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Dynamic power management. Introduction Implementation, levels of operation Modeling Power and performance issues regarding power management Policies Conclusions. Introduction.

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dynamic power management
Dynamic power management
  • Introduction
  • Implementation, levels of operation
  • Modeling
  • Power and performance issues regarding power management
  • Policies
  • Conclusions

Mehdi Amirijoo

introduction
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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introduction1
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

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implementation
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
Modeling
  • View the system as a set of interacting power-manageable components (PMCs), controlled by the power manager (PM).

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modeling1
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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modeling2
Modeling
  • Ex. StrongArm SA-1100 processor (Intel)

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power and performance issues
Power and performance issues..
  • Power management degrades performance.

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power and performance issues1
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

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policies
Policies
  • Different categories:
    • Predictive
    • Adaptive
    • Stochastic
  • Application dependent
  • Statistical properties
  • Resource requirements

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policies predictive
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 predictive1
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.

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policies predictive2
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

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policies adaptive
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 adaptive1
Policies - Adaptive
  • Low pass filter [Wu1997] :

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policies stochastic
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
Policies - Stochastic (Markov)
  • 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).
  • Stationary (or WSS). Statistical properties do not depend on the time shift, k.

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policies stochastic markov2
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 markov3
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
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.

Mehdi Amirijoo

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