Summary of Pase: Parallelization Strategies For Efficient Dnn Training, by Venmugil Elango
PaSE: Parallelization Strategies for Efficient DNN Training
by Venmugil Elango
First submitted to arxiv on: 4 Jul 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 addresses the challenge of parallelizing deep neural networks (DNNs) on multiple devices to reduce training time. The authors highlight that existing methods rely on data parallelism, which can be sub-optimal and memory-intensive. They also note that expert-designed strategies are often specific to particular DNN architectures and lack generalizability. To overcome these limitations, the paper proposes a novel approach to automate the process of finding optimal parallelization strategies for diverse DNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps researchers find ways to train deep neural networks faster by using many devices together. Right now, people usually use data parallelism, but it’s not always the best choice and can be slow or require a lot of memory. The authors want to develop a system that can automatically find the best way to train different types of DNNs. |
