Packet-level network simulation provides high-fidelity modeling but suffers from severe scalability bottlenecks. Existing scaling approaches remain inefficient for modern data-center and AI-training networks. Spatial parallelism requires substantial hardware resources, while temporal-skipping approaches become less effective under bursty traffic. We observe that homogeneous data-center deployments introduce substantial execution redundancy during simulation.