Dragonfly interconnect is a crucial network technology for supercomputers. To support exascale systems, network resources are shared such that links and routers are not dedicated to any node pair. While link utilization is increased, workload performance is often offset by network contention. Recently, intelligent routing built on reinforcement learning demonstrates higher network throughput with lower packet latency. However, its effectiveness in reducing workload interference is unknown. In this work, we present extensive network simulations to study multi-workload contention under different routing mechanisms, intelligent routing and adaptive routing, on a large-scale Dragonfly system. We develop an enhanced network simulation toolkit, along with a suite of workloads with distinctive communication patterns. We also present two metrics to characterize application communication intensity. Our analysis focuses on examining how different workloads interfere with each other under different routing mechanisms by inspecting both application-level and network-level metrics. Several key insights are made from the analysis.

翻译：龙飞虫互连是支持超级计算机的关键网络技术。为支撑百亿亿次系统，网络资源采用共享方式，使链路和路由器不再专属于任何节点对。虽然链路利用率得以提升，但网络争用常会抵消工作负载性能。近期，基于强化学习的智能路由在降低数据包延迟的同时实现了更高的网络吞吐量，但其对减少工作负载干扰的有效性尚不明确。本研究通过大规模网络仿真，在龙飞虫系统中对比了智能路由与自适应路由机制下的多工作负载争用现象。我们开发了增强型网络仿真工具包，并构建了具有差异化通信模式的工作负载套件，同时提出两种表征应用通信密集度的度量指标。研究重点通过分析应用层与网络层指标，探究不同路由机制下各类工作负载的相互干扰机制，并从中提炼出若干关键见解。