Power-aware scheduling

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Power-aware scheduling

• Jan Madsen
• Informatics and Mathematical Modelling
• Technical University of Denmark
• Richard Petersens Plads, Building 321
• DK2800 Lyngby, Denmark
• Jan_at_imm.dtu.dk

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Mission critical embedded systems

• Based on work by
• J. Liu,
• P.H. Chou,
• N. Bagherzadeh,
• F. Kurdahi
• University of California, Irvine
• CODES01 DAC01

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Mars Rover Mission

• Perform experiments
• Autonomous mobile vehicle
• Alpha proton X-ray spectrometer
• Imaging
• Travel between different target locations

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Mars Rover Conditions

• Surface temperature -40 oC -80 oC
• Communication 11 minute
• No real-time control
• Supervised autonomous control

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Mars Rover - System composition

• CPU
• 3 images per day
• Motors
• 60 cm per min
• Hazard detection
• Heaters
• -80 oC requires motors to be heathed

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Mars Rover Power?

• Power sources
• Battery (non-rechargeable)
• Solar panel (free)
• Power consumers
• Digital imaging, communication, control
• Mechanical driving, steering
• Thermal heating motors in the low-temperature
  environment

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System-level power manager

• Amdalhs law applies to power
• Power savings of a component is scaled to its
  contribution to power usage of the whole system
• If a component draws 2 of the power in a system,
  a 50 power reduction amounts to 1 saving to the
  system
• The power manager must consider all power
  consumers in the entire system and identify the
  major power consumers

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System-level power manager

• System-level power consumers
• (Digital) computation domain
• Processors, memory, I/O, ASIC
• Non-computation domains
• Mechanical motors
• Thermal heaters
• Major power consumers mechanical and thermal

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Power-aware vs. low-power

• Low-power
• Minimize power usage
• Just enough power to meet performance requirement
• No distinction between costly power and free
  power
• Component-level power managers
• Power-aware
• Best use of available power
• Minimize power usage with low power budget
• Deliver high performance with high power budget
• Distinguish different models of power sources
• Battery, solar, nuclear, etc.
• Track variant power availability
• System-level power managers

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Low-power scheduling

• Shutting down subsystems
• Variable-voltage processor scheduling
• Limited applicability to power-aware designs
• Timing constraints are not strongly guaranteed
• Power usage is handled as a by-product
• No tracking to power availability
• No distinction to different energy sources

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Low-power scheduling - Example
r1
r1
r1
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Power-aware scheduling

• Min/max timing constraints on tasks
• Min timing constraint
• Subsumes precedence as special cases
• Max timing constraint
• Subsumes deadline as special cases
• Min/max power constraints on the system
• Max power constraint
• Total power budget from the available sources
• Hard constraint, must be guaranteed
• Min power
• Free power (solar), minimize power jitter
• soft constraint, best effort

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Constraint graph G(V, E)

• Vertices V tasks
• d(v), execution delay
• p(v), power consumption
• r(v), resource mapping

• Edges E timing constraints
• Forward edge min constraint
• Backward edge max constraint

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Constraint graph G(V, E)

• Schedule ?
• Time assignments to tasks
• Finish time ??

• Timing-valid schedule
• Timing constraints satisfied
• No resource conflict

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Power-aware Gantt chart

• Time view
• Bins tasks
• Horizontal axis start time, duration
• Vertical axis power
• Tracks parallel resources

• Power view
• Power profile
• Power constraints
• Power properties
• Spikes, gaps
• Energy cost
• Utilization

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Mars Rover - Exercise
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Mars Rover - Exercise
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Mars Rover - Solution
Worst case at 80 oC
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Power properties

• Power profile P?(t)
• System-level power consumption curve
• Power constraints
• Max power constraint Pmax
• Power Spike max power constraint violation
• Min power constraint Pmin
• Power Gap min power constraint violation
• Power-validity
• A timing-valid schedule with no power spikes
• Enforce max power budget

• Min power utilization ??(Pmin)
• Energy utilization from free sources
• Energy cost Ec?(Pmin)
• Energy drawn from expensive (non-free) sources
• Power-aware trade-off
• Performance ?? vs. Energy cost Ec?(Pmin)

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Mars Rover Power profile
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10
??(Pmin)
95.2
Ec?(Pmin)
3.4
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Mars Rover the real thing!

• Timing constraints
• Three cases w/ different power constraints
• Max power
• solar 10W
• Min power
• solar, free
• Best 14.9W
• Typical 12W
• Worst 9W

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Scheduling results

• Worst case
• Slower, high cost
• Same as the existing serial schedule

• Best case
• Fast, low cost
• Typical case
• Slower, increased cost

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Comparisons to schedules

• Existing low-power schedule
• Low performance
• Low energy cost
• Under-utilized free solar power
• Does not track power sources
• Full serialization by hand-crafting

• Power-aware schedules
• High performance
• High energy cost
• Improved utilization of solar power
• Tracks available power from different sources
• Fully constraint-driven by an automated design
  tool

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Comparisons in a scenario

• Scenario
• Mission travel to a target 48 steps away
• Existing low-power schedule
• Fixed slow speed
• Low energy cost in each phase, but high energy
  cost in worst case
• Low performance, high energy cost

• 3 phases best, typical, worst, 10 min each
• Power-aware schedules
• Accelerated speed by tracking available power
• Finish earlier before working in the worst case
• High performance, low energy cost

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Conclusion

• Power-aware design
• Different from low-power
• Deliver high performance by tracking power
  sources
• Power-aware schedulers
• Incremental scheduling by constraint
  classification
• Potentials on performance speedup and energy
  saving
• System-level design tools
• Power manager for the entire system
• Aggressive design space exploration

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Incremental scheduling (1)

• (1) Timing scheduling
• Topological traversal of the constraint graph
• Selective serialize tasks that share the same
  resource
• Prohibit positive cycles
• Proven to find a timing-valid schedule

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Incremental scheduling (2)

• (2) Max power scheduling
• Begin with a timing-valid schedule from (1)
• Enforce max power constraint
• Reorder tasks to eliminate power spikes
• Redo (1) for timing violation
• Heuristics applied

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Incremental scheduling (3)

• (3) Min power scheduling
• Begin with a power-valid schedule from (2)
• Reorder tasks to reduce power gaps in best-effort
• Deliver same performance with less energy cost
• Heuristics applied
• Results applicable to different constraints