dynamic power management
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Dynamic power management. Introduction Implementation, levels of operation Modeling Power and performance issues regarding power management Policies Conclusions. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
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2001-11-22 Mehdi Amirijoo 1
Dynamic power management
Introduction Implementation, levels of operation Modeling Power and performance issues regarding
power management Policies Conclusions
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2001-11-22 Mehdi Amirijoo 2
Introduction To provide the requested services and
performance levels with a minimum number of active components or a minimum load on such components.
Assume non-uniform workload. Assume predictability of workload. Low overhead of caused by power manager;
performance and power.
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2001-11-22 Mehdi Amirijoo 3
Introduction The power manager (PM) implements a control
procedure based on observations and assumptions about the workload.
The control procedure is called a policy. Oracle power manager
P o w er M an g er
D ev ic e 1 D ev ic e n. . . . .
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Implementation Hardware
– Frequency reduction– Supply voltage– Power shutdown
Software– Mostly used– Most flexible
Operative system power manager (OSPM)– Microsoft’s OnNow– ACPI
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Modeling View the system as a set of interacting power-
manageable components (PMCs), controlled by
the power manager (PM).
P o w er m an ag er
O b s er v er C o n tr o lle r
S y s tem
P M C 1
C o m m an dO b s er v a tio n
P M C 2
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Modeling Independent PMCs.
Model PMCs as FSMs; PSMs
Transition between states have a cost.
The cost is associated with delay, performance and power loss.
Service providers and service requesters.
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Modeling Ex. StrongArm SA-1100 processor (Intel)
R u n
S l e e pI dle
P = 4 0 0 m W
P = 5 0 m W P = 0 .1 6 m W
9 0 u s
1 6 0 ms
1 0 u
s1 0 u
s
9 0 us
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Power and performance issues.. Power management degrades performance.
R eq u es t
Bu s y
W o r k
R eq u es t
I d le Bu s y
T s d S leep T w u W o rk
T 1 T 2 T 3 T 4
W o r k lo ad
D ev ic e
P o w er s ta te
P o w er
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Power and performance issues.. Break-even time Tbe - minimum length of an idle
period to save power. Move to sleep state if Tidle > Tbe
• T0 : Transition delay (shutdown and wakeup)
• E0 : Transition energy
• Ps , Pw : Power in sleeping and working states
000
0000
,max
)()(
TPP
TPET
PP
TPETTTPETP
sw
sbe
sw
sbebesbew
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Policies Different categories:
– Predictive– Adaptive– Stochastic
Application dependent Statistical properties Resource requirements
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Policies - Predictive Fixed time-out:
– Static– Assume that if a device is idle for , it will remain idle for at
least Tbe.
– If device idle for , change state to sleep.– Time-out is computed and set off-line.– Very simple to implement. Requires a timer.– Power is wasted in waiting for time-out.– Can cause many under-predictions.– Adaptive version where is adjusted online.
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Policies - Predictive Predictive shut-down [Golding 1996]:
– Take decisions based observations of past idle and busy times. Take decision as soon as an idle time starts.
– The equation f yields a predicted idle time Tpred
– Shut down if
– Sample data and fit data to a non-linear regression equation f (off-line).– Computation and memory requirements.
),..,,,...,( 11 knidle
nidle
knbusy
nbusy
npred TTTTfT
benpred TT
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Policies - Predictive Predictive shut-down [Srivastava 1996]
– Take decision based on observing the last busy time. Take decision as soon as an idle time starts.– If change state.– Suitable for devices where short busy periods are followed by long idle periods. L-shape plot diagrams (idle period vs busy periods).
FSMs similar to multibit branch prediction in processors. Predictive wake-up
Thrnbusy TT
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Policies - Adaptive Static policies are ineffective when the
workload is nonstationary or not known in advance.
Time-out revisited:1. Adapt the time-out .
2. Keep a pool of time-outs and choose the one that will perform best in this context.
3. As above, but assign a weight to each time-out according to how well it will perform
relative to an optimum strategy for the last requests.
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Policies - Adaptive Low pass filter [Wu1997] :
11 )1( npred
nidle
npred TTT
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Policies - Stochastic Predictive and adaptive policies lack some
properties:– They are based on a two state system model. – Parameter tuning can be hard.
Stochastic policies provide a more general and optimal strategies.
Modeled by Markov chains, Pareto.
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Policies - Stochastic (Markov) Stationary (or WSS). Statistical properties do not
depend on the time shift, k.
)1,..,0,,..,(
)1,..,,,..,(
10
10
naaf
nkkaaf
n
n
A set of states. Probability associated with the transitions. The solution of the LP produces stationary, randomized (nondeterministic)
policy. Finding the minimum power policy that meets a given performance
constraint can be cast as a linear program (LP, solved in polynomial time).
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Policies - Stochastic (Markov)
0 10 .9 5 0 .8 5
0 .0 5
0 .1 5
O n O ffs _ o n : 1 .0s _ o f f : 0 .8
s _ o n : 0 .0s _ o f f : 0 .2
s _ o n : 0 .1s _ o f f : 0 .0
s _ o n : 0 .9s _ o f f : 1 .0
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Policies - Stochastic (Markov) The policy computed by LP is globally optimum
[Puterman 1994]. However, requires knowledge of the system and its
workload statistics in advance.
An adaptive extension [Chung 1999]:– Policy precharacterization (PC)– Parameter learning (PL)– Policy interpolation (PI)
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Policies - Stochastic (Markov) An adaptive…(cont.)
– Two-parameters Markov. Parameters “describe” the current workload.
– PC constructs a 2-dim table, addressed by the values of the two parameters.
– The table elements contain the optimal policy, identified by the pair.
– Parameter learning is performed during operation.– PI is performed to find a policy as a combination of the
nearby policies given by the table and the parameters.
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Conclusions The policies are application dependent and have to
be adopted to devices. Policies based on stochastic control and specially
Markov allows a flexible and general design, where all requirements can be incorporated.
Current models are based on observing requests arrivals. A trend in power management is to include higher-level information, particularly software-based information from compilers and OSs.