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 voltagescalable (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, ...
Robert Springer, David K. Lowenthal, Barry Rountre