Automatic Performance Setting for Dynamic Voltage...

Krisztián Flautner - [email protected] Performance Setting for Dynamic Voltage Scaling 1

Automatic Performance Setting for Dynamic Voltage Scaling

Krisztián [email protected]

Steve ReinhardtTrevor Mudge


Overview• A mechanism for quantifying the user experience.

– Metric: response time.– Automatic, no user program modifications required.– Run-time feedback to the kernel.

• Guiding performance setting of DVS processors.– For interactive episodes: slow down processor to save

energy when response times are fast enough.– For periodic events: track periodicity, utilization and inter-

task communication to establish necessary performance.

• Simulated and experimental results.


Dynamic Voltage Scaling

• Voltage is proportional to the frequency.• Reduce f and v to match performance demands.• Reduced frequency implies longer execution time.

Power = Capacitance • voltage2 • frequency

Energy ~ voltage2

Execute only as fast as necessary to meet deadlines.Running fast and idling is not energy efficient.


Why bother?

386 386

486 486

Pentium(R) Pentium(R) MMX

Pentium Pro (R)

Pentium II (R)

1

10

100

1.5µ1.5µ1.5µ1.5µ 1µ1µ1µ1µ 0.8µ0.8µ0.8µ0.8µ 0.6µ0.6µ0.6µ0.6µ 0.35µ0.35µ0.35µ0.35µ 0.25µ0.25µ0.25µ0.25µ 0.18µ0.18µ0.18µ0.18µ 0.13µ0.13µ0.13µ0.13µ

Max

Pow

er (W

atts

) ?

Sour

ce: I

ntel

Higher performance = increased power consumption.


Power Density!

1

10

100

1000

1.5µ1.5µ1.5µ1.5µ 0.8µ0.8µ0.8µ0.8µ 0.35µ0.35µ0.35µ0.35µ 0.18µ0.18µ0.18µ0.18µ 0.1µ0.1µ0.1µ0.1µ

Wat

ts/c

m2

Hot plate

Nuclear Reactor

RocketNozzle Sun’s

Surface?

Sour

ce: I

ntel


Small performance reduction = big energy savings

20% performance reduction = 32% energy reduction40% performance reduction = 55% energy reduction

0

0.4

0.8

1.2

1.6

2

0 200 400 600 800 1000 1200

Frequency (Mhz)

Volta

ge (V

)

0

0.2

0.4

0.6

0.8

1Energy factor

Graph based onIntel XScale data


Processors supporting DVS

5.4

0.18

1000Mhz1.75V1.45W

150Mhz0.75V40mW

Intel XScale Demo

4

0.18

800Mhz1.5V

900mW

150Mhz0.75V40mW

Intel XScale

1.84.49Max/min energy

0.180.350.6Process

700Mhz1.6V~2W

251Mhz1.65V

964mW

100Mhz3.3V

220mWMax.

500Mhz1.2V~1W

59Mhz0.79V

106mW

8Mhz1.1V

1.8mWMin.

Transmeta Crusoe 5600Intel SA-1100lpARM


Some recent desktop processors

38W66W

0.18

200Mhz, 266Mhz1.6V

650Mhz @ 1.75V1.2Ghz @ 1.75V

AMD AthlonModel 4

12W19.1W

0.18

100Mhz, 133Mhz3.3V

500Mhz @ 1.35V733Mhz @ 1.65V

Intel Pentium III

17W19.1W

0.18

133Mhz1.8V-2.5V

533Mhz @ 1.8V667Mhz @ 1.8V

MPC 7450

66.3W

0.18

400Mhz

1.4Ghz @ 1.7V

Intel Pentium IV

Max. Power

Process

I/O

Core


Performance setting algorithms• Programmer specified

– Works well but requires explicit specification of deadlines.• Interval based algorithms

– Use the ratio of idle to busy time to guide DVS.– Only work well if processor utilization is regular.– No service quality guarantees.

• Ours: episode classification based– Find important execution episodes – predict their performance.– Works with existing user programs.– Works well with irregular workloads.– Uses information in kernel to derive deadlines automatically.– Impact on response time is automatically quantified.

• Performance can be adapted to the user’s preference.


Episode classification

• Interactive episodes– When the user is waiting for the computer to respond.

• Periodic episodes– Producer (e.g. MP3 player).– Consumer (e.g. sound daemon).


A utilization trace

Each horizontal quantum is a millisecond, height corresponds to the utilization in that quantum.


Episode classification

Interactive (Acrobat Reader), Producer (MP3 playback), and Consumer (esd sound daemon) episodes.


Mouse movement

X server updates screen every ~10ms. Update takes ~0.25ms.


Interactive episodes


Interactive episodes can include idle time

Waiting for data from the network during a run of Netscape. Page rendering starts after 250ms.


Finding interactive episodes

• One way: mouse click indicates start, idle time indicates end.– Inaccurate, latency in finding the end of the episode.

