Fair share: Allocation of GPU resources for both performance and fairness

Paula Aguilera, Katherine Morrow, Nam Sung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

General-purpose computing on the GPU (GPGPU computing) is becoming widely adopted for an increasing variety of applications. However, it has been shown that as the available computing elements in the GPU increase with every generation some GPGPU applications fail to fully utilize the GPU resources. Spatial multitasking - subdividing GPU resources amongst concurrently-running applicationshas been shown to increase overall system performance and utilization for GPGPU computing. However, dividing the computing resources among multiple applications to maximize system performance often results in one application having 'unfair' access to GPU resources. Yet, evenly dividing resources among applications does not guarantee equal speedups to each application; nor does it take into account overall system performance. In this paper we examine several different ways to characterize 'fairness' for GPGPU spatial multitasking, by balancing individual application's performance and overall system performance. We further present a run-time algorithm to predict and adjust the SM allocation at runtime to meet the desired fairness metric.

Original languageEnglish (US)
Title of host publication2014 32nd IEEE International Conference on Computer Design, ICCD 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages440-447
Number of pages8
ISBN (Electronic)9781479964925
DOIs
StatePublished - Dec 3 2014
Externally publishedYes
Event32nd IEEE International Conference on Computer Design, ICCD 2014 - Seoul, Korea, Republic of
Duration: Oct 19 2014Oct 22 2014

Publication series

Name2014 32nd IEEE International Conference on Computer Design, ICCD 2014

Other

Other32nd IEEE International Conference on Computer Design, ICCD 2014
CountryKorea, Republic of
CitySeoul
Period10/19/1410/22/14

Keywords

  • GPGPU computing
  • fairness
  • resource allocation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Fair share: Allocation of GPU resources for both performance and fairness'. Together they form a unique fingerprint.

Cite this