Effective management of multiple configurable units using dynamic optimization

ABSTRACT

As one of the promising efforts to minimize the surging microprocessor power consumption, adaptive computing environments (ACEs), where microarchitectural resources can be dynamically tuned to match a program's run-time requirement and characteristics, are becoming increasingly common. In an ACE, efficient management of the configurable units (CUs) is vital for maximizing the benefit of resource adaptation. ACEs usually have multiple configurable hardware units, necessitating exploration of a large number of combinatorial configurations in order to identify the most energy-efficient configuration. In this paper, we propose an ACE management framework for efficient management of multiple CUs, utilizing dynamic optimization systems' inherent capabilities of detecting and optimizing program hotspots, i.e., dominate code regions. We develop a scheme where hotpot boundaries are used for phase detection and adaptation. The framework achieves good energy reduction on managing multiple CUs with minimal hardware requirements and low implement cost by leveraging the existing infrastructure of a dynamic optimization system. The proposed framework is evaluated by dynamically adapting five CUs with distinct reconfiguration latencies and overheads. Those CUs are issue queue, reorder buffer, level-one data and instruction caches, and level-two cache. Previous research indicates that those five components dominate the energy consumption of a microprocessor. Despite the growing complexity and overhead of adapting five CUs, our technique reduces the energy consumption of those CUs by as much as 45%, while one of the best techniques provided by prior literature achieves less than 15% energy reduction for all CUs.

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INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.4 Processors
          Subjects: Optimization

General Terms:
Design, Experimentation, Performance

Keywords:
Adaptive computing environment (ACE), dynamic optimization, hotspots, power dissipation