Summary of Adaptive Consensus Gradients Aggregation For Scaled Distributed Training, by Yoni Choukroun et al.
Adaptive Consensus Gradients Aggregation for Scaled Distributed Training
by Yoni Choukroun, Shlomi Azoulay, Pavel Kisilev
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 research paper investigates distributed machine learning, a crucial approach for training large models on vast datasets. The study focuses on the stochastic optimization problem for deep learning within synchronous parallel computing environments under communication constraints. The authors analyze the distributed gradient aggregation process through the lens of subspace optimization, proposing an efficient weighting scheme for gradients guided by subspace coefficients. They also introduce subspace momentum to accelerate convergence while maintaining statistical unbiasedness in the aggregation. Experimental results demonstrate improved performance over the widely used gradient averaging method on multiple MLPerf tasks, with excellent efficiency in both communicational and computational complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how we can train big models using lots of data by breaking it down into smaller pieces that can be processed together. Right now, people usually average the information from each piece to get a better picture. But is this really the best way? The researchers in this study looked at the problem from a new angle and came up with a more efficient way to combine the information. This helps their method work faster and more accurately than the old way on some important tasks. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Optimization
