AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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MoSim is introduced, a GPU-cluster simulator that models DT job execution under dynamic network contention, which reduces simulation error for average JCT by up to 3.28%, and reduces input construction overhead by 44.6% by avoiding real-GPU profiling.
Xronos is proposed, a collaborative fine-tuning framework for heterogeneous CPU edge devices that uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck.
PARSER is a new residual sparsification method that shifts the compression objective from minimizing isolated matrix errors to preserving the expert output error, and achieves this by introducing output importance, which measures the actual contribution to the expert output error.
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