• Our approach: track inter-task communication.– Start of an interactive episode:

• X server sends a message to another task.– During interactive episode:

• Keep track of communicating tasks (episode’s task set).• Compute desired metrics.

– Conditions for ending the episode (applied to tasks in task set):• No tasks are executing.• Data written by the tasks have been consumed.• No task was preempted the last time it ran.• No tasks are blocked on I/O.


Characteristics of Interactive Episodes

• Faster is not necessarily better.– Human perception has finite resolution.– Perception threshold is ~50ms.– The goal is to run fast enough to meet the perception

threshold, no point to running any faster.

• Many interactive episodes are already fast enough.• More will be imperceptible in the near future.

– 200ms perception threshold today estimates work done during 50ms 3 years from now.

Slow down the processor!


Time above the perception threshold

0%

20%

40%

60%

80%

100%

50ms 100ms 150ms 200ms 250ms 300ms

Perception threshold

Tim

e a

bove

the

perc

eptio

n th

resh

old

Acrobat Reader

FrameMaker

GhostviewGIMP

Netscape

Time above the perception threshold is given as a percentage of time spent in all interactive episodes.


The key: performance-setting algorithm

• Use episode detection and classification.– Interactive episodes.– Periodic episodes (producer and consumer).

• Performance-setting on a per episode basis.• Stretch episodes to their deadlines.

– Interactive episode: perception threshold.– Stretch producer to consumer.

No modification of existing programs needed.Works with irregular processor utilization and multiprogramming.


Cumulative interactive episode length distributionFr

ameM

aker

Episode length (sec)

Cumulative numberCumulative time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1e-05 0.0001 0.001 0.01 0.1 1

50ms10ms

Minimum performance level sufficient Max. performance


Performance-setting strategy for interactive episodes

• Predict the performance factor that would be correct most of the time (not for most events).– Based on past optimal performance factors.

• Limit worst case impact on response time.– Run at full performance after PanicThreshold is reached.


Performance-setting for interactive episodes

• Wait 5ms before transition to ignore short episodes• Switch to predicted performance level.

• If episode duration reaches PanicThreshold, switch to maximum performance.

• Estimate full performance episode duration.• Compute optimum performance level for past episode.• Compute new prediction based on optimum settings.

At the beginning of the episode

During the episode

At the end of the episode

PanicThreshold = PerceptionThreshold(1 + PerformanceFactor)Predicted PerformanceFactor is the average of past optimum settings, weighted by the corresponding episode lengths.


Performance-setting algorithm

• Enter period-sampling mode.• Switch to maximum performance.• Establish base performance level.• Exit period-sampling mode.

Periodic activity detected

• If not in period-sampling mode, apply interactive episode performance-setting policy.

Start of interactive episode

• Update interactive episode statistics.• Switch to base performance level, if there is periodic

activity on the machine.

End of interactive episode


Performance-setting during the Acrobat Reader benchmark (200ms p.t.)

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16 18104 124

Time (sec)

Perfo

rman

ce fa

ctor

Transitions to maximum performance level are due to reaching the PanicThreshold


Performance-setting during the Acrobat Reader + MP3 benchmark (200ms p.t.)

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20

Time (sec)

Perfo

rman

ce fa

ctor

Transitions due to PanicThreshold

Full performance for periodic activity.


Hardware assumptions

1msVoltage transition time

0.02msPLL resynch time (stalls execution)

1000Mhz @ 1.75VMaximum performance

150Mhz @ 0.75VMinimum performance

Assumptions based on Intel Xscale.

We assume that processor switches to sleep mode when it is not executing an episode.


Energy factors (no MP3)

0%

20%

40%

60%

80%

100%

50ms 100ms 150ms 200ms 250ms 300ms


Ener

gy fa

ctor

Acroread FrameMakerGhostview GIMPNetscape Xemacs


Energy factors with MP3 playback

0%

20%

40%

60%

80%

100%

50ms 100ms 150ms 200ms 250ms 300ms


Ener

gy fa

ctor

Acroread FrameMakerGhostview GIMPNetscape Xemacs


Changes in cumulative episode lengths as the result of performance scaling (Xemacs 50ms p.t. )

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1e-05 0.0001 0.001 0.01 0.1 1

50ms10ms


Bef

ore

perf

orm

ance

sca

ling A

fter performance scaling

Cum

ulat

ive

perc

enta

ge o

f tim

e


Vertigo

• A DVS implementation for Linux 2.4 kernel.• Currently runs on Transmeta Crusoe.

– Test machine: Sony PictureBook (PCG-C1VN) using TM5600 processor (300Mhz-600Mhz).

Goals:• Robust implementation.• Evaluate our algorithms on computers with DVS.• Contrast with conventional DVS algorithm (LongRun).


Vertigo implementation

• Some kernel modification required (~20 lines):– Socket, inode, task_struct datastructures, task create/exit notification.

• Episode detection done in kernel module.• System calls dynamically patched through syscall table.

