (LTL) tableaux, enabling independent processing of tableau branches to improve performance on benchmarks and random formulas.

Contribution

It introduces a novel partitioning method for branch-independent LTL tableaux that facilitates efficient parallel processing without interprocess communication.

Findings

Partitioning improves tableau processing speed.

Technique reduces communication overhead in parallel LTL tableau.

Performance gains observed on standard benchmarks and random formulas.

Abstract

For many applications, we are unable to take full advantage of the potential massive parallelisation offered by supercomputers or cloud computing because it is too hard to work out how to divide up the computation task between processors in such a way to minimise the need for communication. However, a recently developed branch-independent tableaux for the common LTL temporal logic should intuitively be easy to parallelise as each branch can be developed independently. Here we describe a simple technique for partitioning such a tableau such that each partition can be processed independently without need for interprocess communication. We investigate the extent to which this technique improves the performance of the LTL tableau on standard benchmarks and random formulas.