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Data Movement-Aware GPU Sharing for Data-Intensive Systems

Summary: Introduces GUST, a GPU scheduler that treats PCIe as a first-class schedulable resource and classifies kernels as transfer- or device-intensive to interleave analytics (PCIe-heavy) and inference (compute-heavy) workloads. Prototype colocating four mixed workloads reduces performance degradation vs dedicated GPUs from 3.9–7x to 2.8x. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
572
Venue
CIDR
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,119 | 30.58%
DOI
-

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BibTeX Citation

@inproceedings{jiang_cidr26,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '26},
        title = {{Data Movement-Aware GPU Sharing for Data-Intensive Systems}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Jiang, Yi and Nicholson, Hamish and Sanca, Viktor and Ailamaki, Anastasia},
        year = {2026}
}

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