AllReduce Scheduling with Hierarchical Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912296465334272 |
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| author | Wei, Yufan Liu, Mickel Wu, Wenfei |
| author_facet | Wei, Yufan Liu, Mickel Wu, Wenfei |
| contents | AllReduce is a technique in distributed computing which saw use in many critical applications of deep learning. Existing methods of AllReduce scheduling oftentimes lack flexibility due to being topology-specific or relying on extensive handcrafted designs that require domain-specific knowledge. In this work, we aim to alleviate this inflexibility by proposing a deep-reinforcement-learning (DRL)-based pipeline that can generate AllReduce scheduling for various network topologies without topology-specific design features. The flow scheduling module of this pipeline consists of two hierarchically-structured DRL policies that work cooperatively to find optimal scheduling. We showcase the performance of our method compared to the baseline methods on three topologies: BCube, DCell, and Jellyfish. Finally, we contributed a Python-based simulation environment simulating AllReduce scheduling on these network topologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AllReduce Scheduling with Hierarchical Deep Reinforcement Learning Wei, Yufan Liu, Mickel Wu, Wenfei Networking and Internet Architecture Distributed, Parallel, and Cluster Computing AllReduce is a technique in distributed computing which saw use in many critical applications of deep learning. Existing methods of AllReduce scheduling oftentimes lack flexibility due to being topology-specific or relying on extensive handcrafted designs that require domain-specific knowledge. In this work, we aim to alleviate this inflexibility by proposing a deep-reinforcement-learning (DRL)-based pipeline that can generate AllReduce scheduling for various network topologies without topology-specific design features. The flow scheduling module of this pipeline consists of two hierarchically-structured DRL policies that work cooperatively to find optimal scheduling. We showcase the performance of our method compared to the baseline methods on three topologies: BCube, DCell, and Jellyfish. Finally, we contributed a Python-based simulation environment simulating AllReduce scheduling on these network topologies. |
| title | AllReduce Scheduling with Hierarchical Deep Reinforcement Learning |
| topic | Networking and Internet Architecture Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.21013 |