Using multiple energy gears in MPI programs on a power-scalable cluster

Recently, system architects have built low-power, high-performance clusters, such as Green Destiny. The idea behind these clusters is to improve the energy efficiency of nodes. However, these clus-ters save power at the expense of performance. Our approach is instead to use high-performance cluster nodes that are frequency-and voltage-scalable; energy can than be saved by scaling down the CPU. Our prior work has examined the costs and benefits of executing an entire application at a single reduced frequency. This paper presents a framework for executing a single applica-tion in several frequency-voltage settings. The basic idea is to first divide programs into phases and then execute a series of experi-ments, with each phase assigned a prescribed frequency. During each experiment, we measure energy consumption and time and then use a heuristic to choose the assignment of frequency to phase for the next experiment. Our results show that significant energy can be saved without an undue performance penalty; particularly, our heuristic finds assign-ments of frequency to phase that is superior to any fixed-frequency solution. Specifically, this paper shows that more than half of the NAS benchmarks exhibit a better energy-time tradeoff using mul-tiple gears than using a single gear. For example, IS using multiple gears uses 9 % less energy and executes in 1 % less time than the closest single-gear solution. Compared to no frequency scaling, multiple gear IS uses 16 % less energy while executing only 1% longer