Supercharging Packet-level Network Simulation of Large Model Training via Memoization...

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" summary

Wormhole implements a C++ ns-3 kernel that uses network partitioning, state memoization and rate-based steady-state detection to skip repetitive packet events and replay cached unsteady transitions via a Flow Contention Graph. It reports 227–745× speedups for GPT workloads (510× for MoE), >1,000× peak when paired with Unison, average FCT error <1%, steady-state skip >99% of events, and database storage below megabytes, demonstrating large practical acceleration with bounded error.

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" chapters (11)

1. 0:00Wormhole introduction
2. 3:30Limitations of simulations
3. 4:35Steady states and flow patterns
4. 6:43Wormhole architecture
5. 8:11Simulation memoization
6. 9:36Steady state fast-forwarding
7. 11:21Wormhole acceleration example
8. 13:10Implementation details
9. 13:30Evaluation results
10. 15:36Summary and future work
11. 16:18Q&A

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