Beyond the Node Barrier: Zero-Shot Strategy Planning for LLM Training on Super-Nodes

Emerging super-nodes tightly couple multiple servers with symmetric high-bandwidth fabrics, substantially weakening the legacy bandwidth cliff between nodes. This makes cross-boundary TP/EP a viable part of the strategy space, shifting the optimal strategy basin away from the legacy practice of keeping TP/EP within a node. Yet adapting the parallel strategy to new platforms still requires expensive profiling and brittle manual tuning. We propose Symbolic Barrier-aware Planner (SBP), a profiling-free planner that selects DP/MP/PP factorization and activation checkpoint strength by computing a barrier-aware symbolic score from model shapes, collective semantics, and hardware specifications. On legacy clusters, SBP recovers measured-best configurations; on super-nodes, it captures the regime shift and achieves $1.40\times$ step-time speedup and a 10.62 percentage-point MFU improvement over the best node-local Megatron-style baseline on 128-die training. SBP explores the strategy space in seconds on a single CPU, without accelerator profiling for strategy selection.