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Unified framework for energy-proportional computing in multicore processors: novel algorithms and practical implementation

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Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a

Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multi-cores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%

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2013