Work

MFNetSim: A Multi-Fidelity Network Simulation Framework for Multi-Traffic Modeling of Dragonfly Systems

Year: 2025

Type: article

Abstract: In high-performance computing (HPC), modern supercomputers typically provide exclusive computing resources to user applications. Nevertheless, the interconnect network is a shared resource for both inter-node communication and across-node I/O access, among co-running workloads, leading to inevitable network interference. In this study, we develop MFNetSim, a multi-fidelity modeling framework that enables simulation of multi-traffic simultaneously over the interconnect network, including inter-process communication and I/O traffic. By combining different levels of abstraction, MFNetSim can efficiently co-model the communication and I/O traffic occurring on HPC systems equipped with flash-based storage. We conduct simulation studies of hybrid workloads composed of traditional HPC applications and emerging ML applications on a 1,056-node Dragonfly system with various configurations. Our analysis provides various observations regarding how network interference affects communication and I/O traffic.

Source: ACM Transactions on Modeling and Computer Simulation

DOI: 10.1145/3729424

Authors: Xin Wang, Kevin A. Brown, Robert B. Ross, Christopher D. Carothers, Zhiling Lan

Institutions: University of Illinois Chicago, Argonne National Laboratory, Rensselaer Polytechnic Institute

Language: English

Topic: Interconnection Networks and Systems

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy

Awards: SHF:Small:Intelligent Management of Hybrid Workloads for Extreme Scale Computing, G4140714164, OAC Core: AIMCI: AI-Guided Resource Management for Advanced Cyberinfrastructure