A Statistical Learning Approach for Feature-Aware Task-to-Core Allocation in Heterogeneous Platforms
This work addresses energy efficiency and thermal management in computing systems, offering incremental improvements through statistical feature selection for task allocation.
The paper tackles the problem of optimizing task-to-core allocation in heterogeneous multi-core platforms to reduce power consumption without degrading user experience, achieving up to 10% lower energy consumption and 5°C lower core temperature compared to random selection, along with a 61.6% reduction in mean square error for thermal prediction.
Optimizing task-to-core allocation can substantially reduce power consumption in multi-core platforms without degrading user experience. However, many existing approaches overlook critical factors such as parallelism, compute intensity, and heterogeneous core types. In this paper, we introduce a statistical learning approach for feature selection that identifies the most influential features - such as core type, speed, temperature, and application-level parallelism or memory intensity - for accurate environment modeling and efficient energy optimization. Our experiments, conducted with state-of-the-art Linux governors and thermal modeling techniques, show that correlation-aware task-to-core allocation lowers energy consumption by up to 10% and reduces core temperature by up to 5 degrees Celsius compared to random core selection. Furthermore, our compressed, bootstrapped regression model improves thermal prediction accuracy by 6% while cutting model parameters by 16%, yielding an overall mean square error reduction of 61.6% relative to existing approaches. We provided results based on superscalar Intel Core i7 12th Gen processors with 14 cores, but validated our method across a diverse set of hardware platforms and effectively balanced performance, power, and thermal demands through statistical feature evaluation.