Although users of high-performance computing are most interested in raw performance both energy and power consumption has become critical concerns. One approach to lowering energy and power is to use high-performance cluster nodes that have several power-performance states so that the energy-time trade-off can be dynamically adjusted. This paper analyzes the energy-time trade-off of a wide range of applications-serial and parallel-on a power-scalable cluster. We use a cluster of frequency and voltage-scalable AMD-64 nodes, each equipped with a power meter. We study the effects of memory and communication bottlenecks via direct measurement of time and energy. We also investigate metrics that can, at runtime, predict when each type of bottleneck occurs. Our results show that, for programs that have a memory or communication bottleneck, a power-scalable cluster can save significant energy with only a small time penalty. Furthermore, we find that, for some programs, it is possible to both consume less energy and execute in less time by increasing the number of nodes while reducing the frequency-voltage setting of each node

Recently, the high-performance computing community has realized that power is a performance-limiting factor. One reason for this is that supercomputing centers have limited power capacity and machines are starting to hit that limit. In addition, the cost of energy has become increasingly significant, and the heat produced by higher-energy components tends to reduce their reliability. One way to reduce power (and therefore energy) requirements is to use high-performance cluster nodes that are frequency- and voltage-scalable (e.g., AMD-64 processors).The problem we address in this paper is: given a target program, a power-scalable cluster, and an upper limit for energy consumption, choose a schedule (number of nodes and CPU frequency) that simultaneously (1) satisfies an external upper limit for energy consumption and (2) minimizes execution time. There are too many schedules for an exhaustive search. Therefore, we find a schedule through a novel combination of performance modeling, performance prediction, and program execution. Using our technique, we are able to find a near-optimal schedule for all of our benchmarks in just a handful of partial program executions.