Workload characterization and prediction: A pathway to reliable multi-core systems

As a result of technology scaling, power density of multi-core chips increases and leads to temperature hot-spots which accelerate device aging and chip failure. Moreover, intense efforts to reduce power consumption by employing low-power techniques decrease the reliability of new design generations. Traditionally, reactive thermal/power management techniques have been used to take appropriate action when the temperature reaches a threshold. However, these approaches do not always balance temperature and, as a result, may degrade system reliability. Therefore, to distribute temperature evenly across all cores, a proactive mechanism is needed to forecast future workload characteristics and the corresponding temperature, in order to make decisions before hot spots occur. Such proactive methods rely on an engine to precisely predict future workload characteristics. In this work, we first discuss the state-of-the-art methods for predicting workload dynamics and we compare their performance. We, then, introduce a prediction method based on Support Vector Regression (SVR), which accurately predicts the workload behavior several steps ahead. To evaluate the effectiveness of our approach, we use several programs from the PARSEC benchmark suite on an UltraSPARC T1 processor running the Sun Solaris operating system and we extract architectural traces. Then, the extracted traces are used to generate power and thermal profiles for each core using the McPAT and Hot-Spot simulators. Our results show that the proposed method forecasts workload dynamics and power very accurately and outperforms previous prediction techniques.