Summary of Monta: Accelerating Mixture-of-experts Training with Network-traffc-aware Parallel Optimization, by Jingming Guo et al.
MoNTA: Accelerating Mixture-of-Experts Training with Network-Traffc-Aware Parallel Optimization
by Jingming Guo, Yan Liu, Yu Meng, Zhiwei Tao, Banglan Liu, Gang Chen, Xiang Li
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 Mixture of Experts (MoE) model combines multiple expert models to scale performance without increasing computational costs. However, current frameworks don’t optimize communication for large models. This paper proposes a parallel optimization method that selects the optimal strategy based on network topologies and communication volume. It achieves an 8x increase in AllToAll communication performance under 8-card tensor parallelism and a 13% latency performance improvement for training a 2x70B model using 16 A800 cards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models work better together. Right now, big models can be slow to train because they need to share information with each other. The authors came up with a new way to make this process faster and more efficient. They call it a “network-traffic-aware parallel optimization method.” It helps the model decide how to share its information in the most effective way possible. |
Keywords
» Artificial intelligence » Machine learning » Mixture of experts » Optimization
