On Parallel External-Memory Bidirectional Search

TL;DR

This paper introduces a parallel external-memory framework for bidirectional heuristic search, specifically developing PEM-BAE*, which outperforms existing unidirectional and bidirectional algorithms on large-scale problems.

Contribution

It presents the first PEM framework for bidirectional heuristic search and demonstrates the superior performance of PEM-BAE* over other algorithms on hard problems.

Findings

PEM-BAE* outperforms PEM-A* and PEM-MM algorithms.

Bidirectional search algorithms outperform unidirectional ones.

The framework enables scaling to larger, more complex problems.

Abstract

Parallelization and External Memory (PEM) techniques have significantly enhanced the capabilities of search algorithms when solving large-scale problems. Previous research on PEM has primarily centered on unidirectional algorithms, with only one publication on bidirectional PEM that focuses on the meet-in-the-middle (MM) algorithm. Building upon this foundation, this paper presents a framework that integrates both uni- and bi-directional best-first search algorithms into this framework. We then develop a PEM variant of the state-of-the-art bidirectional heuristic search (BiHS) algorithm BAE* (PEM-BAE*). As previous work on BiHS did not focus on scaling problem sizes, this work enables us to evaluate bidirectional algorithms on hard problems. Empirical evaluation shows that PEM-BAE* outperforms the PEM variants of A* and the MM algorithm, as well as a parallel variant of IDA*. These findings mark a significant milestone, revealing that bidirectional search algorithms clearly outperform unidirectional search algorithms across several domains, even when equipped with state-of-the-art heuristics.