通过动态重配与通信重叠，显著降低分布式训练的通信耗时。
Enabling Reconfiguration-Communication Overlap for Collective Communication in Optical Networks
- 在集体通信中动态调整光网络拓扑，匹配算法需求
- 实现最高89.7%的通信完成时间减少，提升抗延迟能力
- 适合大规模分布式机器学习系统中的高速通信场景
集体通信（CC）对分布式机器学习（DML）的扩展至关重要。DML具有可预测的流量模式，为应用光网络技术提供了良好机会。具备可重构拓扑的光网络能为集体通信提供高带宽和低延迟。然而，现有方法存在固有局限：静态拓扑对动态通信模式效率低下，而频繁匹配算法每一步的拓扑重配又带来显著开销。本文提出SWOT，一种需求感知的光网络框架，采用“组内重配”机制，动态对齐网络资源与CC流量模式。SWOT通过三种关键技术隐藏重配延迟：异构消息分割、异步重叠与拓扑绕行，将重配时间与数据传输重叠。大量仿真表明，相较于静态基线，SWOT在多种CC算法下将通信完成时间最多降低89.7%，展现出对不同光资源和重配延迟的强鲁棒性。
原文摘要 · Abstract (English)
Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a great opportunity for applying optical network technologies. Optical networks with reconfigurable topologies promise high bandwidth and low latency for collective communications. However, existing approaches face inherent limitations: static topologies are inefficient for dynamic communication patterns within CC algorithm, while frequent topology reconfiguration matching every step of the algorithm incurs significant overhead. In this paper, we propose SWOT, a demand-aware optical network framework that employs ``intra-collective reconfiguration'' to dynamically align network resources with CC traffic patterns. SWOT hides reconfiguration latency by overlapping it with data transmission through three key techniques: \textit{Heterogeneous Message Splitting}, \textit{Asynchronous Overlapping}, and \textit{Topology Bypassing}. Extensive simulations demonstrate that SWOT reduces communication completion time up to 89.7% across diverse CC algorithm compared to static baselines, demonstrating strong robustness to varying optical resources and reconfiguration delay.
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