Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search

TL;DR

This paper introduces OBAT, a parallel greedy best-first search algorithm that guarantees the number of expanded states remains within a constant factor of sequential GBFS, addressing challenges in parallelizing non-admissible search algorithms.

Contribution

The paper proposes OBAT, a novel parallel greedy search algorithm with bounded expansion relative to sequential GBFS, improving parallel search efficiency.

Findings

OBAT guarantees bounded expansion relative to sequential GBFS.

Experimental results show OBAT's efficiency and boundedness.

Compared to PUHF, OBAT maintains closer alignment with sequential search behavior.

Abstract

Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be expanded by some tie-breaking strategy for GBFS. We show that despite this constraint, the number of states expanded by PUHF is not bounded by a constant multiple of the number of states expanded by sequential GBFS with the worst-case tie-breaking strategy. We propose and experimentally evaluate One Bench At a Time (OBAT), a parallel greedy search which guarantees that the number of states expanded is within a constant factor of the number of states expanded by sequential GBFS with some tie-breaking policy.