Vertigod daemon

•User-mode process.•Implements DVS policy.

User processes

•Monitored through kernel hooks.•System calls•Task switch/create/exit

•Can specify hints.

Kernel

Hooks

Vertigo Module

•Episode detection & tracking.•Comm. with policy daemon.•Event tracing.•/proc interface.


Vertigo implementation issues• Need millisecond resolution timer interrupts.

– Linux has 10ms resolution.– Generate extra “fake” interrupts from kernel hooks.

• Need constant-rate timestamp counter.– Always count at peak frequency rate, even when asleep.– Transmeta Crusoe does this.– Intel XScale does not. Need to query external clock.

• Policy implemented in user-mode process.– Flexible, can do floating point arithmetic.– Communication cost very platform dependent.

• Pentium II: ~6,000 cycles, Crusoe: >60,000 cycles.– API designed to minimize communication.– Move communication off the critical path (to after critical episodes).


Vertigo vs. LongRun• LongRun: implemented as part of the processor.

– Interval based algorithm (guided by busy vs. idle time).– Min. and max. range is controllable in software.

• Vertigo: implemented in OS kernel.– Classification based algorithm.– Distinguishes important from unimportant parts of execution.– Takes the quality of the user experience into account.

• Qualitative comparison on following graphs.– The two runs of the benchmarks are close but not identical.

• Human repeated the runs of the benchmark.– Transitions to sleep are not shown.– Same perceived interactive performance.


No user activity

Time (s)

Perf

orm

ance

leve

lPe

rfor

man

ce le

vel

Time (s)

LongRun

Vertigo

Frequency range of the TM5600 processor.

50% = 300Mhz @ 1.3V

100% = 600Mhz @ 1.6V

Max. energy savings that should beexpected on this processor is ~34%.


Emacs

Time (s)

Perf

orm

ance

leve

lPe

rfor

man

ce le

vel

Time (s)

LongRun

Vertigo


Acrobat Reader

Time (s)

Perf

orm

ance

leve

lPe

rfor

man

cele

vel

Time (s)

LongRun

Vertigo


Acrobat Reader with sleep transitions

Time (s)

Perf

orm

ance

leve

lPe

rfor

man

cele

vel

Time (s)

LongRun

Vertigo

Frequent transitions to/from sleep mode. Longer durations without sleeping.


Desired improvements

• Processor parameters are good enough.– Faster voltage transitions would help a little.– As peak performance gets higher, lower minimum

performance is desirable.

• More sophisticated prediction algorithms.– Distinguish between episode instances, not just episode

types.

• Larger performance range for DVS processor.– Puts more pressure on performance-setting algorithm.– More opportunity for energy savings.


Conclusions

• Many interactive episodes are already fast enough.– More will be fast enough in the near future.– Use Dynamic Voltage Scaling to save energy.

• Episode classification based on inter-task communication.– Fast, accurate, no user program modifications required.

• Performance-setting based on episode classification.– Works well with multiprogramming, irregular processor utilization.– Ensures high quality interactive performance.– Significant energy savings (10%-80%).


Future work

• Evaluate our algorithms on real hardware.– Processors are slowly becoming available.– Impact on interactive performance.

• An API to specify episodes.– Light-weight: specify hints, not complete information.– Works in concert with existing detection mechanism.

• Apply episode detection to other problems.– Scheduler: can real-time deadlines be detected

automatically?


fin.fin.fin.fin.


Response time

• Faster is not always better.– Fundamental limit to what is perceptible to humans.

• Movies: 20-30 frames per second.• Perceptual causality: 50ms-100ms.• Dragging objects on screen: 200ms.• Non-continuous operation: 1-2sec.

The time it takes for the computer to respond to user initiated events.

The goal is to run fast enough to meet the perception threshold, no point to running any faster.


The performance gap

1

10

100

1000

10000

100000

0 1.5 3 4.5 6 7.5 9Time (years)

Perf

orm

ance

Available performancestarts accommodatingrequirements (A).

Desired performance

Available P erformance

All performancerequirements are met (B).

Slowest availableperformance exceedsminimum requirements (C).

Available performanceis higher than required (D).


Cumulative interactive episode length distributionX

emac

s


Cumulative numberCumulative time

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1e-05 0.0001 0.001 0.01 0.1 1

50ms10ms

Minimum performance level sufficient Max. performance


Communication between tasksCPU 1CPU 0

89 5

75 7

75 7

75 7778

778

88989 575 72088

75 7

R

R

R

W

W

W

W

W

W

W

CPU 1C PU 0

757

757209 0

757

W

W

W

757 W

757W

757 W

757 W


Producer and consumer episodes

• Example: MP3 playback through esd sound daemon.• Monitor communications to/from sound daemon.• Distance between producer and consumer episodes determines

necessary performance level.

Sound daemon

MP3 player

HW sound device

Date post:	29-Aug-2018
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Automatic Performance Setting for Dynamic Voltage...

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