Hierarchical Power Management for Asymmetric MultiCore in Dark









![Resource Share Controller � Target heart rate hrref(Qi) = [hrrefmin, hrrefmax] � Measured heart Resource Share Controller � Target heart rate hrref(Qi) = [hrrefmin, hrrefmax] � Measured heart](https://slidetodoc.com/presentation_image_h/09730815997d04a474d0e4b8061cb2bc/image-10.jpg)



![Qo. S Controller � Ideal Heart Rate hrideal(Qi) = [hridealmin, hridealmax] � Target Heart Qo. S Controller � Ideal Heart Rate hrideal(Qi) = [hridealmin, hridealmax] � Target Heart](https://slidetodoc.com/presentation_image_h/09730815997d04a474d0e4b8061cb2bc/image-14.jpg)







- Slides: 21
Hierarchical Power Management for Asymmetric Multi-Core in Dark Silicon Era Thannirmalai Somu Muthukaruppan Mihai Pricopi Vanchinathan Venkataramani, Tulika Mitra School of Computing, National University of Singapore Sanjay Vishin Cambridge Silicon Radio DAC’ 13
Motivation �Dark silicon phenomenon ◦ A chip can have many cores but a significant fraction of them are left unpowered, or dark, at any point in time due to power and thermal limits. �Asymmetric multi-core architecture as an alternative ◦ Cores with diverse power-performance characteristics.
This paper �Introduce a hierarchical power management framework for asymmetric multi-cores. ◦ Builds on control theory. ◦ Coordinates multiple controllers in a synergistic manner to achieve optimal power-performance efficiency. ◦ Respects thermal design power budget.
Scenario �System exceeds the Thermal Design Power(TDP). ◦ => power budgets have to be reduced. ◦ => scaling down voltage and frequency. ◦ => the Qo. S degrades. �Reverse the process once the system load decreases. �Avoid oscillations!
ARM big. LITTLE �TC 2 ◦ Two high performance Cortex A 15 and three energy-efficient Cortex A 7 ◦ 3 rd model(HMP) ◦ Per-cluster DVFS
Impact of Active Cores on Cluster Power
Heart Rate �The throughput of the critical kernel of a Qo. S task. ◦ Ex: number of frames per second(fps) for a video encoder. �Heartbeats in Qo. S benchmark ◦ Heart rate = heartbeats per second
Feedback Based Controller �Proportional-Integral-Derivative(PID) Controller ◦ Kp, Ki, Kd : proportion, integral, derivative gain.
Framework Overview
Resource Share Controller � Target heart rate hrref(Qi) = [hrrefmin, hrrefmax] � Measured heart rate hr(Qi) � Slice s(Qi)
Core Utilization u(Qi), u(NQj): utilization of of Qo. S and non-Qo. S tasks. � u(Ck) = Σu(Qi) + Σu(NQj): utilization of core k � � u(Clm) = max(u(Ck)) : utilization of cluster m
DVFS Controller � Target utilization uref(Clm) = max(uideal, utarget(Clm))
Chip-Level Power Allocator � Hrthrottle(Qi): throttle factor of heart rate.
Qo. S Controller � Ideal Heart Rate hrideal(Qi) = [hridealmin, hridealmax] � Target Heart Rate hrref(Qi) = [hrrefmin, hrrefmax] = hrideal(Qi) x hrthrottle(Qi)
Load Balancer and Migrator � Balancer ensures that the cores within a cluster are evenly load balanced. � Migrator migrates tasks between clusters.
Experimental Setting �Versatile Express Development Platform ◦ 2 A 15 + 3 A 7 �Linux Completely Fair Scheduler(CFS) �Benchmarks:
Asymmetric V. S. Symmetric � x 264 benchmark ◦ Phases with varying performance requirements during execution.
HPM V. S. Linaro scheduler
HPM V. S. Linaro scheduler
Response under TDP Constraint
Conclusion �The authors present a power management framework for asymmetric multi-cores that based on multiple controllers. ◦ Exploits asymmetry among the cores through selective migration and employs DVFS to minimize power consumption while satisfying Qo. S constraints.





















