Minimizing energy consumption for frame-based tasks on heterogeneous multiprocessor platforms

Dawei Li, Jie Wu

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)

Abstract

Heterogeneous multiprocessors have been widely used in modern computational systems to increase the computing capability. As the performance increases, the energy consumption in these systems also increases significantly. Dynamic Voltage and Frequency Scaling (DVFS) is considered an efficient scheme to achieve the goal of saving energy, because it allows processors to dynamically adjust their supply voltages and/or execution frequencies to work on different power/energy levels. In this paper, we consider scheduling non-preemptive frame-based tasks on DVFS-enabled heterogeneous multiprocessor platforms with the goal of achieving minimal overall energy consumption. We consider three types of heterogeneous platforms, namely, dependent platforms without runtime adjusting, dependent platforms with runtime adjusting, and independent platforms. For these three platforms, we first formulate the problems as binary integer programming problems, and then, relax them as convex optimization problems, which can be solved by the well-known interior point method. We propose a Relaxation-based Iterative Rounding Algorithm (RIRA), which tries to achieve the task set partition, that is closest to the optimal solution of the relaxed problems, in every step of a task-to-processor assignment. Experiments and comparisons show that our RIRA produces a better performance than existing methods and a simple but naive method, and achieves near-optimal scheduling under most cases. We also provide comprehensive complexity, accuracy and scalability analysis for the RIRA approach by investigating the interior-point method and by running specially designed experiments. Experimental results also show that the proposed RIRA approach is an efficient and practically applicable scheme with reasonable complexity.

Original languageEnglish
Article number6777565
Pages (from-to)810-823
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume26
Issue number3
DOIs
StatePublished - 1 Mar 2015

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Energy utilization
Scheduling
Convex optimization
Integer programming
Electron energy levels
Scalability
Energy conservation
Experiments
Electric potential
Voltage scaling
Dynamic frequency scaling

Keywords

  • Dynamic voltage and frequency scaling (DVFS)
  • Energy-aware scheduling
  • Heterogeneous multiprocessor platforms
  • Iteration-based task partitioning

Cite this

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abstract = "Heterogeneous multiprocessors have been widely used in modern computational systems to increase the computing capability. As the performance increases, the energy consumption in these systems also increases significantly. Dynamic Voltage and Frequency Scaling (DVFS) is considered an efficient scheme to achieve the goal of saving energy, because it allows processors to dynamically adjust their supply voltages and/or execution frequencies to work on different power/energy levels. In this paper, we consider scheduling non-preemptive frame-based tasks on DVFS-enabled heterogeneous multiprocessor platforms with the goal of achieving minimal overall energy consumption. We consider three types of heterogeneous platforms, namely, dependent platforms without runtime adjusting, dependent platforms with runtime adjusting, and independent platforms. For these three platforms, we first formulate the problems as binary integer programming problems, and then, relax them as convex optimization problems, which can be solved by the well-known interior point method. We propose a Relaxation-based Iterative Rounding Algorithm (RIRA), which tries to achieve the task set partition, that is closest to the optimal solution of the relaxed problems, in every step of a task-to-processor assignment. Experiments and comparisons show that our RIRA produces a better performance than existing methods and a simple but naive method, and achieves near-optimal scheduling under most cases. We also provide comprehensive complexity, accuracy and scalability analysis for the RIRA approach by investigating the interior-point method and by running specially designed experiments. Experimental results also show that the proposed RIRA approach is an efficient and practically applicable scheme with reasonable complexity.",
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Minimizing energy consumption for frame-based tasks on heterogeneous multiprocessor platforms. / Li, Dawei; Wu, Jie.

In: IEEE Transactions on Parallel and Distributed Systems, Vol. 26, No. 3, 6777565, 01.03.2015, p. 810-823.

Research output: Contribution to journalArticleResearchpeer-review

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