Rapid increases in demand for cloud computing, large-scale internet services, and mobile devices have led to a correspondingly large increase in the amount of energy required for computation. This additional energy use comes at a high cost, both financially and environmentally.

Energy profilers are useful tools which allow programmers to energy audit their programs and determine what functions are responsible for the most energy use. In this paper, we introduce a new technique for energy profiling, which takes energy measurements at regularly spaced intervals and uses linear regression to correlate executing functions with changes in power demand. We have implemented this technique in Alpaca, an energy profiler for Linux. Initial results suggest that this approach shows promise in energy profiling programs in an accurate and meaningful way.