Application-Specific Autonomic Cache Tuning for General Purpose GPUs

Sam Gianelli, Edward Richter, DIego Jimenez, Hugo Valdez, Tosiron Adegbija, Ali Akoglu

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

3 Scopus citations

Abstract

Cache tuning has been widely studied in CPUs, and shown to achieve substantial energy savings, with minimal performance degradations. However, cache tuning has yet to be explored in General Purpose Graphics Processing Units (GPGPU), which have emerged as efficient alternatives for general purpose high-performance computing. In this paper, we explore autonomic cache tuning for GPGPUs, where the cache configurations (cache size, line size, and associativity) can be dynamically specialized/tuned to the executing applications' resource requirements. We investigate cache tuning for both the level one (L1) and level two (L2) caches to derive insights into which cache level offers maximum optimization benefits. To illustrate the optimization potentials of autonomic cache tuning in GPGPUs, we implement a tuning heuristic that can dynamically determine each application's best L1 data cache configurations during runtime. Our results show that application-specific autonomic L1 data cache tuning can reduce the average energy delay product (EDP) and improve the performance by 16.5% and 18.8%, respectively, as compared to a static cache.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages104-113
Number of pages10
ISBN (Electronic)9781538619391
DOIs
StatePublished - Oct 9 2017
Event4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017 - Tucson, United States
Duration: Sep 18 2017Sep 22 2017

Other

Other4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
CountryUnited States
CityTucson
Period9/18/179/22/17

Keywords

  • adaptable hardware
  • cache memories
  • cache tuning
  • configurable caches
  • GPGPU
  • GPU cache management
  • Graphics processing unit
  • high performance computing
  • low-power design
  • low-power embedded systems

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Optimization

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  • Cite this

    Gianelli, S., Richter, E., Jimenez, DI., Valdez, H., Adegbija, T., & Akoglu, A. (2017). Application-Specific Autonomic Cache Tuning for General Purpose GPUs. In Proceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017 (pp. 104-113). [8064058] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAC.2017.17