  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.
