Summary of Shortcut-connected Expert Parallelism For Accelerating Mixture-of-experts, by Weilin Cai et al.
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
by Weilin Cai, Juyong Jiang, Le Qin, Junwei Cui, Sunghun Kim, Jiayi Huang
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 proposed shortcut-connected MoE (ScMoE) architecture tackles the efficiency limitations of large-scale MoE models by decoupling communication from computation. By introducing an overlapping parallel strategy, ScMoE reduces training and inference times compared to traditional top-2 MoE architectures. The ScMoE model achieves speed improvements of 30% and 11%, as well as inference improvements of 40% and 15%, in distributed environments using PCIe and NVLink hardware, respectively. Additionally, the paper presents an expert offloading strategy for memory-limited inference, optimizing latency through expert migration overlap. Experimental results demonstrate comparable or even superior model quality compared to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale MoE models are designed to process complex tasks efficiently, but they have limitations when it comes to processing power and communication between devices. To address this issue, scientists have developed a new architecture called ScMoE, which helps to distribute the workload more effectively across multiple computing devices. This means that ScMoE can process large amounts of data faster than traditional MoE models. The researchers also introduced a strategy to offload experts from memory-limited inference tasks, reducing latency and making it easier to make predictions. |
Keywords
* Artificial intelligence * Inference
