Application-aware memory system for fair and efficient execution of concurrent GPGPU applications

Adwait Jog, Evgeny Bolotin, Zvika Guz, Mike Parker, Stephen W. Keckler, Mahmut T. Kandemir, Chita R. Das

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

12 Scopus citations

Abstract

The available computing resources in modern GPUs are growing with each new generation. However, as many general purpose applications with limited thread-scalability are tuned to take advantage of GPUs, available compute re- sources might not be optimally utilized. To address this, modern GPUs will need to execute multiple kernels simultaneously. As current generations of GPUs (e.g., NVIDIA Kepler, AMD Radeon) already enable concurrent execution of kernels from the same application, in this paper we ad- dress the next logical step: executing multiple concurrent applications in GPUs. We show that while this paradigm has a potential to improve the overall system performance, negative interactions among concurrently executing applications in the memory system can severely hamper the performance and fairness among applications. We show that the current application agnostic GPU memory system design can (1) lead to sub-optimal GPU performance; and (2) create significant imbalance in performance slowdowns across kernels. Thus, we argue that GPU memory system should be augmented with application awareness. As one example to the applicability of this concept, we augment the memory system hardware with application awareness such that requests from different applications can be scheduled in a round robin (RR) fashion while still preserving the benefits of the current first-ready FCFS (FR-FCFS) memory scheduling policy. Evaluations with different multi-application work- loads demonstrate that the proposed memory scheduling policy, first-ready round-robin FCFS (FR-RR-FCFS), improves fairness and delivers better system performance compared to the existing FR-FCFS memory scheduling scheme.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th Workshop on General Purpose Processing Using Graphics Processing Units, GPGPU 2014
PublisherAssociation for Computing Machinery
Pages1-8
Number of pages8
ISBN (Print)9781450327664
DOIs
StatePublished - Jan 1 2014
Event7th Workshop on General Purpose Processing Using Graphics Processing Units, GPGPU 2014 - Salt Lake City, UT, United States
Duration: Mar 1 2014Mar 1 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other7th Workshop on General Purpose Processing Using Graphics Processing Units, GPGPU 2014
CountryUnited States
CitySalt Lake City, UT
Period3/1/143/1/14

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All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Jog, A., Bolotin, E., Guz, Z., Parker, M., Keckler, S. W., Kandemir, M. T., & Das, C. R. (2014). Application-aware memory system for fair and efficient execution of concurrent GPGPU applications. In Proceedings of the 7th Workshop on General Purpose Processing Using Graphics Processing Units, GPGPU 2014 (pp. 1-8). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2576779.2576780