Towards optimizing energy costs of algorithms for shared memory architectures

Energy consumption by computer systems has emerged as an important concern. However, the energy consumed in executing an algorithm cannot be inferred from its performance alone: it must be modeled explicitly. This paper analyzes energy consumption of parallel algorithms executed on shared memory multicore processors. Specifically, we develop a methodology to evaluate how energy consumption of a given parallel algorithm changes as the number of cores and their frequency is varied. We use this analysis to establish the optimal number of cores to minimize the energy consumed by the execution of a parallel algorithm for a specific problem size while satisfying a given performance requirement. We study the sensitivity of our analysis to changes in parameters such as the ratio of the power consumed by a computation step versus the power consumed in accessing memory. The results show that the relation between the problem size and the optimal number of cores is relatively unaffected for a wide range of these parameters.