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)
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Authors
- 1. Yi Jiang (EPFL)
- 2. Hamish Nicholson (EPFL)
- 3. Viktor Sanca (EPFL; Oracle)
- 4. Anastasia Ailamaki (EPFL)
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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