Power Analysis of Mobile 3D Graphics - DAC

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2 Δεκ 2013 (πριν από 3 χρόνια και 11 μήνες)

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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,

University of Notre Dame

DAC 2006

3

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)

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

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

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

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 Purpose

Simplicity

Application specific

Accuracy

Signature
-
Based Predictor

8

Outline


Introduction and Motivation


Background



3D
-
pipeline Basics



Challenges in workload Estimation


Signature
-
Based Workload Prediction


Experimental Results


Conclusions


9

3D Pipeline Basics


3D representation


2D image

World View

Camera View

Raster View

Frame Buffer

Geometry

Setup

Rendering



Transformations



Lighting




Clipping



Scan
-
line conversion



Pixel rendering



Texturing


Texturing

10

Workload Across Applications


Workload varies significantly between applications


Prediction scheme must be flexible

RoomRev

TexCube

0

2

4

6

8

10

12

Execution Cycles (ARM, x10
7
)

Benchmark

11

Workload Within an Application


Workload can change rapidly between frames

0

1

2

3

4

5

6

1

16

31

46

61

76

91

106

121

136

151

166

181

196

Execution Cycles (ARM, x10
7
)

Frame

geometry

render

setup

Race

12

Outline


Introduction and Motivation


Background


Signature
-
Based Workload Prediction



Signature Generation



Method Overview



Pipeline Modifications


Experimental Results


Conclusions


13

Example

Signature

Table

Application

Frame

Buffer

Workload
Prediction



Signature

Workload

<6, 2.5>

1.0e4

extract

signature

measure

workload

Default

end

frame

extract

Signature:

<
vertex count
,

avg. area
>

3D Pipeline

14

Example

Signature

Table

Application

Frame

Buffer

Workload
Prediction



Signature

Workload

<6, 2.5>

<6, 2.5>

1.0e4

extract

signature

measure

workload

1.0e4

1.0e4

end

frame

extract

3D Pipeline

Signature:

<
vertex count
,

avg. area
>

15

Example

Signature

Table

Application

Frame

Buffer

Workload
Prediction



Signature

Workload

<6, 2.5>

<6, 2.5>

1.2e4

extract

signature

measure

workload

1.0e4

1.0e4

end

frame

extract

No overlap


(render all pixels)

3D Pipeline

Signature:

<
vertex count
,

avg. area
>

16

Transform

Clipping

Lighting

Scan
-
line

conversion

Per
-
pixel

Operations

Lighting

Scan
-
line

conversion

Per
-
pixel

Operations

Transform

Clipping

Application

Display

Partitioning the 3D pipeline

GEOMETRY

SETUP

RENDER

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
-
Buffer

Post Buffer

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

post
-
buffer

pre
-
buffer

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

Outline


Introduction & Background


Experimental Framework


Signature
-
Based Workload Prediction


Experimental Results



Evaluation Framework



Signature length vs. accuracy



Frame Rate



Energy


Conclusions


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

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

Workload Accuracy

Average Error (normalized)

<
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

23

Frame Rate

High peaks result in wasted energy

Low valleys result in poor visual quality

Target

24

Workload prediction for DVFS

Before DVFS

DVFS using signature
-
based
workload Prediction

32% energy
reduction

25

Outline


Introduction & Background


Experimental Framework


Signature
-
Based Workload Prediction


Experimental Results


Conclusions

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

Future Work


Automatic selection of signature elements


More sophisticated data structures for
signature storage


Faster comparison and replacement
algorithms

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?