Achieving autonomous power management using reinforcement learning
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Temperature aware thread block scheduling in GPGPUs
Proceedings of the 50th Annual Design Automation Conference
CoMETC: Coordinated management of energy/thermal/cooling in servers
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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In this paper, we propose a multitier approach for significantly lowering the cooling costs associated with fan subsystems without compromising the system performance. Our technique manages the fan speed by intelligently allocating the workload at the core level as well as at the CPU socket level. At the core level we propose a proactive dynamic thermal management scheme. We introduce a new predictor that utilizes the band-limited property of the temperature frequency spectrum. A big advantage of our predictor is that it does not require the costly training phase and still maintains high accuracy. At the socket level, we use control theoretic approach to develop a stable scheduler that reduces the cooling costs further by providing a better thermal distribution. Our thermal management scheme incorporates runtime workload characterization to perform efficient thermally aware scheduling. The experimental results show that our approach delivers an average cooling energy savings of 80% compared to the state of the art techniques. The reported results also show that our formal technique maintains stability while heuristic solutions fail in this aspect.