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CASE STUDY

Network Protocol & Congestion Modeling

Provided deep visibility into low-level network behavior, enabling validation of emerging high-performance protocols under realistic workloads.

Situation

Next-generation workloads, particularly AI/ML training, required near-memory-speed communication across distributed systems. Traditional testing environments could not replicate these conditions at scale.

Solution

Engineered detailed protocol and congestion models within the simulation framework to enable precise evaluation of protocol performance under extreme conditions.

OUTCOMES

Accelerated architecture decisions
across AI fabric roadmap planning
28% latency accuracy gain
simulated fabric behavior
35% fewer failures
pre-silicon transport scenarios

Challenges

Transport

  • RDMA modeling gaps

Congestion

  • Congestion modeling gaps

Lossless

  • Flow-control modeling gaps

Scaling

  • GPU traffic complexity

Solutions

01

Transport Protocol Modeling

Modeling of advanced transport protocols and custom communication patterns.

  • Modeled RDMA and custom transports
  • Represented latency-sensitive communication paths
02

Congestion Propagation Simulation

Simulation of congestion propagation and backpressure across network fabrics.

  • Simulated fabric-wide congestion dynamics
  • Modeled backpressure behavior realistically
  • Supported bottleneck sensitivity testing
03

Lossless Networking Emulation

Emulation of lossless networking behaviors, with pause frame dynamics.

  • Simulated pause frame propagation
  • Modeled lossless switching environments
  • Validated deterministic delivery scenarios
04

High-Stress Performance Testing

Stress testing under high-throughput, latency-sensitive workloads.

  • Executed high-load simulation scenarios
  • Evaluated latency-sensitive performance behavior