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-Based Workload Estimation for Mobile 3D Graphics. Bren Mochocki* † , Kanishka Lahiri*, Srihari Cadambi*, Xiaobo Sharon Hu † *NEC Laboratories America, † University of Notre Dame. DAC 2006. Mobile Graphics Technology. 2000. 2001. 2002. 2003. 2004. 2005. 2006. - PowerPoint PPT Presentation
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2
-Based Workload
Estimation for Mobile 3D Graphics
Bren Mochocki*†, Kanishka Lahiri*, Srihari Cadambi*, Xiaobo Sharon Hu†
*NEC Laboratories America, †University of Notre Dame
DAC 2006
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Mobile Graphics Technology
2000 2001 2002 2003 2004 2005 2006 2007
Basic 3D
Graphics Technology
Video clips
Advanced 3D
1997
2D color
Time
Increasing resource load • Performance (Speed)• Lifetime (Energy)
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Meeting Performance/Lifetime Requirements
System - Level Optimizations
Graphics Algorithms
Hardware Solutions
Tack, 04• LoD control for mobile terminals
Kameyama, 03• low-power 3D ASIC
Woo, 04• low-power 3D ASIC
Akenine-Moller, 03• Texture compression for mobile terminalsMochocki, Lahiri, Cadambi, 06
• DVFS for mobile 3D graphics
Accurate workload prediction is critical
Gu, Chakraborty, Ooi, 06• Games are up for DVFS
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Mobile 3D Workload EstimationWhy?
Adapt architectural parameters Adapt application parameters Better on-line resource management
Desirable properties Speed – must be performed on-line Accuracy Compact
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Workload-Estimation Spectrum
General purpose history-based predictors provide poor prediction accuracy for rapidly changing workloads
Highly accurate analytical schemes are too complex for use at run time
General Purpose
SimplicitySimplicity
Application specific
AccuracyAccuracy
History-Based Predictors
Analytical Predictors
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Workload-Estimation Spectrum
Uses combination of history and application-specific parameters (the signature) to predict future workload
Strikes a balance between simplicity and accuracyPreserves both cause AND effect Preserves substantial history
General Purpose
SimplicitySimplicity
Application specific
AccuracyAccuracy
Signature-Based Predictor
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OutlineIntroduction and MotivationBackground
3D-pipeline Basics Challenges in workload Estimation
Signature-Based Workload PredictionExperimental ResultsConclusions
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3D Pipeline Basics3D representation 2D image
World View Camera View Raster View Frame Buffer
Geometry Setup Rendering
• Transformations• Lighting
• Clipping• Scan-line conversion
• Pixel rendering• Texturing
Texturing
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Workload Across Applications
Workload varies significantly between applicationsPrediction scheme must be flexible
RoomRevTexCube
0
2
4
6
8
10
12
Exec
utio
n C
ycle
s (A
RM
, x10
7 )
Benchmark
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Workload Within an ApplicationWorkload can change rapidly between frames
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1
2
3
4
5
6
1 16 31 46 61 76 91 106 121 136 151 166 181 196
Exec
utio
n C
ycle
s (A
RM
, x10
7 )
Frame
geometry
render
setup
Race
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OutlineIntroduction and MotivationBackgroundSignature-Based Workload Prediction
Signature Generation Method Overview Pipeline Modifications
Experimental ResultsConclusions
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Example
SignatureTable
Application Frame Buffer
Workload Prediction
Signature Workload
<6, 2.5> 1.0e4extract
signaturemeasureworkload
Default
endframe
extract
Signature: <vertex count, avg. area>
3D Pipeline3D Pipeline
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Example
SignatureTable
Application Frame Buffer
Workload Prediction
Signature Workload
<6, 2.5>
<6, 2.5> 1.0e4extract
signaturemeasureworkload
1.0e41.0e4
endframe
extract
3D Pipeline3D PipelineSignature: <vertex count, avg. area>
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Example
SignatureTable
Application Frame Buffer
Workload Prediction
Signature Workload
<6, 2.5>
<6, 2.5> 1.2e4extract
signaturemeasureworkload
1.0e41.0e4
endframe
extractNo overlap (render all pixels)
3D Pipeline3D PipelineSignature: <vertex count, avg. area>
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TransformTransform ClippingClippingLightingLighting Scan-lineScan-lineconversionconversion
Per-pixelPer-pixelOperationsOperationsLighting Scan-line
conversionPer-pixel
OperationsTransform Clipping
ApplicationApplication DisplayDisplay
Partitioning the 3D pipeline
GEOMETRY SETUP RENDER
ApplicationApplication DisplayDisplay
• Generally small workload• Provides necessary signature elements
Bulk of 3D workload
Transform+
Clipping
Scan-lineconversion
Per-pixelOperationsLightingBuffer
ORIGINAL
PARTITIONED
Pre-Buffer Post Buffer
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Pipeline Workload
Pre-buffer workload is less than 10% of the total workload
Pre-buffer variation is small
Post-buffer workload is large with significant variation
post-bufferpre-buffer
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Signature Composition Can vary by application May include:
1. Average Triangle Area2. Average Triangle Height3. Total vertex count4. Lit vertex count5. Number of lights6. Any measurable parameter
Larger signatures more accurate Smaller signatures less time & space
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OutlineIntroduction & BackgroundExperimental FrameworkSignature-Based Workload PredictionExperimental Results
Evaluation Framework Signature length vs. accuracy Frame Rate Energy
Conclusions
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Architectural View
Programmable 3D Graphics
Engine
Frame Buffer
Performance counter
Memory
Applications Processor
System-level Communication Architecture
Prog. Voltage Regulator
Prog. PLL
V, F
• buffer• signature table
• pre-buffer• signature extraction
post-buffer
output
measure workload
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Evaluation FrameworkOpenGL/ES library Instrumented withpipeline stage triggers
Hans-Martin WillFast, cycle-accurateSimulation
W. Qin
Trace simulator of mobile 3D pipeline
OpenGL/ES 1.0 3D – application
3D pipeline Performance/power
Simit-ARM
Cross CompilerARM — g++
Trace Simulator
Triangle,Instruction, &Trigger traces
Workload predictionscheme
3D application
Vincent
ProcessorEnergy Model
Architecture Model
Simulation output
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Workload AccuracyA
vera
ge E
rror
(nor
mal
ized
)
<a>2 bytes
<a,b>6 bytes
<a,b,c>10 bytes
<a,b,c,d>14 bytes
Signature Complexity
> 2 fps error at peaks
Peaks < 1 fps
<a> triangle count, <b> avg. area, <c> avg. height, <d> vertex count
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Frame Rate
High peaks result in wasted energy
Low valleys result in poor visual quality
Target
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Workload prediction for DVFS
Before DVFS DVFS using signature-based workload Prediction
32% energy reduction
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OutlineIntroduction & BackgroundExperimental FrameworkSignature-Based Workload PredictionExperimental ResultsConclusions
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ConclusionsAccurate 3D workload prediction critical
for mobile platforms.Proposed signature-based method
Outperforms conventional history methods Trade accuracy for time & space
Can be used to meet real time constraints and conserve energy.
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Future WorkAutomatic selection of signature elementsMore sophisticated data structures for
signature storageFaster comparison and replacement
algorithms
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-Based Workload
Estimation for Mobile 3D Graphics
Bren Mochocki*†, Kanishka Lahiri*, Srihari Cadambi*, Xiaobo Sharon Hu†
*NEC Laboratories America, †University of Notre Dame
DAC 2006
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