The amount of energy available in some contexts is strictly limited. For example, in mobile computing, available
energy is constrained by battery capacity. As multicore processors with a large number of processors, it will be
possible to significantly vary the number and frequency of cores used in order to manage the performance and
energy consumption of an algorithm. We develop a method to analyze the scalability of an algorithm given an
energy budget. The resulting energy-bounded scalability analysis can be used to optimize performance of a parallel
algorithm executed on a scalable multicore architecture given an energy budget. We illustrate our methodology by
analyzing the behavior of four parallel algorithms on scalable multicore architectures: namely, parallel addition,
two versions of parallel quicksort, and a parallel version of Prim???s Minimum Spanning Tree algorithm. We study
the sensitivity of energy-bounded scalability to changes in parameters such as the ratio of the energy required
for a computational operation versus the energy required for communicating a unit message. Our results shows
that changing the number and frequency of cores used in a multicore architecture could significantly improve
performance under fixed energy budgets.published or submitted for publicatio
