Recently, energy has become an important issue in high-performance computing. For example, supercomputers that have energy in mind, such as BlueGene/L, have been built; the idea is to improve the energy efficiency of nodes. Our approach, which uses off-the-shelf, high-performance cluster nodes that are frequency scalable, allows energy saving by scaling down the CPU. This paper investigates the energy consumption and execution time of applications from a standard benchmark suite (NAS) on a power-scalable cluster. We study via direct measurement and simulation both intra-node and inter-node effects of memory and communication bottlenecks, respectively. Additionally, we compare energy consumption and execution time across different numbers of nodes. Our results show that a power-scalable cluster has the potential to save energy by scaling the processor down to lower energy levels. Furthermore, we found that for some programs, it is possible to both consume less energy and execute in less time when using a larger number of nodes, each at reduced energy. Additionally, we developed and validated a model that enables us to predict the energy-time tradeoff of larger clusters.