Summary of Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-wise Updates, by Cabrel Teguemne Fokam et al.
Asynchronous Stochastic Gradient Descent with Decoupled Backpropagation and Layer-Wise Updates
by Cabrel Teguemne Fokam, Khaleelulla Khan Nazeer, Lukas König, David Kappel, Anand Subramoney
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Partial Decoupled ASGD (PD-ASGD) addresses issues in distributed deep learning training by decoupling forward and backward passes, allowing for a higher ratio of forward to backward threads. This approach reduces parameter staleness, improves robustness to delays, and increases model flops utilization. PD-ASGD achieves close-to-state-of-the-art results while running up to 5.95x faster than synchronous data parallelism and 2.14x times faster than comparable ASGD algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are getting bigger, which means they need to be trained on many devices at once. But right now, training these models is slow because it takes a long time for all the devices to talk to each other. Some methods can make things go faster, but they’re not very good at handling delays or differences in how fast different devices are processing information. That’s why researchers came up with an idea called Partial Decoupled ASGD (PD-ASGD). PD-ASGD makes separate threads for the forward and backward parts of the training process, so it can work more efficiently. It also updates models in chunks across multiple threads, which helps keep everything running smoothly even when there are delays. This new approach is really fast – up to 5.95 times faster than before! |
Keywords
* Artificial intelligence * Deep learning
