Understanding hardware and software metrics with respect to power consumption

In this paper, we utilize statistical techniques to foster understand and investigate hardware counters as potential indicators of energy behavior. We capture hardware and software counters including power with a fixed frequency and analyze the resulting timelines of these measurements. The concepts introduced can be applied to any set of measurements in order to compare them to another set of measurements. We demonstrate how these techniques can aid identifying interesting behavior and significantly reducing the number of features that must be inspected. Next, we propose counters that can potentially be used for building linear models for predicting with a relative accuracy of 3%. Finally, we validate the completeness of a benchmark suite, from the point of view of using the available architectural components, for generating accurate models.