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Recently, improving the energy efficiency of HPC machines has become important. As a result, interest in using powerscalable clusters, where frequency and voltage can be dynamically modified, has increased. On power-scalable clusters, one opportunity for saving energy with little or no loss of performance exists when the computational load is not perfectly balanced. This situation occurs frequently, as balancing load between nodes is one of the long standing problems in parallel and distributed computing.In this paper we present a system called Jitter, which reduces the frequency on nodes that are assigned less computation and therefore have slack time. This saves energy on these nodes, and the goal of Jitter is to attempt to ensure that they arrive "just in time" so that they avoid increasing overall execution time. For example, in Aztec, from the ASCI Purple suite, our algorithm uses 8% less energy while increasing execution time by only 2.6%.

Power is now a first-order design constraint in large-scale parallel computing. Used carefully, dynamic voltage scaling can execute parts of a program at a slower CPU speed to achieve energy savings with a relatively small (possibly zero) time delay. However, the problem of when to change frequencies in order to optimize energy savings is NP-complete, which has led to many heuristic energysaving algorithms.To determine how closely these algorithms approach optimal savings, we developed a system that determines a bound on the energy savings for an application. Our system uses a linear programming solver that takes as inputs the application communication trace and the cluster power characteristics and then outputs a schedule that realizes this bound. We apply our system to three scientific programs, two of which exhibit load imbalance-particle simulation and UMT2K. Results from our bounding technique show particle simulation is more amenable to energy savings than UMT2K.

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 clusters save power at the expense of performance. Our approach is instead to use high-performance cluster nodes that are frequencyand 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 application in several frequency-voltage settings. The basic idea is to first divide programs into phases and then execute a series of experiments, 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 assignments 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 multiple 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.

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