Exploring Parallelization of Conjunctive Branches in Tableau-Based Description Logic Reasoning

Multiprocessor equipment is cheap and ubiquitous now, but users of description logic (DL) reasoners have to face the awkward fact that the major tableau-based DL reasoners can make use only one of the available processors. Recently, researchers have started investigating how concurrent computing can play a role in tableau-based DL reasoning with the intention of fully exploiting the processing resources of multiprocessor computers. The published research mostly focuses on utilizing disjunctive branches, the or-part of tableau expansion trees. We investigated the possibility and the role of concurrently processing conjunctive branches, the and-part of tableau expansion trees. In this work, we present an algorithm to process conjunctive branches in parallel and address the key implementation aspects of the algorithm. A research prototype to execute this algorithm has been developed and empirically evaluated. The experimental results are presented and analyzed. We found that parallelizing the processing of conjunctive branches of tableau expansion trees is auspicious and can partly evolve into a scalable solution for DL reasoning.