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Brief Announcement: Energy Optimization of Memory Intensive Parallel Workloads

Published:11 July 2016Publication History

ABSTRACT

Energy consumption is an important concern in modern multicore processors. The energy consumed during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the energy consumed by the CPU chip on memory accesses in addition to the energy consumed on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. We present an analytical framework around our energy-performance model to predict the operating frequencies for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses.

References

  1. A. Benoit, P. Renaud-Goud, and Y. Robert. Models and complexity results for performance and energy optimization of concurrent streaming applications. International Journal of High Performance Computing Applications, 25(3):261--273, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Cho and R. Melhem. On the interplay of parallelization, program performance, and energy consumption. Parallel and Distributed Systems, IEEE Transactions on, 21(3):342--353, March 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. L. H. Marco E.T. Gerards and J. Kuper. On the interplay between global dvfs and scheduling tasks with precedence constraints. IEEE TRANSACTIONS ON COMPUTERS, 64(06), 2015.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Conferences
      SPAA '16: Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures
      July 2016
      492 pages
      ISBN:9781450342100
      DOI:10.1145/2935764

      Copyright © 2016 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 July 2016

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