Summary of Cyclic Data Parallelism For Efficient Parallelism Of Deep Neural Networks, by Louis Fournier (mlia) et al.
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks
by Louis Fournier, Edouard Oyallon
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)
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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 proposed Cyclic Data Parallelism shifts the execution of micro-batches from simultaneous to sequential, allowing for constant total memory usage and balanced gradient communications during training. This paradigm combines with Model Parallelism to reduce GPU requirements by sharing GPUs across micro-batches. Within the ZeRO-DP framework, point-to-point operations replace collective broadcasts, demonstrating strength on CIFAR-10 and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cyclic Data Parallelism is a new way of doing things that helps large deep learning models work faster and more efficiently. It does this by processing small chunks of data one after the other instead of all at once. This makes it possible to use less memory and reduces the amount of communication needed during training. By combining this with Model Parallelism, we can even reduce the number of powerful GPUs required. The approach shows promise on popular image datasets like CIFAR-10 and ImageNet. |
Keywords
* Artificial intelligence * Deep learning
