• linear regression model
• parallel computing
• power-aware computing
• self-adaptive computing
• simultaneous multithreading

Energy demanding trend in Information and Communication Technologies makes power consumption an important metric in parallel computations. In this thesis we focus on the contribution brought by the Simultaneous Multithreading (SMT) technology both on performances and on power consumption. We build a linear regression model that integrates SMT and we show that it can predict both metrics more accurately with respect to another model that ignores SMT. This is tested both on offline data, by calculating the model error for a test set, and online by integrating it in Nornir, a framework for self-adaptive computing.