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.

翻译：并行化与外部存储器（PEM）技术显著提升了搜索算法在求解大规模问题时的能力。现有关于PEM的研究主要集中于单向算法，仅有一篇关于双向PEM的文献聚焦于中间相遇（MM）算法。基于此基础，本文提出了一个将单向与双向最佳优先搜索算法统一整合的框架。随后，我们开发了当前最先进的双向启发式搜索（BiHS）算法BAE*的PEM变体（PEM-BAE*）。由于先前关于BiHS的研究未关注大规模问题的扩展，本工作使得我们能够在复杂问题上评估双向算法。实验评估表明，PEM-BAE*在性能上优于A*与MM算法的PEM变体，以及并行化的IDA*变体。这些发现标志着一个重要里程碑，揭示了即使配备最先进的启发函数，双向搜索算法在多个领域仍明显优于单向搜索算法。