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Sharing a Fabric with Collective Communication: Two Storage Penalties in Deep Learning Training
Distributed DL training on HPC systems often shares one network fabric between NCCL/RCCL collective communication and parallel-filesystem I/O. Using a real GNN training workload on a Slingshot-11 system, we show that this sharing imposes two distinct costs. The primary cost is heavy-tailed DataLoade
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