Summary of Scattered Mixture-of-experts Implementation, by Shawn Tan et al.
Scattered Mixture-of-Experts Implementation
by Shawn Tan, Yikang Shen, Rameswar Panda, Aaron Courville
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel Sparse Mixture-of-Experts (SMoE) implementation, ScatterMoE, is presented for GPU-based inference and training. By optimizing memory usage and reducing unnecessary copies, ScatterMoE improves performance speed while maintaining efficient memory management. This breakthrough is made possible by the ParallelLinear component, which enables the development of more complex SMoE models. Benchmarking against Megablocks shows significant gains in throughput and reduced memory footprint. Furthermore, ScatterMoE’s extension capabilities are showcased through a Mixture-of-Attention implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ScatterMoE is a new way to make computers faster and use less energy when doing important tasks like recognizing patterns or making predictions. It works by making sure the computer only uses as much memory as it needs, so it can do its job quickly without wasting resources. This helps make complex things like mixtures of attention models possible. In tests, ScatterMoE was faster and used less memory than other similar tools called Megablocks. |
Keywords
* Artificial intelligence * Attention * Inference * Mixture of experts
