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Green Computing Energy in mobile games. Outline Introduction Reducing CPU power Reducing LCD power Acknowledgements: Slides partly by Ehsan AZIMZADEH.

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Presentation on theme: "Green Computing Energy in mobile games. Outline Introduction Reducing CPU power Reducing LCD power Acknowledgements: Slides partly by Ehsan AZIMZADEH."— Presentation transcript:

1 Green Computing Energy in mobile games

2 Outline Introduction Reducing CPU power Reducing LCD power Acknowledgements: Slides partly by Ehsan AZIMZADEH & Morteza MOHAJJEL

3 Control Theory-based DVS for Interactive 3D Games Yan Gu Samarjit Chakraborty Department of Computer Science, National University of Singapore DAC 2008

4 Introduction Popularity of mobile games Problem – Battery life is a major concern – Mobile games are power hungry Solution – Power management techniques

5 Introduction (cont.) Average power consumption on Android handset

6 CPU power management Dynamic voltage/frequency scaling (DVFS) Basic idea – scaling of f & V proportional to workload Problem – Interactive nature of games Solution – Workload prediction

7 Big Picture of the Scheme

8 Game Engines Infinite loop: – One Frame per each loop Each loop: – Computing Collision detection Game physics AI – Rendering Transforming of 3D objects onto 2D screen Deleting invisible pixels …

9 Game power reduction based on DVS Predicting the workload of a frame before processing/rendering. – History based – PID Controller-based – LMS-based

10 History-based

11 PID Controller-based Proportional–Integral–Derivative Generic closed loop controller Adjusting system parameters based on: – Difference between measured and set point K p : Proportional gain K i : Integral gain K d : Derivative gain Error = SP − PV

12 PID Controller-based (cont.)

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14 Hand tuned parameters: – K p – D – I UnstableSlow

15 PID Controller-based (cont.) PID Controller based vs. History-based – Prediction Accuracy Laptop

16 PID Controller-based (cont.) PID Controller based vs. History-based – Prediction Accuracy PDA

17 PID Controller-based (cont.)

18 Experimental evaluation – Quake/Quake II game engines – Running on: Laptop – Intel Pentium M & Windows XP PDA – Intel XScale & Windows Mobile 5 Simu-disc – No transition overhead Simu- cont – No transition overhead and continuously scalable frequency

19 PID Controller-Experimental evaluation – Laptop 20 frame/sec. 30 frame/sec.

20 PID Controller-Experimental evaluation – PDA

21 Energy Reduction Results Laptop – 20 fps Up to 22% less power than FIX Max possible saving: 23% – 30 fps 13% less power than FIX

22 Energy Reduction Results PDA – Under Simu-cont 35% less power than FIX – Under Simu-disc 25% less power than FIX – Upper bound on savings: 68%

23 LMS-based Low-Complexity Game Workload Prediction for DVFS B. Dietrich, S. Nunna, D. Goswami, S. Chakraborty, M. Gries TU Munich, Germany & Intel Labs, Germany ICCD 2010

24 LMS-based Workload Prediction Disadvantages of PID Controller-based: – Generality of PID Controllers – Unstability for most combinations of gains – Hand tuning – Different optimized values even for different runs of a game play Needs for dynamically adaptable predictor

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26 Pareto Optimal Points – PID Controller

27 Table III - PERCENTAGE OF FRAME DEADLINE MISSES OF INDIVIDUALLY TUNED PID GAIN SETS AND OBTAINED RESULTS FOR LMS LINEAR

28 LMS-based (cont.) Representing predicted workload as a linear combination of: – Previous frame workloads – System parameter values Goal: Learn W[i] adaptively to minimize error:

29 LMS-based (cont.)

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33 Performance of LMS Linear Prediction – Order of the predictor High order: computationally expensive Low order: less accuracy – Learning rate

