Summary of Echo: Simulating Distributed Training at Scale, by Yicheng Feng et al.
Echo: Simulating Distributed Training At Scale
by Yicheng Feng, Yuetao Chen, Kaiwen Chen, Jingzong Li, Tianyuan Wu, Peng Cheng, Chuan Wu, Wei Wang, Tsung-Yi Ho, Hong Xu
First submitted to arxiv on: 17 Dec 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 The paper introduces Echo, a simulation system designed to tackle three key challenges in large-scale training simulations. These challenges include tracing runtime workloads, estimating collective communication without high overheads, and accounting for interference-induced computation slowdown. Echo aims to improve the accuracy of training step estimation by reducing errors. Specifically, it achieves an average error of 8% compared to state-of-the-art simulators, making it suitable for large-scale distributed training jobs on massive machine learning clusters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Echo is a new simulation system that helps manage big machine learning projects. It solves three tricky problems: understanding what happens during each part of the training process, figuring out how communication between devices affects the whole system, and accounting for when different parts of the process slow down due to overlap. Echo is better than other simulators because it’s more accurate – it makes mistakes only 8% of the time. |
Keywords
» Artificial intelligence » Machine learning
