Summary of Hybrid Approach to Parallel Stochastic Gradient Descent, by Aakash Sudhirbhai Vora and Dhrumil Chetankumar Joshi and Aksh Kantibhai Patel
Hybrid Approach to Parallel Stochastic Gradient Descent
by Aakash Sudhirbhai Vora, Dhrumil Chetankumar Joshi, Aksh Kantibhai Patel
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Distributed, Parallel, and Cluster Computing (cs.DC); Neural and Evolutionary Computing (cs.NE)
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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 paper introduces a novel approach to data parallelism for training neural networks using multiple worker nodes. Stochastic Gradient Descent (SGD) is commonly used for large datasets to reduce training time, but traditional synchronous and asynchronous methods have their limitations. The authors propose a hybrid approach that combines the benefits of both methods, leveraging threshold functions to gradually shift parameter aggregation from asynchronous to synchronous. Experimental results demonstrate that this hybrid method outperforms existing approaches in terms of efficiency and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to train neural networks quickly is being developed. Right now, people use something called Stochastic Gradient Descent (SGD) to speed up training time when working with large amounts of data. Another technique used is parallelism, which lets multiple computers work together to train a model. Two main approaches are synchronous and asynchronous. However, both have drawbacks. The idea behind this research is to combine the best parts of each method into one hybrid approach. By choosing the right “threshold function” to switch between these methods, scientists found that their new way works better than previous ones in a given time frame. |
Keywords
» Artificial intelligence » Stochastic gradient descent
