用于集合通信的高效直连拓扑
Efficient Direct-Connect Topologies for Collective Communications
AI总结：
研究集合通信的高效网络拓扑提取问题，提出一种构建直连拓扑的算法框架，通过从小规模最优基础拓扑迭代推导并结合大规模图拓扑的多项式时间调度算法，为给定工作负载识别最优拓扑与调度，在多测试平台和大规模模拟中展现显著性能优势。
摘要：
我们考虑为集合通信提取高效网络拓扑的问题。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连拓扑。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和调度，然后为给定工作负载识别合适的拓扑和调度。我们的算法从较小的最优基础拓扑及关联的通信调度出发，使用可迭代应用的技术来推导出大得多的拓扑和调度。此外，我们通过使用一种新颖的多项式时间算法为这些拓扑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。我们的评估使用多个测试平台和大规模模拟，证明了我们推导出的拓扑和调度具有显著的性能优势。
英文摘要：
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 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.
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