Summary of Megascale: Scaling Large Language Model Training to More Than 10,000 Gpus, by Ziheng Jiang et al.
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
by Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang, Yangrui Chen, Zhi Zhang, Yanghua Peng, Xiang Li, Cong Xie, Shibiao Nong, Yulu Jia, Sun He, Hongmin Chen, Zhihao Bai, Qi Hou, Shipeng Yan, Ding Zhou, Yiyao Sheng, Zhuo Jiang, Haohan Xu, Haoran Wei, Zhang Zhang, Pengfei Nie, Leqi Zou, Sida Zhao, Liang Xiang, Zherui Liu, Zhe Li, Xiaoying Jia, Jianxi Ye, Xin Jin, Xin Liu
First submitted to arxiv on: 23 Feb 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 This paper presents MegaScale, a production system for training large language models (LLMs) at scale, leveraging over 10,000 GPUs. The authors tackle unprecedented challenges in training efficiency and stability, adopting a full-stack approach to co-design algorithmic and system components. They develop diagnosis tools to monitor system events, identify root causes, and achieve fault tolerance. MegaScale achieves 55.2% Model FLOPs Utilization (MFU) when training a 175B LLM model on 12,288 GPUs, improving MFU by 1.34x compared to Megatron-LM. The authors share operational experience in identifying and fixing failures and stragglers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Training huge language models is a big deal! Researchers built MegaScale, a special system that helps train these massive models using many computers (over 10,000!). This was super hard because the computers got very slow and unstable. To fix this, they designed a whole new way of making the computers work together smoothly. They also made special tools to find out what went wrong when something didn’t work. With MegaScale, they were able to train one big model really well and even share how they fixed some problems that happened along the way. |
