GPU Virtualization and Scheduling Methods: A Comprehensive Survey

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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.


    • GPU Virtualization and Scheduling Methods

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      Accepted author manuscript, 354 KB, PDF-document


    Original languageEnglish
    Article number35
    Number of pages37
    JournalACM Computing Surveys
    Journal publication date29 Jun 2017
    Issue number3
    Publication statusPublished - 29 Jun 2017

    ID: 127562496