GPU Virtualization and Scheduling Methods: A Comprehensive Survey

Cheol-Ho Hong, Ivor Spence, Dimitrios Nikolopoulos

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)
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The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high-performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey article, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.
Original languageEnglish
Article number35
Number of pages37
JournalACM Computing Surveys
Issue number3
Publication statusPublished - 29 Jun 2017


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