发表机构
China Telecom Research Institute（中国电信研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SIP-MOSP异步精确并行框架，通过版本化前沿快照与索引化支配剪枝分离标签扩展和前沿维护，在多种图拓扑上实现高达46.9倍加速，并显著降低内存。
AI 中文摘要
精确多目标最短路径（MOSP）搜索计算指定起点和终点顶点之间的完整帕累托集，其计算成本会随着非支配标签集的扩大以及对每个顶点帕累托前沿的频繁支配测试而迅速增长。高效并行化精确MOSP仍然是一个开放挑战。本文提出SIP-MOSP（基于快照的索引化剪枝MOSP），一种异步精确框架，在单一协作搜索中将标签扩展与前沿维护分离。SIP-MOSP结合了不可变的版本化前沿快照与索引化支配剪枝，使得无需并发访问同一可变前沿即可进行并发标签处理。这些机制共同减少了同步开销并加速了支配测试。我们使用块最小值（SIP-MOSP-BM）和线段树最小值（SIP-MOSP-ST）索引实例化该框架，并证明了其精确性。我们将两种变体与四种涵盖顺序和并行搜索的最新精确MOSP基线进行了评估。在道路网络、互联网服务提供商拓扑以及一个180顶点完全有向图上，跨多个目标维度的实验表明，在共同求解的实例上，SIP-MOSP相对于性能最佳的顺序基线实现了高达46.9倍的加速，相对于性能最佳的并行基线实现了高达7.05倍的加速。在20目标完全图设置中，许多实例在一小时内未被顺序基线求解，SIP-MOSP-ST实现了3.34倍的加速，同时相对于性能最佳的并行基线将峰值内存减少了60.3倍。这些结果表明，SIP-MOSP是跨结构多样图拓扑的精确MOSP的高效共享内存框架。
英文摘要
Exact multi-objective shortest-path (MOSP) search computes the complete Pareto set between specified start and goal vertices, and its computational cost can grow rapidly with expanding nondominated label sets and frequent dominance tests over per-vertex Pareto frontiers. Efficiently parallelizing exact MOSP remains an open challenge. This paper presents SIP-MOSP (Snapshot-based Indexed-Pruning MOSP), an asynchronous exact framework that separates label expansion from frontier maintenance within a single cooperative search. SIP-MOSP combines immutable versioned frontier snapshots with indexed dominance pruning, enabling concurrent label processing without concurrent access to the same mutable frontier. Together, these mechanisms reduce synchronization overhead and accelerate dominance testing. We instantiate the framework with block-minimum (SIP-MOSP-BM) and segment-tree-minimum (SIP-MOSP-ST) indices and prove exactness. We evaluate both variants against four state-of-the-art exact MOSP baselines covering sequential and parallel search. Experiments across multiple objective dimensions on a road network, an Internet service provider topology, and an 180-vertex complete directed graph show that SIP-MOSP achieves speedups of up to 46.9* over the best-performing sequential baseline and up to 7.05* over the best-performing parallel baseline on mutually solved instances. In the 20-objective complete-graph setting, where many instances remain unsolved by the sequential baselines within one hour, SIP-MOSP-ST achieves a 3.34* speedup while reducing peak memory by a factor of 60.3 relative to the best-performing parallel baseline. These results demonstrate that SIP-MOSP is an efficient shared-memory framework for exact MOSP across structurally diverse graph topologies.
