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arXiv · 2408.13356

Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI

Mikhail Khalilov·Salvatore Di Girolamo·Marcin Chrapek·Rami Nudelman·Gil Bloch·Torsten Hoefler
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Abstract

In the Fully Sharded Data Parallel (FSDP) training pipeline, collective operations can be interleaved to maximize the communication/computation overlap. In this scenario, outstanding operations such as Allgather and Reduce-Scatter can compete for the injection bandwidth and create pipeline bubbles. To address this problem, we propose a novel bandwidth-optimal Allgather collective algorithm that leverages hardware multicast. We use multicast to build a constant-time reliable Broadcast protocol, a building block for constructing an optimal Allgather schedule. Our Allgather algorithm achieves 2x traffic reduction on a 188-node testbed. To free the host side from running the protocol, we employ SmartNIC offloading. We extract the parallelism in our Allgather algorithm and map it to a SmartNIC specialized for hiding the cost of data movement. We show that our SmartNIC-offloaded collective progress engine can scale to the next generation of 1.6 Tbit/s links.

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Mikhail Khalilov, Salvatore Di Girolamo, Marcin Chrapek, Rami Nudelman, Gil Bloch, Torsten Hoefler. 2024-08-23. Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI. https://arxiv.org/abs/2408.13356

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