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：我们探讨了为集合通信提炼高效网络拓扑结构的问题。我们提出了一种算法框架，用于构建针对工作负载中延迟与带宽权衡进行优化的直连拓扑结构。该方法针对给定的集群规模和度数，综合生成多种不同的拓扑结构及其调度方案，进而根据特定工作负载确定最合适的拓扑与调度。我们的算法从最优的小规模基础拓扑结构及其关联通信调度出发，通过一系列可迭代应用的技术，推导出规模更大的拓扑结构与调度方案。此外，我们将经过充分研究的大规模图拓扑结构纳入算法框架，通过一种新颖的多项式时间算法为其生成高效的集合通信调度。我们利用多个测试平台和大规模仿真评估，证明所推导的拓扑结构与调度方案能带来显著的性能提升。