Modeling of DRAM power control policies using deterministic and stochastic Petri nets

  • Authors:
  • Xiaobo Fan;Carla S. Ellis;Alvin R. Lebeck

  • Affiliations:
  • Department of Computer Science, Duke University, Durham, NC;Department of Computer Science, Duke University, Durham, NC;Department of Computer Science, Duke University, Durham, NC

  • Venue:
  • PACS'02 Proceedings of the 2nd international conference on Power-aware computer systems
  • Year:
  • 2002

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Abstract

Energy is a critical resource in many computing systems, motivating the need for energy-efficient software design. This work proposes a new approach, energy-driven statistical sampling, to help software developers reason about the energy impact of software design decisions. We describe a prototype implementation of this approach built on the Itsy pocket computing platform. Our experimental results of 14 benchmark programs show that when multiple power states are exercised, energy-driven statistical sampling provides greater accuracy than existing time-driven statistical sampling approaches. Furthermore, if instruction-level energy attribution is desired, energy-driven statistical sampling may provide better resolution. On simple handheld systems, however, many applications may exercise only a single power state other than idle mode. In this case, time profiling may sufficiently approximate energy profiling for the purpose of assisting programmers, without requiring any hardware support.