We consider the problem of distilling efficient network topologies for collective communications. We provide an algorithmic framework for constructing direct-connect topologies optimized for the latency vs. bandwidth trade-off associated with the workload. Our approach synthesizes many different topologies and schedules for a given cluster size and degree and then identifies the appropriate topology and schedule for a given workload. Our algorithms start from small, optimal base topologies and associated communication schedules and use a set of techniques that can be iteratively applied to derive much larger topologies and schedules. Additionally, we incorporate well-studied large-scale graph topologies into our algorithmic framework by producing efficient collective schedules for them using a novel polynomial-time algorithm. Our evaluation uses multiple testbeds and large-scale simulations to demonstrate significant performance benefits from our derived topologies and schedules.

翻译：我们研究了为集合通信提炼高效网络拓扑结构的问题。我们提供了一个算法框架，用于构建针对工作负载相关的延迟与带宽权衡进行优化的直接连接拓扑。我们的方法针对给定的集群规模和度数合成多种不同的拓扑结构及调度方案，进而确定为特定工作负载最适合的拓扑与调度。算法从小型、最优的基础拓扑及相应的通信调度出发，采用一系列可迭代应用的技术，从而推导出更大的拓扑结构与调度方案。此外，我们通过一种新颖的多项式时间算法为大规模图拓扑结构生成高效的集合调度，从而将经过充分研究的大规模图拓扑纳入我们的算法框架。评估环节利用多个测试平台和大规模仿真，证明我们推导出的拓扑与调度方案能带来显著的性能提升。