一句话：提出 SING——面向校园共享 GPU 集群的全栈管理方案，降低运维成本并提升资源利用率。
随着大模型快速发展，全球高校大量投入 GPU 集群建设，如何在多用户之间有效共享这些资源成为提升利用率与可及性的关键。然而共享 GPU 集群的管理在系统配置、用户间公平资源分配等方面面临显著挑战，且往往缺乏充足运维人力。本文提出 SING，一个旨在简化共享 GPU 集群管理的全栈解决方案，通过降低维护成本、优化资源利用来提升运营效率。文章系统介绍了 SING 四个可扩展架构层次，逐层剖析其特性，并分享了真实部署中得到的经验，包括用户使用模式与故障（事件）管理策略。为推进共享机器学习集群管理的研究与实践，作者将 SING 的相关资源开源，以支持类似系统的开发与运营。
The rapid advancement of large machine learning (ML) models has driven universities worldwide to invest heavily in GPU clusters. Effectively sharing these resources among multiple users is essential for maximizing both utilization and accessibility. However, managing shared GPU clusters presents significant challenges, ranging from system configuration to fair resource allocation among users. This paper introduces SING, a full-stack solution tailored to simplify shared GPU cluster management. Aimed at addressing the pressing need for efficient resource sharing with limited staffing, SING enhances operational efficiency by reducing maintenance costs and optimizing resource utilization. We provide a comprehensive overview of its four extensible architectural layers, explore the features of each layer, and share insights from real-world deployment, including usage patterns and incident management strategies. As part of our commitment to advancing shared ML cluster management, we open-source SING's resources to support the development and operation of similar systems.
@inproceedings{XuSWRWLW0C25,
title = {Design and Operation of Shared Machine Learning Clusters on Campus},
author = {Kaiqiang Xu and Decang Sun and Hao Wang and Zhenghang Ren and Xinchen Wan and Xudong Liao and Zilong Wang and Junxue Zhang and Kai Chen},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707266}
}