Using DVFS and task scheduling algorithms for a hard real-time heterogeneous multicore processor environment

Sung Il Kim, Hwan Tae Kim, Gyu Seong Kang, Jong Kook Kim

Research output: Contribution to conferencePaper

7 Citations (Scopus)

Abstract

The usage of heterogeneous multicore processors (HMP) are rapidly spreading from data centers for large scale deployment to smart phones for the flexibility to adapt to power constraints and performance needs. In this paper, we show that for an example HMP environment, an intelligent-task scheduler is critical in improving performance and energy efficiency. The environment in this paper assumes that the tasks are independent, have hard real-time constraints, and a multicore systems where processors can be manipulated to change the clock cycle speed and power levels. Tasks are assumed to arrive aperiodically and these tasks are applications from the SPEC CPU 2006 benchmark suite. For evaluation, an actual system composed of two multicore processors which support on-the-fly DVFS is used in this study. One of our energy efficient algorithms achieved 49.9% higher task completion rate than an enhanced version of the nave Linux scheduler while consuming only 45.3% of the energy.

Original languageEnglish
Pages23-30
Number of pages8
DOIs
Publication statusPublished - 2013
Event2013 ACM Workshop on Energy Efficient High Performance Parallel and Distributed Computing, EEHPDC 2013 - New York, NY, United States
Duration: 2013 Jun 172013 Jun 17

Other

Other2013 ACM Workshop on Energy Efficient High Performance Parallel and Distributed Computing, EEHPDC 2013
CountryUnited States
CityNew York, NY
Period13/6/1713/6/17

Keywords

  • DVFS
  • dynamic task scheduling
  • heterogeneous multicore processor
  • power-aware
  • real-time
  • task scheduling algorithm

ASJC Scopus subject areas

  • Software

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  • Cite this

    Kim, S. I., Kim, H. T., Kang, G. S., & Kim, J. K. (2013). Using DVFS and task scheduling algorithms for a hard real-time heterogeneous multicore processor environment. 23-30. Paper presented at 2013 ACM Workshop on Energy Efficient High Performance Parallel and Distributed Computing, EEHPDC 2013, New York, NY, United States. https://doi.org/10.1145/2480347.2480350