ESUN-Bench: A Workload-Aware Benchmarking Framework for Ethernet-Based Scale-Up AI Fabrics

Ethernet fabric metrics do not by themselves determine AI execution performance. The same communication delay can be hidden by concurrent computation or exposed at a synchronization boundary. Existing machine learning network co-simulators provide foundations for coupling communication with application execution. However, Ethernet scale-up benchmarking additionally requires mechanism-specific modeling and observability to interpret these interactions. We present ESUN-Bench, a benchmarking framework that connects packet-level simulation with communication operation timing and an iteration directed acyclic graph. In addition, we formalize the interfaces required for this mapping, including packet-to-operation association, communication completion semantics, and dependency-aware timing propagation. Moreover, we define exposed iteration overhead as the modeled iteration-time difference between matched network configurations, retaining workload dependencies that aggregate packet metrics discard. Evaluation across 102 benchmark cases investigates what communication timing reveals about iteration overhead, which workload conditions shape that timing, and how network-mechanism gains translate into application benefits. The results show that preserving communication semantics and execution dependencies improves iteration-overhead estimation over aggregate network metrics. They further identify message granularity, congestion pressure, and spatial contention as workload factors that shape communication service. Mechanism comparisons demonstrate that activation, communication improvement, and iteration benefit are distinct outcomes: the application value of a network optimization depends on the completion times it changes and the execution dependencies those changes affect. As a lightweight simulation framework, ESUN-Bench fills the gap in existing Ethernet-oriented evaluation methodologies and encourages more researchers to engage in ESUN-related studies.