FairGV: Fair and Fast GPU Virtualizationopen access
- Authors
- Hong, Cheol-Ho; Spence, Ivor; Nikolopoulos, Dimitrios S.
- Issue Date
- Dec-2017
- Publisher
- IEEE COMPUTER SOC
- Keywords
- GPU virtualization; trap-less architecture; fair queuing; coscheduling and hybrid scheduling strategies
- Citation
- IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, v.28, no.12, pp 3472 - 3485
- Pages
- 14
- Journal Title
- IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
- Volume
- 28
- Number
- 12
- Start Page
- 3472
- End Page
- 3485
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67996
- DOI
- 10.1109/TPDS.2017.2717908
- ISSN
- 1045-9219
1558-2183
- Abstract
- Increasingly high performance computing (HPC) application developers are opting to use cloud resources due to higher availability. Virtualized GPUs would be an obvious and attractive option for HPC application developers using cloud hosting services. Unfortunately, existing GPU virtualization software is not ready to address fairness, utilization, and performance limitations associated with consolidating mixed HPC workloads. This paper presents FairGV, a radically redesigned GPU virtualization system that achieves system-wide weighted fair sharing and strong performance isolation in mixed workloads that use GPUs with variable degrees of intensity. To achieve its objectives, FairGV introduces a trap-less GPU processing architecture, a new fair queuing method integrated with work-conserving and GPU-centric coscheduling polices, and a collaborative scheduling method for non-preemptive GPUs. Our prototype implementation achieves near ideal fairness (>= 0.97 Min-Max Ratio) with little performance degradation (<= 1.02 aggregated overhead) in a range of mixed HPC workloads that leverage GPUs.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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