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 terminalsMochocki, 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

SimplicitySimplicity

Application specific

AccuracyAccuracy

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

SimplicitySimplicity

Application specific

AccuracyAccuracy

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

RoomRevTexCube

0

2

4

6

8

10

12

Ex

ec

uti

on

Cy

cle

s (

AR

M,

x1

07 )

Benchmark

11

Workload Within an ApplicationWorkload 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

Ex

ecu

tio

n C

yc

les

(A

RM

, 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

SignatureTable

ApplicationFrame Buffer

Workload Prediction

Signature Workload

<6, 2.5> 1.0e4extract

signaturemeasureworkload

Default

endframe

extract

Signature: <vertex count, avg. area>

3D Pipeline3D Pipeline

14

Example

SignatureTable

ApplicationFrame Buffer

Workload Prediction

Signature Workload

<6, 2.5>

<6, 2.5> 1.0e4extract

signaturemeasureworkload

1.0e41.0e4

endframe

extract

3D Pipeline3D Pipeline

Signature: <vertex count, avg. area>

15

Example

SignatureTable

ApplicationFrame Buffer

Workload Prediction

Signature Workload

<6, 2.5>

<6, 2.5> 1.2e4extract

signaturemeasureworkload

1.0e41.0e4

endframe

extractNo overlap (render all pixels)

3D Pipeline3D Pipeline

Signature: <vertex count, avg. area>

16

TransformTransformTransformTransformClippingClippingClippingClipping

LightingLightingLightingLighting Scan-lineScan-lineconversionconversion

Scan-lineScan-lineconversionconversion

Per-pixelPer-pixelOperationsOperations

Per-pixelPer-pixelOperationsOperationsLightingLighting Scan-line

conversion

Scan-lineconversion

Per-pixelOperations

Per-pixelOperationsTransformTransform ClippingClipping

ApplicationApplicationApplicationApplicationDisplayDisplayDisplayDisplay

Partitioning the 3D pipeline

GEOMETRY SETUP RENDER

ApplicationApplicationApplicationApplicationDisplayDisplayDisplayDisplay

• Generally small workload• Provides necessary signature elements

Bulk of 3D workload

Transform+

Clipping

Transform+

ClippingScan-line

conversion

Scan-lineconversion

Per-pixelOperations

Per-pixelOperationsLightingLightingBufferBuffer

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-bufferpre-buffer

18

Signature Composition

Can vary by application

May include:1. Average Triangle Area2. Average Triangle Height3. Total vertex count4. Lit vertex count5. Number of lights6. 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 FrameworkOpenGL/ES library Instrumented withpipeline stage triggers

Hans-Martin WillFast, cycle-accurateSimulation

W. Qin

Trace simulator of mobile 3D pipeline

OpenGL/ES 1.0 3D – application

3D pipeline Performance/power

Simit-ARM

Cross CompilerARM — g++

Trace Simulator

Triangle,Instruction, &Trigger traces

Workload predictionscheme

3D application

Vincent

ProcessorEnergy Model

Architecture Model

Simulation output

22

Workload AccuracyA

ve

rag

e E

rro

r (n

orm

aliz

ed

)

<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

Ta

rge

t

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?