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1. 2 -Based Workload Estimation for Mobile 3D Graphics Bren Mochocki*, Kanishka Lahiri*, Srihari Cadambi*, Xiaobo Sharon Hu *NEC Laboratories America,

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Presentation on theme: "1. 2 -Based Workload Estimation for Mobile 3D Graphics Bren Mochocki*, Kanishka Lahiri*, Srihari Cadambi*, Xiaobo Sharon Hu *NEC Laboratories America,"— Presentation transcript:

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2 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

3 3 Mobile Graphics Technology Basic 3D Graphics Technology Video clips Advanced 3D D color Time Increasing resource load Performance (Speed) Lifetime (Energy)

4 4 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 terminals Mochocki, Lahiri, Cadambi, 06 DVFS for mobile 3D graphics Accurate workload prediction is critical Gu, Chakraborty, Ooi, 06 Games are up for DVFS

5 5 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

6 6 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 PurposeSimplicity Application specificAccuracy History-Based Predictors Analytical Predictors

7 7 Workload-Estimation Spectrum 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 General PurposeSimplicity Application specificAccuracy Signature-Based Predictor

8 8 Outline Introduction and Motivation Background 3D-pipeline Basics Challenges in workload Estimation Signature-Based Workload Prediction Experimental Results Conclusions

9 9 3D Pipeline Basics 3D representation 2D image World ViewCamera ViewRaster ViewFrame Buffer GeometrySetupRendering Transformations Lighting Clipping Scan-line conversion Pixel rendering Texturing

10 10 Workload Across Applications Workload varies significantly between applications Prediction scheme must be flexible RoomRev TexCube Execution Cycles (ARM, x10 7 ) Benchmark

11 11 Workload Within an Application Workload can change rapidly between frames Execution Cycles (ARM, x10 7 ) Frame geometry render setup Race

12 12 Outline Introduction and Motivation Background Signature-Based Workload Prediction Signature Generation Method Overview Pipeline Modifications Experimental Results Conclusions

13 13 Example Signature Table Application Frame Buffer Workload Prediction SignatureWorkload 1.0e4 extract signature measure workload Default end frame extract Signature: 3D Pipeline

14 14 Example Signature Table Application Frame Buffer Workload Prediction SignatureWorkload 1.0e4 extract signature measure workload 1.0e4 end frame extract 3D Pipeline Signature:

15 15 Example Signature Table Application Frame Buffer Workload Prediction SignatureWorkload 1.2e4 extract signature measure workload 1.0e4 end frame extract No overlap (render all pixels) 3D Pipeline Signature:

16 16 TransformClippingLightingScan-lineconversionPer-pixelOperations Lighting Scan-line conversion Per-pixel Operations TransformClipping Application Display Partitioning the 3D pipeline GEOMETRYSETUPRENDER Application Display Generally small workload Provides necessary signature elements Bulk of 3D workload Transform + Clipping Scan-line conversion Per-pixel Operations Lighting Buffer ORIGINAL PARTITIONED Pre-BufferPost Buffer

17 17 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

18 18 Signature Composition Can vary by application May include: 1.Average Triangle Area 2.Average Triangle Height 3.Total vertex count 4.Lit vertex count 5.Number of lights 6.Any measurable parameter Larger signatures more accurate Smaller signatures less time & space

19 19 Outline Introduction & Background Experimental Framework Signature-Based Workload Prediction Experimental Results Evaluation Framework Signature length vs. accuracy Frame Rate Energy Conclusions

20 20 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

21 21 Evaluation Framework OpenGL/ES library Instrumented with pipeline stage triggers Hans-Martin Will Fast, cycle-accurate Simulation W. Qin Trace simulator of mobile 3D pipeline OpenGL/ES 1.0 3D – application 3D pipeline Performance/power Simit-ARM Cross Compiler ARM g++ Trace Simulator Triangle, Instruction, & Trigger traces Workload prediction scheme 3D application Vincent Processor Energy Model Architecture Model Simulation output

22 22 Workload Accuracy Average Error (normalized) 2 bytes 6 bytes 10 bytes 14 bytes Signature Complexity > 2 fps error at peaks Peaks < 1 fps triangle count, avg. area, avg. height, vertex count

23 23 Frame Rate High peaks result in wasted energy Low valleys result in poor visual quality Target

24 24 Workload prediction for DVFS Before DVFS DVFS using signature-based workload Prediction 32% energy reduction

25 25 Outline Introduction & Background Experimental Framework Signature-Based Workload Prediction Experimental Results Conclusions

26 26 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.

27 27 Future Work Automatic selection of signature elements More sophisticated data structures for signature storage Faster comparison and replacement algorithms

28 28 -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 Questions?


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