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技术显著提升了搜索算法解决大规模问题的能力。先前关于并行外部存储器的研究主要集中于单向算法，仅有一篇关于双向并行外部存储器的文献聚焦于中间相遇算法。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整合至该框架的统一框架。随后，我们开发了当前最先进的双向启发式搜索算法BAE*的并行外部存储器变体。由于先前关于双向启发式搜索的研究未关注大规模问题扩展，本工作使得我们能够在复杂问题上评估双向算法。实证评估表明，并行外部存储器-BAE*在性能上优于A*与中间相遇算法的并行外部存储器变体，以及并行迭代深化A*算法。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式函数，双向搜索算法在多个领域仍明显优于单向搜索算法。