# Study of Workload Interference with Intelligent Routing on Dragonfly

arXiv: https://arxiv.org/abs/2403.16288 · HTML: https://mlsanity.com/paper/2403.16288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2024.

## Built on by (most cited first, 2 of 2)

- [Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach](https://mlsanity.com/paper/2409.16429/) (2024)
- [A Unified Structured Query Understanding Framework for Industrial Semantic Search](https://mlsanity.com/paper/2605.27441/) (2026)

## Abstract (arXiv)

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.