Zhou Peng, Zuo Decheng, Zhou Haiying Harbin Institute of Technology

vengefulsantaclausElectronics - Devices

Nov 25, 2013 (3 years and 10 months ago)

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Zhou
Peng
,
Zuo

Decheng
,
Zhou
Haiying

Harbin Institute of Technology

1

Harbin institution of technology


1.Introducation


2
.Workload effect on Energy effective


3.Conclusion & Future works


2

Harbin institution of technology

Green computing is
imperative

Increasing of
computers

Increasing of
energy
cost

Increasing of
Carbon
emissions

3

Harbin institution of technology

Moore’s law

Moore’s
law

for energy
effective

4

Harbin institution of technology

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

5

VS

B
attery
life become shorter and shorter



e.g. smart phones

Harbin institution of technology


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

6

To Shutdown
unused
component or circuit

Harbin institution of technology


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.

7

Harbin institution of technology


Two solutions: slow down & race
-
to
-
halt










Objectives
: To
evaluate
the energy effective of
DVFS &
C
-
state
with different task models


8

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?

Harbin institution of technology


1.Introducation


2.Workload effect on Energy effective


3.Conclusion &
Future works



9

Harbin institution of technology

10

2
dd static
P CV f P
  
2
( )/
dd t dd
f k V V V
  

Relationship of the power and the frequency:


Relationship of the voltage and frequency:


k:

is a circuit dependent constant


V
t
:

is the threshold voltage


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.

,

Note that:


T
he
operation frequency almost has a linear
relationship with
voltage.


BUT, d
ecreasing
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


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.


11

2
2
( )
( )
dd static
d dd dd
dd t
WV P
E V PT CV W
k V V
  


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



T
r+
T
s

= W/
f
d

2
dd
CV W
2
( )
dd static
dd t
WV P
k V V

Harbin institution of technology


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





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
.



is very low, which can be ignored.

12

2
c peak static r sleep s
E CWV P T P T
  

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



sleep
P
Harbin institution of technology


The derivative of energy model

13

2 3
2
( ) 2
( ) ( )
st dd st
dd
dd t dd t
WP WV P
dv 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

Harbin institution of technology


C
-
state becomes popular because
P
static

(leakage power)
increase effects


We can consider time
t
as the workload
arrival
time,
when , rewrite the equation

14

2 2
( ) ( ) ( )
d c dvfs peak static
dvfs
W
E 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


( ) 0
E t
 
2 2
( )
peak dvfs
st dvfs
CW W
t V v
P f
  
Harbin institution of technology


For
Poisson distribution
workload


The average arrival rate of task is
λ
0
;


The
average interval time of task is t=1/

λ
0




Summary:


DVFS and C
-
state save the same energy in this situation
When deadline
t
deadline

< t, C
-
state saves more energy than
DVFS;


W
hen the arrival rate
λ
>
λ
0
,
DVFS is better than C
-
state

15

2 2
0
1
( )
peak dvfs
st dvfs
CW W
V v
P f

  
Harbin institution of technology

16


For
P
eriodic
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.

Harbin institution of technology


1.Introducation


2
.Workload effect on Energy effective


3.Conclusion & Future works


17

Harbin institution of technology


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]
.

18

Harbin institution of technology


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.

19

Harbin institution of technology

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

20

Harbin institution of technology

Thank
you!