Post on 31-Mar-2015
Slow Down or Race to Halt: Workload effect on
Energy Effective
Zhou Peng, Zuo Decheng, Zhou HaiyingHarbin Institute of Technology
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1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works
Outline
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Background
Green computing is imperative
Increasing of computers
Increasing of energy cost
Increasing of Carbon emissions
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Energy effective
Moore’s law Moore’s law for energy effective
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Explosive growth of the tasks and complexity
Linear growth of energy density in battery
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
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VS
Battery life become shorter and shorter ; e.g. smart phones
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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
Motivation
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To Shutdown unused component or circuit
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According to the present researches:◦ C-state can save up to 44%[1] energy◦ DVFS can save 13%[2] to 70%[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.
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Slow Down or Race to HaltDVFS vs. C-state
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Two solutions: slow down & race-to-halt
Objectives: To evaluate the energy effective of DVFS & C-state with different task models
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DVFS vs. C-state
Slow down race-to-halt
Typical technology DVFS C-state
Runtime power Dynamic & low Higher
Time to finish task Longer short
Deadline miss High risk Lower risk
Energy effective Save lots of energy Save lots of energy
DVFS vs C-state: which is better in energy effective?
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1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works
Outline
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Relate works & Premise
2dd staticP CV f P
2( ) /dd t ddf k V V V
Relationship of the power and the frequency:
Relationship of the voltage and frequency:
k: is a circuit dependent constant Vt: is the threshold voltage
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.
, 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] .
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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.
DVFS model
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( )( )
dd staticd dd dd
dd t
WV PE V PT CV W
k V V
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
2ddCV W
2( )dd static
dd t
WV P
k V V
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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+Ts is 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.
C-state model
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2c peak static r sleep sE CWV P T P T
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
sleepP
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The derivative of energy model
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Analysis of the optimal voltage
2 3
2( ) 2
( ) ( )st dd st
dddd t dd t
WP WV Pdv E CV W
k V V k V V
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
In order to minimize the energy consumption and also try to find the best voltage, we can get the derivative of energy models
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C-state becomes popular because Pstatic (leakage power) increase effects
We can consider time t as the workload arrival time, when , rewrite the equation
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Workload effect on Energy effective
2 2( ) ( ) ( )d c dvfs peak staticdvfs
WE t E E CW v V P t
f
In order to evaluate the energy effective of DVFS and C-state ,We get the difference value of the two energy models :
( ) 0E t
2 2( )peak dvfsst dvfs
CW Wt V v
P f
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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
Workload effect on Energy effective
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2 2
0
1( )peak dvfs
st dvfs
CW WV v
P f
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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.
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1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works
Outline
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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].
Conclusion
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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.
Future works
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1. Pavel Somavat. Accounting for the Energy Consumption of Personal Computing Including Portable Devices
2. Rotem, E., et al. Energy Aware Race to Halt: A Down to EARtH Approach for Platform Energy Management. Computer Architecture Letters.
3. Shekar, V. and B. Izadi. Energy aware scheduling for DAG structured applications on heterogeneous and DVS enabled processors.
4. Valentini, Giorgio Luigi, et al. An overview of energy efficiency techniques in cluster computing systems.
5. Petters, S. M. and M. A. Awan., Slow down or race to halt: Towards managing complexity of real-time energy management decisions.
6. Awan, M. A. and S. M. Petters. Enhanced race-to-halt: A leakage-aware energy management approach for dynamic priority systems. Real-Time Systems
7. Naik, R. Biswas, S. , Datta, S.; Distributed Sleep-Scheduling Protocols for Energy Conservation in Wireless Networks. System Sciences,
8. Le Sueur, Etienne, Heiser, Gernot. Dynamic voltage and frequency scaling: The laws of diminishing returns.
9. Le Sueur, E. and G. Heiser. Slow Down or Sleep, that is the Question.
10. Schmitz, M.T., et al.; Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems.
11. Wan Yeon Lee. Energy-Saving DVFS Scheduling of Multiple Periodic Real-Time Tasks on Multi-core Processors.
12. F. Paterna, et al.Variability-Tolerant Workload Allocation for mpsoc Energy Minimization under Real-Time Constraints
Reference
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Thank you!