Energy-Efficient Operation of Multicore Processors by DVFS, Task Migration, and Active Cooling

Energy efficiency has taken center stage in all aspects of computing, regardless of whether it is performed on a portable battery-powered device, a desktop PC, on servers in a data center, or on a supercomputer. It is expressed as performance-per-watt (PPW), which is equal to the number of instructions that are executed per Joule of energy. The shift to multicore processors, with tens or hundreds of cores on a single die requires that the operation of the cores be dynamically controlled to maximize the processor's overall energy efficiency. This paper presents a unified formulation and an efficient solution for this problem. The solution considers dynamic frequency and voltage scaling, thread migration, and active cooling as the means to control the cores. The solution method is efficient for a real-time implementation. The formulation includes accurate power and thermal models, temperature constraints, and accounts for the dependence of leakage power and circuit delay on temperature. The PPW metric is extended to $(P^{\alpha }PW)$ (performance$(^\alpha)$-per-watt), which allows examining the tradeoffs between optimizing for performance versus optimizing for energy by varying $(\alpha)$. Simulation experiments assuming a four-core processor demonstrate that the derived control strategy can achieve 3.2脳 greater energy efficiency (i.e., executes more than three times the number of instructions per Joule) over the performance-optimal solution. The formulation and the efficiency of the solution method also allows for fast design space exploration. Specifically, it is shown how simply increasing the number of cores in a processor can significantly diminish its energy efficiency, and that there is an optimal number of cores that maximize the PPW. This number depends on the ratio of how much the power of an individual core is reduced by scaling, i.e., as the number of cores are increased. Finally, the proposed method is implemented on a quad-core Intel Sandy Bridge processor, and verified by running benchmarks. The experiments suggest that the proposed method results in an improvement of 37 percent over the current state-of-the-art energy-efficient schemes.