Energy Efficient Computing
In this talk I give an overview of the algorithms we have developed at UCSD to significantly lower the energy consumption in computing systems. We derived optimal power management strategies for stationary workloads that have been implemented both in HW and SW. Run-time adaptation can be done via an online learning algorithm that selects among a set of policies. We generalize the algorithm to include thermal management since we found that minimizing the power consumption does not necessarily reduce the overall energy costs. To reduce the performance costs typically associated with state of the art thermal management techniques, we developed a new set of proactive management policies. The experimental results using real datacenter workloads on an actual multicore system show that our proactive technique is able to dramatically reduce the adverse effects of temperature by over 60 energy savings with 20 performance benefit in high utilization scenarios. I will also present some of the recent work we had done to address the energy savings in battery powered and energy harvesting systems. We are designing a new kind of ΓÇ£citizen infrastructureΓÇ¥, CitiSense, as an end-to-end health and environmental information system with near real-time data streams and feedback loops from the system to the sensing, processing, and actuation infrastructure. We have developed adaptive algorithms to tradeoff accuracy of computation versus the available energy for such systems, while taking into account the energy harvesting capabilities.
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