CF-TUNE: Collaborative Filtering Auto-Tuning for Energy Efficient Many-Core Processors

Energy efficiency is considered today as a first class design principle of modern many-core computing systems in the effort to overcome the limited power envelope. However, many-core processors are characterised by high micro-architectural complexity, which is propagated up to the application level affecting both performance and energy consumption. In this paper, we present CF-TUNE, an online and scalable auto-tuning framework for energy aware applications mapping on emerging many-core architectures. CF-TUNE enables the extraction of an energy-efficient tuning configuration point with minimal application characterisation on the whole tuning configuration space. Instead of analyzing every application against every tuning configuration, it adopts a collaborative filtering technique that quickly and with high accuracy configures the application's tuning parameters by identifying similarities with previously optimized applications. We evaluate CF-TUNE's efficiency against a set of demanding and diverse applications mapped on Intel Many Integrated Core processor and we show that with minimal characterization, e.g., only either two or four evaluations, CF-TUNE recommends a tuning configuration that performs at least at the 94 percent level of the optimal one.