7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
7-days of FREE Audio papers, translation & more with Prime
7-days of FREE Prime access
https://doi.org/10.7873/date.2015.0992
Copy DOIPublication Date: Jan 1, 2015 |
Citations: 98 |
As power density emerges as the main constraint for many-core systems, controlling power consumption under the Thermal Design Power (TDP) while maximizing the performance becomes increasingly critical. To dynamically save power, Dynamic Voltage Frequency Scaling (DVFS) techniques have proved to be effective and are widely available commercially. In this paper, we present an On-line Distributed Reinforcement Learning (OD-RL) based DVFS control algorithm for many-core system performance improvement under power constraints. At the finer grain, a per-core Reinforcement Learning (RL) method is used to learn the optimal control policy of the Voltage/Frequency (VF) levels in a system model-free manner. At the coarser grain, an efficient global power budget reallocation algorithm is used to maximize the overall performance. The experiments show that compared to the state-of-the-art algorithms: 1) OD-RL produces up to 98% less budget overshoot, 2) up to 44.3x better throughput per over-the-budget energy and up to 23% higher energy efficiency, and 3) two orders of magnitude speedup over state-of-the-art techniques for systems with hundreds of cores.
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.