Journal of Lightwave Technology / 2021

X-NEST: A Scalable, Flexible, and High-Performance Network Architecture for Distributed Machine Learning

Yunfeng Lu, Huaxi Gu, Xiaoshan Yu, Peng Li

Foundation ModelsLarge Language ModelsML SystemsPopular and Landmark Papers

In a large-scale distributed machine learning system, the interconnection network between computing devices has an important impact on performance in the training of neural network models. The current expansion of training data and model size has led to a rapid increase in the number of computing devices used in distributed machine learning systems, which places higher demands on network scalability. In addition, the synchronization algorithms used for data exchange between computing devices have different communication topologies, and traditional electrical networks have difficulty matching them due to their fixed network topology. Neural network models and model partitioning methods can also affect the amount of communication between devices, but the overprovisioned bandwidth of traditional electric networks incurs unnecessary costs. To address these issues, we propose a scalable, flexible, and high-performance network architecture called X-NEST. The flexibility of optical switching devices allows X-NEST to dynamically change its topology and the number of links between devices according to traffic pattern variations, thereby improving network performance and resource utilization. Although changes in the connection relationships between devices depend on the controller, the simple and flexible control plane of X-NEST can quickly respond to network communication requirements. Extensive analytical simulations using different traffic patterns demonstrate that X-NEST copes well with the communication characteristics of various synchronization algorithms.

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Title: X-NEST: A Scalable, Flexible, and High-Performance Network Architecture for Distributed Machine Learning
Authors: Yunfeng Lu, Huaxi Gu, Xiaoshan Yu, Peng Li
Year: 2021
Venue: Journal of Lightwave Technology
Categories: Foundation Models, Large Language Models, ML Systems, Popular and Landmark Papers
Citations: 16
Paper URL: https://www.semanticscholar.org/paper/1a8331c55ededb7cea8e7620bd488d8689eeb209
Open PDF: Not available

Abstract:
In a large-scale distributed machine learning system, the interconnection network between computing devices has an important impact on performance in the training of neural network models. The current expansion of training data and model size has led to a rapid increase in the number of computing devices used in distributed machine learning systems, which places higher demands on network scalability. In addition, the synchronization algorithms used for data exchange between computing devices have different communication topologies, and traditional electrical networks have difficulty matching them due to their fixed network topology. Neural network models and model partitioning methods can also affect the amount of communication between devices, but the overprovisioned bandwidth of traditional electric networks incurs unnecessary costs. To address these issues, we propose a scalable, flexible, and high-performance network architecture called X-NEST. The flexibility of optical switching devices allows X-NEST to dynamically change its topology and the number of links between devices according to traffic pattern variations, thereby improving network performance and resource utilization. Although changes in the connection relationships between devices depend on the controller, the simple and flexible control plane of X-NEST can quickly respond to network communication requirements. Extensive analytical simulations using different traffic patterns demonstrate that X-NEST copes well with the communication characteristics of various synchronization algorithms.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=X-NEST%3A%20A%20Scalable%2C%20Flexible%2C%20and%20High-Performance%20Network%20Architecture%20for%20Distributed%20Machine%20Learning)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=X-NEST%3A%20A%20Scalable%2C%20Flexible%2C%20and%20High-Performance%20Network%20Architecture%20for%20Distributed%20Machine%20Learning)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/1a8331c55ededb7cea8e7620bd488d8689eeb209)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=X-NEST%3A%20A%20Scalable%2C%20Flexible%2C%20and%20High-Performance%20Network%20Architecture%20for%20Distributed%20Machine%20Learning+paper+explained)