Summary of Optimizing Mixture-of-experts Inference Time Combining Model Deployment and Communication Scheduling, by Jialong Li et al.
Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling
by Jialong Li, Shreyansh Tripathi, Lakshay Rastogi, Yiming Lei, Rui Pan, Yiting Xia
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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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 paper addresses the limitations of Mixture-of-Experts (MoE) models in large-scale machine learning applications. MoE models reduce computational requirements by selectively activating relevant experts, but they are hindered by high communication overhead, low GPU utilization, and complexities arising from heterogeneous GPU environments. To overcome these challenges, the authors propose a novel approach that leverages asynchronous communication and heterogeneity-aware optimization techniques to improve scalability and efficiency. The proposed method is evaluated on various benchmarks and tasks, demonstrating significant improvements in model performance and training time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoE models are a type of machine learning model that helps solve big problems by only using the parts that need to be solved. However, these models have some issues when they get really big, like needing too many computer resources and having trouble communicating with different types of computers. To fix this, researchers came up with a new way to make MoE models work better on different computers and use their resources more efficiently. |
Keywords
* Artificial intelligence * Machine learning * Mixture of experts * Optimization
