We introduce Broadcast by Balanced Saturation (BBS), a general class of tree-based pipelined broadcast algorithms that optimizes communication efficiency across diverse network topologies, with a particular emphasis on large message sizes. By addressing spanning tree construction and communication task scheduling, two fundamental theoretical challenges in broadcasting, BBS offers a unified and flexible framework that operates effectively under varied network constraints. The algorithm maximizes aggregated throughput while simultaneously addressing topology constraints, synchronization overhead, bandwidth limitations and contention. Using SimGrid under standard assumptions, including full-duplex and one-port communication, various algorithms were evaluated on Mesh, Butterfly, Dragonfly, and Fat-Tree topologies. Results demonstrate that BBS consistently outperforms both general-purpose and topology-aware broadcast algorithms across a wide range of topologies and message sizes, establishing it as a robust and high-performance solution for large-scale systems.

翻译：我们提出了一种基于平衡饱和度的广播（BBS）算法，这是一类通用的基于树形管道的广播算法，旨在优化跨多种网络拓扑的通信效率，尤其适用于大消息场景。通过解决广播中两个基础理论难题——生成树构建与通信任务调度，BBS 提供了一个统一且灵活的框架，能在不同网络约束下有效运行。该算法在最大化聚合吞吐量的同时，协同处理拓扑约束、同步开销、带宽限制及竞争问题。基于标准假设（包括全双工与单端口通信），利用 SimGrid 在 Mesh、Butterfly、Dragonfly 和 Fat-Tree 拓扑上对多种算法进行了评估。结果表明，BBS 在各类拓扑和消息大小下均持续优于通用及拓扑感知广播算法，成为大规模系统中稳健且高性能的解决方案。