NSX: Large-Scale Network Simulation on an AI Server
Year: 2025
Type: conference-paper
Abstract: Network innovation is key to supporting AI workloads. Packet-level simulation is indispensable for testing new network features as it enables high-fidelity experimentation. However, today's simulators struggle to scale to large topologies that are typical to AI clusters. To scale the simulation, we have built NSX which is a new simulator that takes advantage of AI servers themselves (e.g., NVIDIA's DGX) to experiment with AI networks. Network simulation has unique workload characteristics that make AI servers an ideal fit: relatively simple, parallelizable compute, with high memory bandwidth pressure. Yet, in order to fully leverage this platform, we need new techniques to rearchitect network simulators for GPU execution. We describe the design decisions that have gone into NSX, and report evaluation results from our current prototype: NSX can scale simulation to networks of 524 k nodes, and it finishes 0.1 ms simulation in less than 2 seconds on a DGX-H100 box. NSX is being used by NVIDIA's networking team on a daily basis for AI cluster design, and new features are added to it on a regular basis.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413922171