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arXiv · 2405.02969

Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation

Banruo Liu·Mubarak Adetunji Ojewale·Yuhan Ding·Marco Canini
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Abstract

We propose NeuronaBox, a flexible, user-friendly, and high-fidelity approach to emulate DNN training workloads. We argue that to accurately observe performance, it is possible to execute the training workload on a subset of real nodes and emulate the networked execution environment along with the collective communication operations. Initial results from a proof-of-concept implementation show that NeuronaBox replicates the behavior of actual systems with high accuracy, with an error margin of less than 1% between the emulated measurements and the real system.

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Banruo Liu, Mubarak Adetunji Ojewale, Yuhan Ding, Marco Canini. 2024-05-05. Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation. https://arxiv.org/abs/2405.02969

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