1. 2 -based workload estimation for mobile 3d graphics bren mochocki*, kanishka lahiri*, srihari...

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

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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

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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

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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

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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>

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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>

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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

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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

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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

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Outline

Introduction & Background

Experimental Framework

Signature-Based Workload Prediction

Experimental Results Evaluation Framework Signature length vs. accuracy Frame Rate Energy

Conclusions

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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

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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

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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

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Frame Rate

High peaks result in wasted energy

Low valleys result in poor visual quality

Ta

rge

t

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Workload prediction for DVFS

Before DVFS DVFS using signature-based workload Prediction

32% energy reduction

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Outline

Introduction & Background

Experimental Framework

Signature-Based Workload Prediction

Experimental Results

Conclusions

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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.

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Future Work

Automatic selection of signature elements

More sophisticated data structures for signature storage

Faster comparison and replacement algorithms

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-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?