# Power-aware scheduling

Document Sample

```					courseware

Power-aware scheduling

Informatics and Mathematical Modelling
Technical University of Denmark
DK2800 Lyngby, Denmark
Jan@imm.dtu.dk
Mission critical embedded systems

 Based on work by
   J. Liu,
   P.H. Chou,
   F. Kurdahi
 University of
California, Irvine
 CODES’01 &
DAC’01

Mars Rover – Mission

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

Mars Rover – Conditions

 Surface temperature [-40 oC; -80 oC]
 Communication ~ 11 minute
 No real-time control
 Supervised autonomous control

Mars Rover - System composition

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

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

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

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

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

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

Low-power scheduling - Example

p1       r1                             r1
p2                                                         r1
p3

r1 idle               r1 idle
p1       r1           r1
p2                                                              r1
p3

r1 idle
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

Constraint graph G(V, E)

 Vertices V: tasks                    Edges E: timing
 d(v), execution delay            constraints
 p(v), power consumption            Forward edge: min
 r(v), resource mapping              constraint
 Backward edge: max
constraint

Constraint graph G(V, E)

 Schedule                            Timing-valid schedule
 Time assignments to tasks          Timing constraints satisfied
 Finish time                      No resource conflict

Power-aware Gantt chart

 Time view                             Power view
 Bins – tasks                      Power profile
 Horizontal axis – start      Power constraints
time, duration
 Power properties
 Vertical axis – power
 Spikes, gaps
 Tracks – parallel                    Energy cost
resources                            Utilization

Mars Rover - Exercise

Mars Rover - Exercise

Power sources &      Duration     Power @ -40 oC   Power @ -60 oC   Power @ -80 oC
Solar panel                             17              14                11

Battery pack                          8 max            8 max            8 max

CPU                  Constant           2                3                4

Heating two motors      5               8               10               12

Driving                 10              8                11              14

Steering                5               4                6                8

Hazard detection        10              3                4                5

Mars Rover - Solution

Worst case at –80 oC

Hd
St
Dr
HW12
HW34
HW56
HS12
HS34
CPU

Power   9    9    16    16    16   16     16   12   18   18   9   9   12   18   18

Power properties
 Power profile P(t)                     Min power utilization
 System-level power
consumption curve                   (Pmin)
 Power constraints                          Energy utilization from free
 Max power constraint Pmax              sources
 Power Spike: max power
constraint violation         Energy cost Ec(Pmin)
 Min power constraint Pmin
 Power Gap: min power            Energy drawn from
constraint violation             expensive (non-free)
 Power-validity                              sources
 A timing-valid schedule with no
 Enforce max power budget
 Performance  vs. Energy
cost Ec(Pmin)

Mars Rover – Power profile

Pmax
20
P(t)

Pmin
10

(11 x 75) – (2 x 2 x 10)
(Pmin) =                                 = 95.2 %
(11 x 75)

5x25+5x1+10x7+5x1+10x7
Ec(Pmin) =                            = 3.4
75

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

Scheduling results

 Best case
 Fast, low cost

 Worst case
 Typical case                       Slower, high cost
 Slower, increased cost       Same as the existing
serial schedule

Comparisons to schedules

 Existing low-power                   Power-aware schedules
schedule                               High performance
 Low performance                  High energy cost
 Low energy cost                  Improved utilization of
 Under-utilized free solar         solar power
power                            Tracks available power
 Does not track power              from different sources
sources                          Fully constraint-driven by
 Full serialization by hand-       an automated design tool
crafting

Comparisons in a scenario

 Scenario
 Mission: travel to a target      3 phases: best, typical,
48 steps away                     worst, 10 min each
 Existing low-power                   Power-aware schedules
schedule                               Accelerated speed by
 Fixed slow speed                  tracking available power
 Low energy cost in each          Finish earlier before
phase, but high energy            working in the worst case
cost in worst case               High performance, low
 Low performance, high             energy cost
energy cost

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

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

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

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

```
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
 views: 9 posted: 2/28/2012 language: English pages: 28