34 LMS-based - Experimental Evaluation – laptop Quake II Game Engine (open source) Intel Pentium M Processor (1.8 GHz) & Ubuntu Power measurement circuitry Intel SpeedStep® Technology RDTSC (Read TimeStamp Counter) instruction

35 LMS - Experimental Evaluation

36 Comparison: Linux Power Management Kernel ondemand governor – Sets V and F values for processor based on utilization – 7% power saving in this scenario – Why? LMS power saving – Up to 34.5%

37 Concluding Remarks DVFS effective in 3D games on mobiles Basic technique in these works: – Predict workload, set V and F accordingly PID controller – Unstable – Needs tuning several params LMS controller – Stable – Adjust only one param

38 Adaptive Display Power Management for Mobile Games National University of Singapore B. Anand, P. G Kannan J. Sebastian, A. L. Ananda M. C. Chan Singapore Management University K. Thirugnanam, R. K. Balan MobiSys’11, June 28–July 1, 2011, Bethesda, Maryland, USA.

39 Motivation Mobile games are becoming very popular. Display, wireless and CPU are The main power consuming elements on a mobile device. Power consumed by the LCD backlight is 45 to 95%.

40 Average power consumption on Android handset

41 TFT LCD

42 Thin Film Transistor Liquid Crystal Display (TFT- LCD). There are three kinds of TFT LCD panels: – Transmissive – Reflective – Transflective Are used in: – computer monitors – mobile phones – handheld video game systems

43 TFT LCD Transmissive LCD Displays

44 TFT LCD Reflective LCD Displays

45 TFT LCD Structure of TFT LCD

46 Basic Idea The backlight component is the major source of power consumption. Brighten the frame Dimming Backlight Energy Savings

47 Increase the brightness Increase the pixel values Linearly: 1.Less quality. 2.Computationally expensive. Non-linearly

48 Non-linear approach use a standard Tone Mapping operator called the Inverse Gamma Function. Gamma Function widely available in: – OpenGL – X11 – game rendering engines – many commercial 3D games – mobile GPUs (Nvidia Tegra 2)

49 The Effect of Gamma and Linear Transformations. The Amount of Power Saved is the Same for Both Approaches.

50 Applying a higher Gamma value results in a higher contrast loss Global Contrast Loss vs Gamma

51 Different images will exhibit different quality loss Global Contrast Loss vs Image Brightness for γ=3

52 Backlight Power Measurement Laptop: Figure 12 setup HTC Magic & HTC Hero smartphones: using the Power Tutor

53 Gamma to Backlight Relationship set the Gamma and backlight intensity to their default values (1 Gamma and 255 backlight) The backlight levels ranged from 0 to 255 Gamma parameter ranged from 1 to 10

54 Gamma to Backlight Relationship

55 Computing Image Brightness Naïve approach – Compute average brightness of all images Careful sampling – 2000 samples (1 out of every 20 pixels)

56 Human Calibration of Gamma Thresholds Study the acceptable values of Gamma for each image brightness level. used 14 discrete brightness levels. 2 different images for each brightness level. The goal was to obtain an aggressive & conservative threshold for each image.

57 Human Calibration of Gamma Thresholds

58 Flowchart of System

59 Analytical Evaluation Lenovo W500 HTC HeroHTC Magic

60 Laptop Power Measurement

61 Analytical Evaluation Quake IIIPlaneshift

62 Baseline Power on Laptop

63 Baseline Power on HTC Magic

64 Power Saving in the Display Power-Savings Measurements on laptop Aggr: Aggressive Cons: Conservative

65 Demographics Statistics for User Study

66 User Study & Analysis

67 تمرین اضافی توان مصرفی گوشی موبایل خود و اجزای آن را با نرم افزار مناسب اندازه گیری کرده و تحلیل کنید. نوع گوشی، روش اندازه گیری، نوع صفحه نمایش و حالتهای مختلف آزمایش را گزارش کنید. بررسی کنید آیا گوشی شما با افزایش Brightness تصویر انرژی بیشتری مصرف می کند یا کمتری، و چرا؟


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