Slow Down or Race to Halt: Workload effect on Energy Effective

1. Slow Down or Race to Halt: Workload effect on Energy EffectiveSlow Down or Race to Halt: Workload effect on Energy Effective Zhou Peng, ZuoDecheng, Zhou Haiying Harbin Institute of Technology

2. Outline 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works

3. Background

4. Energy effective Moore’s law Moore’s lawfor energy effective

5. VS • Exponential growth of code； e.g. Linux code in tar.gz format increase from 117K(0.11) to 109M(3.11.1) • Explosive growth of applications;e.g. apps for android and apple • Explosive growth of amount of computation; e.g.AI & Big data • Linear improve of battery Battery life become shorter and shorter； e.g. smart phones Explosive growth of the tasks and complexity Linear growth of energy density in battery

6. Motivation To Shutdown unused component or circuit • Main technologies to improve energy effective • Hardware level: Low power devices • System level: Power-management mechanisms in different levels • Application level: Consolidate with virtualization • Power-management mechanisms • Circuit level: Clock-gating • System level: DPM • Processor level: DVFS/DFS/DVS, C-state

7. Slow Down or Race to HaltDVFS vs. C-state • According to the present researches: • C-state can save up to 44%[1] energy • DVFS can save 13%[2]to70%[3]energy • Limitation of present research • All the results come from particular system with special application or SPAC CPU. • Few works can consider the effect of workload to the energy consumption.

8. DVFS vs. C-state DVFS vs C-state: which is better in energy effective? Two solutions: slow down & race-to-halt Objectives: To evaluate the energy effective of DVFS & C-state with different task models

9. Outline 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works

10. Relate works & Premise • Relationship of the power and the frequency: • C : is the capacitance of the transistor gates • f : is the frequency • Vdd: is the supply voltage of the device. • Pstatic: represents power consumed from leakage mechanisms. • Relationship of the voltage and frequency: • k: is a circuit dependent constant • Vt: is the threshold voltage , Note that: The operation frequency almost has a linear relationship with voltage. BUT, decreasing the frequency and keeping the voltage constant does not contribute much to energy saving. It just saves the cost of cache misses[11].

11. DVFS model • C: capacitance • f : frequency • Vdd: runtime voltage • Pstatic: leakage power • Vpeak: peak voltage • Tr: Time to finish task • Ts:Time to sleep • W: workload, the instruction cycles of a task • Tr+Ts= W/fd • DVFS Modeling • Defining the amount of computation/ instructions for a task/workload is W, • and then within a period of run-to-completion, the energy consumption of task is • is energy consumption based on dynamic power • is energy consumption based on leakage power • Summary: • DVFS: compute the energy consumption of processor but ignore the energy cost of cache misses.

12. C-state model • C:capacitance • f :frequency • Vdd: runtime voltage • Pstatic: leakage power • Vpeak: peak voltage • Tr: Time to finish task • Ts:Time to sleep • W: workload, the instruction cycles of a task • C-state Modeling • Defining the amount of computation/ instructions for a task/workload is W, and then within a period of run-to-completion, the energy consumption of task is • Tr+Tsis the interval time of a task run-to-completion based on DVFS Tr+Ts= W/fd • Summary: • C-state operates at higher voltage, So C-state finish a task faster than DVFS. • If all the tasks is completed, system changes to sleep mode. • is very low, which can be ignored.

13. Analysis of the optimal voltage In order to minimize the energy consumption and also try to find the best voltage, we can get the derivative of energy models • The extreme point in energy model shows that • Workload W is not the key influence factor to the minimal energy consumption • The minimal energy consumption is only depended on the characteristics of devices The derivative of energy model

14. Workload effect on Energy effective In order to evaluatethe energy effective of DVFS and C-state， We get the difference value of the two energy models： C-state becomes popular because Pstatic (leakage power) increase effects We can consider time t as the workload arrival time, when , rewrite the equation

15. Workload effect on Energy effective • For Poisson distribution workload • The average arrival rate of task is λ0; • The average interval time of task is t=1/λ0 • Summary: • DVFS and C-state save the same energy in this situation When deadline tdeadline < t, C-state saves more energy than DVFS; • When the arrival rate λ>λ0, DVFS is better than C-state

16. Workload effect on Energy effective • For Periodic distribution workload • C-state saves more energy if and only if the deadline is smaller than period, i.e. tdeadline< t; • DVFS does not shutdown the processor after the task finished.

17. Outline 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works

18. Conclusion • Evaluate the energy effective of DVFS & C-state with different task models • The most energy saving voltage is only depended on the characteristics of the device itself. • The energy effective of DVFS and C-state is closely related to the arrival rate of the tasks and the features of workloads. • For the heavy workload systems, DVFS is better in energy saving than another. The result is consistent with the conclusion in [5].

19. Future works • In this paper, we mainly focus on processor and ignore the energy consumption during state transition. • So, future works will be: • To analyze the effects of cache hit rate on energy effective in the whole system. • To take the reliability into consideration. • To explore the schedulability analysis methods for the energy and reliability critical system.

20. Reference PavelSomavat. Accounting for the Energy Consumption of Personal Computing Including Portable Devices Rotem, E., et al. Energy Aware Race to Halt: A Down to EARtH Approach for Platform Energy Management. Computer Architecture Letters. Shekar, V. and B. Izadi. Energy aware scheduling for DAG structured applications on heterogeneous and DVS enabled processors. Valentini, Giorgio Luigi, et al. An overview of energy efficiency techniques in cluster computing systems. Petters, S. M. and M. A. Awan., Slow down or race to halt: Towards managing complexity of real-time energy management decisions. Awan, M. A. and S. M. Petters. Enhanced race-to-halt: A leakage-aware energy management approach for dynamic priority systems. Real-Time Systems Naik, R. Biswas, S. , Datta, S.; Distributed Sleep-Scheduling Protocols for Energy Conservation in Wireless Networks. System Sciences, Le Sueur, Etienne, Heiser, Gernot. Dynamic voltage and frequency scaling: The laws of diminishing returns. Le Sueur, E. and G. Heiser. Slow Down or Sleep, that is the Question. Schmitz, M.T., et al.; Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems. Wan Yeon Lee. Energy-Saving DVFS Scheduling of Multiple Periodic Real-Time Tasks on Multi-core Processors. F. Paterna, et al.Variability-Tolerant Workload Allocation for mpsoc Energy Minimization under Real-Time Constraints

21. Thank you!