Transcript ppt
Zhou Peng, Zuo Decheng, Zhou Haiying Harbin Institute of Technology 1
1.Introducation
2.Workload effect on Energy effective 3.Conclusion & Future works Harbin institution of technology 2
Green computing is imperative
Increasing of
computers
Increasing of
energy cost
Increasing of
Carbon emissions
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Moore’s law Harbin institution of technology Moore’s law for energy effective 4
Explosive growth of the tasks and complexity
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 growth of energy density in battery
Linear improve of battery
Battery life become shorter and shorter ； e.g. smart phones Harbin institution of technology 5
◦ ◦ ◦ 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 To Shutdown unused component or circuit Harbin institution of technology 6
◦ ◦ According to the present researches: C-state can save up to
44%
[1] DVFS can save
13%
[2] to energy
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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Two solutions: slow down & race-to-halt Typical technology Time to finish task Deadline miss Energy effective
Slow down
DVFS Dynamic & low Longer High risk Save lots of energy
race-to-halt
C-state Higher short Lower risk Save lots of energy Objectives: To evaluate the energy effective of
DVFS & C-state
with different task models Harbin institution of technology 8
1.Introducation
2.Workload effect on Energy effective
3.Conclusion & Future works Harbin institution of technology 9
Relationship of the power and the frequency:
P
CV dd
2
P static
C
: is the capacitance of the transistor gates
f
: is the frequency
V dd
: is the supply voltage of the device.
P static
: represents power consumed from leakage mechanisms.
f
Relationship of the voltage and frequency: (
V dd
V t
, 2 ) /
V dd k:
is a circuit dependent constant
V t
: 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] . Harbin institution of technology 10
◦ ◦ 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
d
2
dd CV W dd
)
PT
2
CV W dd
ignore the energy cost of cache misses.
WV P dd static dd
V t
) 2 is energy consumption based on dynamic power
C
: capacitance
f
: frequency
V dd
: runtime voltage
P stati
c : leakage power
V peak
: peak voltage
T
r : Time to finish task
T s
:Time to sleep
W
: workload, the instruction
WV P dd static
dd V t
) 2 is energy consumption based on leakage power cycles of a task ◦ Summary: DVFS: compute the energy consumption of processor but T r+ T s = W/f d Harbin institution of technology 11
◦ 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
E c
2
CWV peak
P T static r
P T sleep s
◦
T r+ T s
is the interval time of a task run-to-completion based on DVFS
T
r+
T
s
= W/f
d
◦ ◦ ◦ 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.
P sleep
is very low, which can be ignored.
C
:capacitance
f
:frequency
V dd
: runtime voltage
P stati
c : leakage power
V peak
: peak voltage
T
r : Time to finish task
T s
:Time to sleep
W
: workload, the instruction cycles of a task Harbin institution of technology 12
In order to minimize the energy consumption and also try to find the best voltage, we can get the derivative of energy models The derivative of energy model 2
CV W dd
WP st
dd V t
) 2 2
WV P dd st dd
V t
) 3 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 Harbin institution of technology 13
In order to evaluate the energy effective of DVFS and C-state ， We get the difference value of the two energy models ：
E d
E c
2
dvfs
2
V peak
) C-state becomes popular because P static increase effects
P static
(
t
W
)
f dvfs
(leakage power) We can consider time
t
as the workload arrival time,
t
CW P st
2 (
V peak
2
v dvfs
)
W f dvfs
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For
Poisson distribution
workload
◦ ◦ The average arrival rate of task is λ 0 ; The average interval time of task is t=1/ λ 0 1 0
CW P st
2 (
V peak
2
v dvfs
)
f W dvfs
Summary:
◦ DVFS and C-state save the same energy in this situation When deadline t deadline DVFS; < t, C-state saves more energy than ◦ When the arrival rate λ>λ 0 , DVFS is better than C-state Harbin institution of technology 15
For
Periodic distribution
workload
◦ C-state saves more energy if and only if the deadline is ◦ smaller than period, i.e. t deadline < 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
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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] .
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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. Harbin institution of technology 19
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Pavel Somavat. 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, 8.
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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 Harbin institution of technology 20
Thank you!
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