Characterization of Real Communication Patterns and Congestion Dynamics in HPC Interconnection Networks

2026-04-17

AI Summary

This study addresses the challenge of accurately modeling congestion in high-performance computing interconnection networks, which is often triggered by specific communication patterns and hinders performance optimization. Building upon the VEF Traces framework, the authors extend its capabilities to directly analyze network congestion from application traces or simulations. For the first time in a real-world supercomputing environment, they identify congestion scenarios induced by collective communication operations in several representative applications—including NEST, GROMACS, LAMMPS, and PATMOS. The proposed methodology enables comprehensive characterization, modeling, and simulation of these communication patterns, offering empirical insights and novel tool support for the design and optimization of interconnection networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Applications
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Computational social science

Abstract

The interconnection network is a key component of Supercomputers and Data centers, and its design must cope with the increasing communication demands of current applications and services; otherwise, it may become a system bottleneck. The most challenging network design issues are the topology, routing algorithm, flow control, and power efficiency. However, even the most efficient interconnection networks may suffer severe performance degradation due to congestion, especially under specific network traffic patterns generated by communication operations in high-performance computing~(HPC), deep learning training, or online data-intensive services. In this context, characterizing and modeling these communication operations and the network traffic patterns they generate is a fundamental challenge for studying their impact on network performance. This paper presents a methodology, based primarily on the VEF Traces framework, to characterize, model, and simulate the communication patterns of representative computing- and data-intensive applications. More precisely, we have extended the VEF traces framework with tools that enable us to characterize network congestion, either directly from VEF traces or via simulations. We have analyzed a set of VEF traces obtained from runs of NEST, GROMACS, LAMMPS, and PATMOS on several Supercomputers. In these studies, we identify potential congestion scenarios that arise in realistic network configurations when certain collective operations are performed.

Problem

• communication patterns
• congestion dynamics
• HPC interconnection networks
• network traffic characterization
• collective operations

Innovation

• VEF Traces
• congestion characterization
• communication patterns
• HPC interconnection networks
• network simulation