Summary of Towards a Flexible and High-fidelity Approach to Distributed Dnn Training Emulation, by Banruo Liu et al.
Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation
by Banruo Liu, Mubarak Adetunji Ojewale, Yuhan Ding, Marco Canini
First submitted to arxiv on: 5 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary NeuronaBox is a novel approach to simulate Deep Neural Network (DNN) training workloads. By executing the training process on a subset of real nodes and replicating the networked execution environment, NeuronaBox aims to provide accurate performance observation with an error margin of less than 1%. The approach shows promise in accurately emulating actual systems, as demonstrated by initial proof-of-concept results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuronaBox is a way to simulate how deep learning models train. Right now, training these models can be hard because it requires lots of computers and special connections between them. NeuronaBox makes it easier by letting you use just some of the real computers and then simulating what would happen if all the computers were working together. So far, this idea has shown that it can get very close to how things really work. |
Keywords
» Artificial intelligence » Deep learning » Neural network
