GPUnion: Autonomous GPU Sharing on Campus
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917063636811776 |
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| author | Li, Yufang Zhang, Yuanbo Liao, Hanlong Guo, Deke Tang, Guoming |
| author_facet | Li, Yufang Zhang, Yuanbo Liao, Hanlong Guo, Deke Tang, Guoming |
| contents | A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full provider autonomy. GPUnion incorporates three core mechanisms: i) container-based task dispatching and execution, ii) resource provider-first architecture, and iii) resilient execution featuring automatic check-pointing and migration. Case studies across multiple campus scenarios demonstrate 30% more GPU utilization improvement, 40% increase in interactive sessions, and 94% successful workload migration during provider departures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPUnion: Autonomous GPU Sharing on Campus Li, Yufang Zhang, Yuanbo Liao, Hanlong Guo, Deke Tang, Guoming Distributed, Parallel, and Cluster Computing C.2.4; C.2.1 A pronounced imbalance in GPU resources exists on campus, where some laboratories own underutilized servers while others lack the compute needed for AI research. GPU sharing can alleviate this disparity, while existing platforms typically rely on centralized oversight and persistent allocation models, conflicting with the voluntary and autonomous nature of academic resource ownership. We present GPUnion, a campus-scale GPU sharing platform enabling voluntary participation while preserving full provider autonomy. GPUnion incorporates three core mechanisms: i) container-based task dispatching and execution, ii) resource provider-first architecture, and iii) resilient execution featuring automatic check-pointing and migration. Case studies across multiple campus scenarios demonstrate 30% more GPU utilization improvement, 40% increase in interactive sessions, and 94% successful workload migration during provider departures. |
| title | GPUnion: Autonomous GPU Sharing on Campus |
| topic | Distributed, Parallel, and Cluster Computing C.2.4; C.2.1 |
| url | https://arxiv.org/abs/2507.18928 |