Application Transformations for Energy and PerformanceAware Device Management

1
Application Transformations for Energy and
Performance-Aware Device Management

• Taliver Heath, Eduardo Pinheiro, Jerry Hom,
  Ulrich Kremer, and Ricardo Bianchini
• Rutgers University

2
The Problem

• Conserve energy in devices
• Must take advantage of lower power states
• State transitions have overhead
• Cost in both energy and performance
• Challenge non-interactive applications and fast
  processors
• Short device idle times
• Devices cannot use lower power states

3
Our Solution
An Unmodified Application (UM)
CPU
Disk
CPU
idle
idle
active
active
Disk
idle
idle
active
active
Transformed Application
4
Our Goals

• Conserve energy by exploiting transformations
  that increase idle time
• Evaluate ideas using
• Hand-modified programs
• Automated compiler transformations
• Specific policies Energy Oblivious, Fixed
  Threshold, Direct Deactivation, Pre-Activation,
  and Combined

5
Application Transformations

• Increase idle times with help of compiler or
  programmer
• Identify loops where accesses occur
• Perform loop transformations
• Estimate device idle times
• Insert system calls
• Idle time limited by memory or real-time
  constraints

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Example Original Application
i 1 while i lt N read chunki of
file compute on chunki i i1
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Example Transformed Application
available how_much_memory() numchunks
available/sizeof(chunks) compute_time
appfunc(numchunks) i 1 while i lt N read
chunkiinumchunks of file next_R(compute_time
) compute on chunkiinumchunks i
inumchunks
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Compiler Framework

• Annotations to file descriptors
• Replace disk calls using interprocedural analysis
• Profiling
• Buffered I/O
• Notify OS of idle times
• Based on SUIF infrastructure

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Device Management

• Energy-Oblivious (EO)
• Fixed-Threshold (FT)
• Direct-Deactivation (DD)
• Pre-Activation (PA)
• Combined(CO) DDPA
• Final state based on model Heath02

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Sample Disk Power Graphs (mp3 player)
UM
FT
CO
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Experimental Setup

• Fujitsu Disk
• 6-GB, 4200-rpm laptop disk
• 3 states
• Idle 0.9 W
• Standby 0.22 W
• Sleep - 0.09W
• Buffer memory available 19MB
• Time allowed for reading .3 seconds

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Experiment

• 6 applications
• Mp3 player, mpeg-player
• Gzip, sftp, mpeg-encode, image smoother
• Variables investigated
• Disk management policies
• Compiler vs. hand-optimized
• OS prefetching on/off

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Non-streaming SFTP
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Streaming MP3 player
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Average Hand-Modified Results
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Average Compiler Results
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Related Work (partial list)

• Application-controlled power states
• Concept, but no implementation Ellis99,Lu99
• Compiler infrastructure Delaluz01
• Direct deactivation and preactivation
  Hom01,Heath02
• Conserving disk energy Douglis94
• Modifying disk access API Weissel02

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Conclusions

• Application transformations
• 55-89 savings in energy
• Minimal effect on performance
• Idle time predictions are difficult
• Prefetching has little impact
• Compiler transformations work well
• As good as hand modifications
• Generic framework other disks and devices

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For more information

• www.darklab.rutgers.edu

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Technique

• Create model of disk energy
• Transform applications
• Realize model on real disk
• Predict disk energy usage
• Measure disk on 4 applications

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Future Work

• More disks
• Other devices
• Multiple active processes
• Asynchronous I/O

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Summary
23
Historical Use of States

• Change to Lower State during Period of Idleness
• Fixed-threshold
• Adaptive/Heuristic
• OS Hints
• Based on general knowledge of system

24
Runlength vs. Energy
25
Projected Application Gain
26
Projected Application Gain
27
Overhead for DD
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Combined (CO)
CPU
idle
idle
active
active
Disk
idle
active
active
idle
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Parameter Description
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Reality Departs from Model

• Hidden states in several transitions
• Transition from active to idle
• Behavior on activation

For CO
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Experiments
Modified App Runlengths
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Energy, mpg123
33
Energy, sftp
34
Performance, mpg123
35
Performace, sftp
36
Experimental Results
MP3 player
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Summary

• direct-deactivation and preactivation (CO)
• Can save up to 89 of disk energy
• No performance penalty, except for MPEG player
  (lt10)
• Just increasing runlengths, we can save up to 50
  energy
• Error in model can be significant up to 50 for
  the entire application

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Energy Oblivious(EO)
CPU
idle
idle
active
active
Disk
idle
39
Direct Deactivation(DD)
CPU
idle
idle
active
active
Disk
40
Pre-Activation(PA)
CPU
idle
idle
active
active
Disk
idle
41
Fixed-Threshold(FT)
CPU
idle
idle
active
active
Disk
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Terminology
Runlength (R)
Time between device accesses by the processor
R
R
Device Time
CPU Time
Blocking device accesses (reads) Single
ready-to-run application