survey

GPU Virtualization and Scheduling Methods: A Comprehensive Survey

Authors:

Dimitrios S. NikolopoulosAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 3

Article No.: 35, Pages 1 - 37

https://doi.org/10.1145/3068281

Published: 29 June 2017 Publication History

Abstract

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.

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 3

May 2018

550 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3101309

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Published: 29 June 2017

Accepted: 01 March 2017

Revised: 01 February 2017

Received: 01 October 2016

Published in CSUR Volume 50, Issue 3

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