Samples Dispatching Mechanism for Accelerating Recommendation Model Training in Edge Intelligent Computing System

Li Guopeng; Tan Haisheng; Zhang Chi; Ni Hongqiu; Wang Zilong; Zhang Xinyue; Xu Yang; Tian Han; Chen Guoliang

doi:10.7544/issn1000-1239.202550128

Journal of Computer Research and Development > 2025 > Corrected proof > DOI: 10.7544/issn1000-1239.202550128 CSTR: 32373.14.issn1000-1239.202550128

Li Guopeng, Tan Haisheng, Zhang Chi, Ni Hongqiu, Wang Zilong, Zhang Xinyue, Xu Yang, Tian Han, Chen Guoliang. Samples Dispatching Mechanism for Accelerating Recommendation Model Training in Edge Intelligent Computing System[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202550128

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Samples Dispatching Mechanism for Accelerating Recommendation Model Training in Edge Intelligent Computing System

1.
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027
2.
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230027

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This work is supported by the Key Program of the National Natural Science Foundation of China (62132009).

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Author Bio:
Li Guopeng: born in 1997. PhD candidate. His main research interests include edge intelligence, large language model-based agent, and machine learning system

Tan Haisheng: born in 1981. PhD, professor. Member of CCF. His main research interests include edge intelligence, and system and networking for AI

Zhang Chi: born in 1995. PhD, associate professor. His main research interests include edge computing and network algorithms

Ni Hongqiu: born in 2000. PhD candidate. Her main research interests include edge computing, large language model inference, and machine learning system

Wang Zilong: born in 2000. Master candidate. His main research interests include edge computing, scheduling mechanism, and machine learning system

Zhang Xinyue: born in 2000. PhD candidate. Her main research interests include edge computing, serverless computing, and machine learning system

Xu Yang: born in 2003. Master candidate. His main research interests include edge computing, machine learning system， and large language model

Tian Han: born in 1989. PhD, associate professor. His main research interest includes machine learning and its applications in networking, system and private computing

Chen Guoliang: born in 1938. Professor. Fellow of CCF. His main research interests include parallel algorithms, computer architectures, and computational intelligence
Received Date: February 28, 2025
Revised Date: April 06, 2025
Available Online: April 14, 2025

Graphical Abstract

Abstract

Abstract

Using edge workers to train deep learning recommendation model (DLRM) in edge intelligent computing system brings several benefits, particularly in terms of data privacy protection, low latency and personalization. However, due to the huge size of embedding tables, typical DLRM training frameworks adopt one or more parameter servers to maintain global embedding tables, while leveraging several edge workers to cache part of them. This incurs significant transmission cost for embedding transmissions between workers and parameter servers, which can dominate the training cycle. In this paper, we investigate how to dispatch input embedding samples to appropriate edge workers to minimize the total embedding transmission cost when facing edge-specific challenges such as heterogeneous networks and limited resources. We develop ESD, a novel mechanism that optimizes the dispatching of input embedding samples to edge workers based on expected embedding transmission cost. We propose HybridDis as the dispatch decision method within ESD, which combines a resource-intensive optimal algorithm and a heuristic algorithm to balance decision quality and resource consumption. We implement a prototype of ESD using C++ and Python and compare it with state-of-the-art mechanisms on real-world workloads. Extensive experimental results show that ESD reduces the embedding transmission cost by up to 36.76% and achieves up to 1.74 times speedup in end-to-end DLRM training.
- distributed training,
- edge intelligence,
- deep learning,
- recommendation model,
- dispatch mechanism

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References (91)

References

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