Automated Approach for Accurate CPU Power Modelling

Power supply is a limiting factor when increasing the computing capacity of supercomputers. As a consequence, power consumption has become one of the biggest challenges in the field of High Performance Computing (HPC). In order to develop energy-efficient tools (e.g., frameworks, applications), it is essential to have an accurate power consumption modelling. Al-though previous works proposed a wide variety of approaches to model CPU power consumption, building models in an automated and adaptable way to changing scenarios and predicting power with high precision remains complex due to multiple factors (e.g., training and test workloads, model variables). In this paper, we present a set of tools to fully automate the process of modelling power consumption using CPU time series data. More specifically, our proposal includes two tools: (1) CPUPowerWatcher, which gathers CPU metrics during the execution of user-configurable workloads; and (2) CPUPowerSeer, which builds models to predict CPU power consumption (e.g., polynomial regression) from different CPU variables (e.g., usage, clock frequency) using time series data. Thus, multiple models can be created and evaluated easily, allowing the selection of an optimal model for a specific workload. The experiments conducted by combining these tools allow analysing the impact of novel factors on CPU power consumption, such as the type of CPU usage generated by different workloads or how the CPU cores are allocated to them. In addition, the accuracy of six regression models is compared when predicting CPU- and I/O-intensive workloads using two different core allocations.