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With the rapid advancement of Artificial Intelligence (AI) and High-Performance Computing (HPC), GPU-centric clusters have become pivotal in driving research across diverse disciplines. Consequently, campus data centers are increasingly aggregating substantial computing resources. However, akin to commercial GPU and AI clusters, campus GPU clusters frequently encounter challenges related to GPU underutilization. While pooling technologies offer a viable solution to this issue, existing GPU pooling approaches fall short in effectively supporting environments where multiple applications coexist within campus data centers. This paper presents gPooling, a novel pooling scheme that leverages device driver hijacking to optimize GPU resource allocation. We designed a benchmark based on real-world traces from a campus data center and deployed gPooling within a GPU cluster environment. Experimental results from both benchmarking and actual deployment demonstrate that gPooling significantly enhances GPU utilization and reduces user waiting times, thereby improving the overall efficiency of campus GPU clusters.

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Design and Operation of Elastic GPU-Pooling on Campus

  • 会议论文

  • 发表日期：

    2026年

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  • Kaicheng Guo,
  • Jingyi Chen,
  • Yun Wang,
  • Semakin Anton,
  • Tovmachenko Dmitry,
  • Jiajie Sheng,
  • Jianwen Wei,
  • James Lin,
  • Zhengwei Qi,
  • Haibing Guan

摘要

With the rapid advancement of Artificial Intelligence (AI) and High-Performance Computing (HPC), GPU-centric clusters have become pivotal in driving research across diverse disciplines. Consequently, campus data centers are increasingly aggregating substantial computing resources. However, akin to commercial GPU and AI clusters, campus GPU clusters frequently encounter challenges related to GPU underutilization. While pooling technologies offer a viable solution to this issue, existing GPU pooling approaches fall short in effectively supporting environments where multiple applications coexist within campus data centers. This paper presents gPooling, a novel pooling scheme that leverages device driver hijacking to optimize GPU resource allocation. We designed a benchmark based on real-world traces from a campus data center and deployed gPooling within a GPU cluster environment. Experimental results from both benchmarking and actual deployment demonstrate that gPooling significantly enhances GPU utilization and reduces user waiting times, thereby improving the overall efficiency of campus GPU clusters.