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ABSTRACT
The peak power consumption of hardware components affects their powersupply, packaging, and cooling requirements. When the peak power consumption is high, the hardware components or the systems that use them can become expensive and bulky. Given that components and systems rarely (if ever) actually require peak power, it is highly desirable to limit power consumption to a less-than-peak power budget, based on which power supply, packaging, and cooling infrastructure scan be more intelligently provisioned. In this paper, we study dynamic approaches for limiting the powerconsumption of main memories. Specifically, we propose four techniques that limit consumption by adjusting the power states of thememory devices, as a function of the load on the memory subsystem. Our simulations of applications from three benchmarks demonstrate that our techniques can consistently limit power to a pre-established budget. Two of the techniques can limit power with very low performance degradation. Our results also show that, when using these superior techniques, limiting power is at least as effective an energy-conservation approach as state-of-the-art technique sexplicitly designed for performance-aware energy conservation. These latter results represent a departure from current energy management research and practice.
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CITED BY 4
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Matthew Curtis-Maury , Ankur Shah , Filip Blagojevic , Dimitrios S. Nikolopoulos , Bronis R. de Supinski , Martin Schulz, Prediction models for multi-dimensional power-performance optimization on many cores, Proceedings of the 17th international conference on Parallel architectures and compilation techniques, October 25-29, 2008, Toronto, Ontario, Canada
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Hongzhong Zheng , Jiang Lin , Zhao Zhang , Eugene Gorbatov , Howard David , Zhichun Zhu, Mini-rank: Adaptive DRAM architecture for improving memory power efficiency, Proceedings of the 2008 41st IEEE/ACM International Symposium on Microarchitecture, p.210-221, November 08-12, 2008
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