Summary of Hardware Scaling Trends and Diminishing Returns in Large-scale Distributed Training, by Jared Fernandez et al.
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
by Jared Fernandez, Luca Wehrstedt, Leonid Shamis, Mostafa Elhoushi, Kalyan Saladi, Yonatan Bisk, Emma Strubell, Jacob Kahn
First submitted to arxiv on: 20 Nov 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 investigates the crucial factors that impact the efficiency and cost-effectiveness of training extremely large neural networks, such as those used in modern language models. The authors highlight the importance of optimizing hardware configuration and parallelization strategies to train these massive models across thousands of GPUs, distributed across large computing clusters. The study demonstrates that beyond a certain scale, certain communication strategies become preferable, and scaling the total number of accelerators quickly yields diminishing returns even with optimized hardware and parallelization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make big neural networks work better and faster on lots of computers. Right now, these networks are really big and require thousands of special computer chips to train them. The researchers found that if you don’t do things the right way, it gets harder to make them work well. They also discovered that even if you have more and more computer chips, there’s a point where using them all becomes less effective. |
