Racing and pacing to idle: an evaluation of heuristics for energy-aware resource allocation
Proceedings of the Workshop on Power-Aware Computing and Systems
A generalized software framework for accurate and efficient management of performance goals
Proceedings of the Eleventh ACM International Conference on Embedded Software
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Hardware execution throttling mechanisms such as duty cycle modulation and voltage/frequency scaling can effectively control core or chip-level resource consumption and hence have been advocated to manage multicore resource competition. However, finding the right throttle setting is challenging since the configuration space grows exponentially as the number of cores increases, making the naive approach of exhaustive search untenable. This paper proposes a flexible framework for Throttling-Enabled Multicore Management (TEMM) that efficiently finds a high-quality hardware execution throttling configuration for a user-specified resource management objective. In a manner similar to the Newton-Raphson method in numerical analysis, TEMM employs an iterative method to continuously improve the configuration search quality by leveraging the search results from previous iterations. Within each iteration, TEMM extrapolates the effects of throttling from reference configurations, searches for a high-quality throttling configuration based on model predictions (accelerated by hill climbing), sample-runs the selected configuration, and adds the measured performance and recorded execution statistics of interest as a new reference. Our evaluations show TEMM can quickly arrive at the exact or close to optimal throttling configuration.