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Discrete and continuous min-energy schedules for variable voltage processors.

Minming Li1, Andrew C Yao, Frances F Yao

  • 1Department of Computer Sciences and Technology and Center for Advanced Study, Tsinghua University, Beijing 100084, China.

Proceedings of the National Academy of Sciences of the United States of America
|March 16, 2006
PubMed
Summary

This study introduces a faster algorithm for min-energy job scheduling, reducing computation time from O(n^3) to O(n^2 log n). This advancement optimizes processor energy consumption for dynamic voltage scaling.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Algorithm Design

Background:

  • Dynamic voltage scaling (DVS) is crucial for energy efficiency in microprocessors.
  • A decade-old theoretical model for min-energy job scheduling exists, computable in O(n^3) time.
  • This model's efficiency has been a long-standing research challenge.

Purpose of the Study:

  • To develop a more efficient algorithm for min-energy job scheduling.
  • To improve upon the existing O(n^3) computational complexity.
  • To enhance energy saving strategies in microprocessor design.

Main Methods:

  • Developed a novel algorithm with a running time of O(n^2 log n).
  • Employed successive approximations to determine the optimal schedule.

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  • Utilized efficient partitioning of job sets based on speed thresholds.

    • Main Results:

    • Achieved a significant reduction in computational complexity for min-energy scheduling.
    • The new algorithm is faster than the previously known O(n^3) method.
    • The approach avoids computing the exact speed function for optimality.

    Conclusions:

    • The developed O(n^2 log n) algorithm offers a substantial improvement for min-energy job scheduling.
    • This advancement contributes to more effective energy consumption management in processors.
    • The successive approximation and partitioning method provides a practical approach to optimizing schedules.