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

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Abstract
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 application mapping on emerging manycore architectures. CF-TUNE enable the extraction of an energy-efficient tuning configuration point, with minimal online application characterisation. 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 to previously optimized applications. We evaluate CF-TUNE's efficiency against a set of demanding and diverse applications mapped onto Intel Many Integrated Core processor, and we show that with minimal online characterization, e.g. only either two or four online evaluations, CF-TUNE recommends tuning configurations that achieve more than 94% in respect to the optimal one.

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