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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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Mobile Graphics Technology
Increasing resource load Performance (Speed) Lifetime (Energy) Graphics Technology Advanced 3D Basic 3D Video clips 2D color 1997 2000 2001 2002 2003 2004 2005 2006 2007 Time
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Meeting Performance/Lifetime Requirements
Hardware Solutions Woo, 04 low-power 3D ASIC Kameyama, 03 low-power 3D ASIC Gu, Chakraborty, Ooi, 06 Games are up for DVFS Akenine-Moller, 03 Texture compression for mobile terminals Mochocki, Lahiri, Cadambi, 06 DVFS for mobile 3D graphics Keep short Stick to title Some can use workload prediction System - Level Optimizations Graphics Algorithms Tack, 04 LoD control for mobile terminals Accurate workload prediction is critical
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Mobile 3D Workload Estimation
Why? 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 Simplicity Application specific Accuracy History-Based Predictors Analytical Predictors 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
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Workload-Estimation Spectrum
General Purpose Simplicity Application specific Accuracy Signature-Based Predictor Uses combination of history and application-specific parameters (the signature) to predict future workload Strikes a balance between simplicity and accuracy Preserves both cause AND effect Preserves substantial history
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Outline Introduction and Motivation Background
3D-pipeline Basics Challenges in workload Estimation Signature-Based Workload Prediction Experimental Results Conclusions
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3D Pipeline Basics 3D representation 2D image Geometry Setup
Texturing Geometry Setup Rendering High level simplified view of 3D graphics pipeline Practice describing rendering stage quickly (hidden surface removal) World View Camera View Raster View Frame Buffer Transformations Lighting Clipping Scan-line conversion Pixel rendering Texturing
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Workload Across Applications
12 TexCube RoomRev 10 8 Execution Cycles (ARM, x107) 6 4 2 Motivate the source of the pipeline imbalance Benchmark Workload varies significantly between applications Prediction scheme must be flexible
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Workload Within an Application
Workload can change rapidly between frames 1 2 3 4 5 6 Race geometry render Execution Cycles (ARM, x107) setup Start animation right away This motivates DVFS, but first Transition: what impacts the workload variation? 1 16 31 46 61 76 91 106 121 136 151 166 181 196 Frame
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Outline Introduction and Motivation Background
Signature-Based Workload Prediction Signature Generation Method Overview Pipeline Modifications Experimental Results Conclusions
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Example Signature: <vertex count, avg. area> 3D Pipeline extract
end frame Frame Buffer Application extract extract signature measure workload <6, 2.5> 1.0e4 -Workload we want to predict (last box) -Default prediction (Static analysis) Success slide after this one Signature Workload Default Signature Table Workload Prediction
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Example Signature: <vertex count, avg. area> 3D Pipeline
end frame Frame Buffer Application extract extract signature measure workload <6, 2.5> 1.0e4 1. More parameters implies fewer collisions 2. Exact match not necessary – Distance measurements can be used 3. Initial signature table can be included with the application Signature Workload <6, 2.5> 1.0e4 1.0e4 Signature Table Workload Prediction
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Example Signature: <vertex count, avg. area> 3D Pipeline
end frame Frame Buffer Application No overlap (render all pixels) extract extract signature measure workload <6, 2.5> 1.2e4 1. More parameters implies fewer collisions 2. Exact match not necessary – Distance measurements can be used 3. Initial signature table can be included with the application Signature Workload <6, 2.5> 1.0e4 1.0e4 Signature Table Workload Prediction
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Partitioning the 3D pipeline
Bulk of 3D workload ORIGINAL GEOMETRY SETUP RENDER Application Display Transform Transform Lighting Lighting Clipping Clipping Scan-line conversion Scan-line conversion Per-pixel Operations Per-pixel Operations Generally small workload Provides necessary signature elements Pre buffer, post buffer PARTITIONED Application Display Transform + Clipping Buffer Lighting Scan-line conversion Per-pixel Operations 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-buffer pre-buffer Red lines are min-max range
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Signature Composition
Can vary by application May include: Average Triangle Area Average Triangle Height Total vertex count Lit vertex count Number of lights Any measurable parameter Larger signatures more accurate Smaller signatures less time & space
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Outline Introduction & Background Experimental Framework
Signature-Based Workload Prediction Experimental Results Evaluation Framework Signature length vs. accuracy Frame Rate Energy Conclusions
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System-level Communication Architecture
Architectural View pre-buffer signature extraction post-buffer Prog. Voltage Regulator Prog. PLL V, F Applications Processor Programmable 3D Graphics Engine Performance counter System-level Communication Architecture Animate Voltage regulation (for example, we could…) Memory Frame Buffer measure workload buffer signature table output
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Evaluation Framework OpenGL/ES library Instrumented with
pipeline stage triggers Hans-Martin Will Vincent Fast, cycle-accurate Simulation W. Qin Cross Compiler ARM — g++ 3D application Simit-ARM OpenGL/ES 1.0 3D – application Trace simulator of mobile 3D pipeline Triangle, Instruction, & Trigger traces Workload prediction scheme Architecture Model Trace Simulator Processor Energy Model 3D pipeline Performance/power Simulation output
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Workload Accuracy > 2 fps error at peaks Average Error (normalized)
Peaks < 1 fps Even simple signatures can give a good prediction (3 or 4 four well chosen signatures) Signature must be chosen well <a> 2 bytes <a,b> 6 bytes <a,b,c> 10 bytes <a,b,c,d> 14 bytes Signature Complexity <a> triangle count, <b> avg. area, <c> avg. height, <d> vertex count
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Frame Rate High peaks result in wasted energy
Target Describe why peaks are bad (above target) and valleys are bad (below bad) Low valleys result in poor visual quality
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Workload prediction for DVFS
Before DVFS DVFS using signature-based workload Prediction 32% energy reduction One application of workload prediction….
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Outline Introduction & Background Experimental Framework
Signature-Based Workload Prediction Experimental Results Conclusions
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Conclusions Accurate 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 Work Automatic selection of signature elements
More sophisticated data structures for signature storage Faster comparison and replacement algorithms
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-Based Workload Estimation for Mobile 3D Graphics
Questions? -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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