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
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