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-Based Workload Estimation for Mobile 3D Graphics

-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.

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-Based Workload Estimation for Mobile 3D Graphics

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  1. -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

  2. Mobile Graphics Technology 2000 2001 2002 2003 2004 2005 2006 2007 • Increasing resource load • Performance (Speed) • Lifetime (Energy) Graphics Technology Advanced 3D Basic 3D Video clips 2D color 1997 Time

  3. 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 Graphics Algorithms System - Level Optimizations • Tack, 04 • LoD control for mobile terminals Accurate workload prediction is critical

  4. 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

  5. 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 Simplicity Application specific Accuracy History-Based Predictors Analytical Predictors

  6. 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 Purpose Simplicity Application specific Accuracy Signature-Based Predictor

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

  8. 3D Pipeline Basics Texturing • 3D representation  2D image Geometry Setup Rendering World View Camera View Raster View Frame Buffer • Transformations • Lighting • Clipping • Scan-line conversion • Pixel rendering • Texturing

  9. Workload Across Applications 12 RoomRev TexCube • Workload varies significantly between applications • Prediction scheme must be flexible 10 8 6 Execution Cycles (ARM, x107) 4 2 0 Benchmark

  10. Workload Within an Application 6 5 4 3 2 1 0 • Workload can change rapidly between frames Race geometry render Execution Cycles (ARM, x107) setup 1 16 31 46 61 76 91 106 121 136 151 166 181 196 Frame

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

  12. Example Signature: <vertex count, avg. area> 3D Pipeline end frame Frame Buffer Application extract extract signature measure workload 1.0e4 <6, 2.5> Default Signature Table Workload Prediction

  13. Example Signature: <vertex count, avg. area> 3D Pipeline end frame Frame Buffer Application extract extract signature measure workload 1.0e4 <6, 2.5> Signature Table 1.0e4 1.0e4 Workload Prediction

  14. Example Signature: <vertex count, avg. area> 3D Pipeline end frame Frame Buffer Application extract No overlap (render all pixels) extract signature measure workload 1.2e4 <6, 2.5> Signature Table 1.0e4 1.0e4 Workload Prediction

  15. Partitioning the 3D pipeline Bulk of 3D workload • Generally small workload • Provides necessary signature elements ORIGINAL GEOMETRY SETUP RENDER Application Display Transform Clipping Transform Lighting Lighting Clipping Scan-line conversion Scan-line conversion Per-pixel Operations Per-pixel Operations PARTITIONED Application Display Scan-line conversion Per-pixel Operations Lighting Buffer Transform + Clipping Pre-Buffer Post Buffer

  16. Pipeline Workload pre-buffer post-buffer • Pre-buffer workload is less than 10% of the total workload • Pre-buffer variation is small • Post-buffer workload is large with significant variation

  17. 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

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

  19. Architectural View Prog. Voltage Regulator V, F Prog. PLL • pre-buffer • signature extraction post-buffer Applications Processor Programmable 3D Graphics Engine Performance counter System-level Communication Architecture Memory Frame Buffer measure workload • buffer • signature table output

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

  21. Workload Accuracy > 2 fps error at peaks Average Error (normalized) Peaks < 1 fps <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

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

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

  24. Outline • Introduction & Background • Experimental Framework • Signature-Based Workload Prediction • Experimental Results • Conclusions

  25. 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.

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

  27. 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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