SIMD and GPU-Accelerated Rendering of Implicit Models by Pourya Shirazian Iran University of ...

125 Pages·2015·12.84 MB·English

SIMD and GPU-Accelerated Rendering of Implicit Models
by
Pourya Shirazian
Iran University of Science and Technology, 2008
Azad University of Tehran, 2005
A Dissertation Submitted in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY
in the Department of Computer Science
(cid:13)c Pourya Shirazian, 2014
University of Victoria
All rights reserved. This dissertation may not be reproduced in whole or in part, by
photocopying or other means, without the permission of the author.
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SIMD and GPU-Accelerated Rendering of Implicit Models
by
Pourya Shirazian
Iran University of Science and Technology, 2008
Azad University of Tehran, 2005
Supervisory Committee
Dr. Brian Wyvill, Supervisor
(Department of Computer Science, University of Victoria)
Dr. Roy Eagleson, Outside Member
(Department of Electrical and Computer Engineering, Western University)
Dr. Paul Lalonde, Departmental Member
(Department of Computer Science, University of Victoria)
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Supervisory Committee
Dr. Brian Wyvill, Supervisor
(Department of Computer Science, University of Victoria)
Dr. Roy Eagleson, Outside Member
(Department of Electrical and Computer Engineering, Western University)
Dr. Paul Lalonde, Departmental Member
(Department of Computer Science, University of Victoria)
ABSTRACT
Implicit models inherently support automatic blending and trivial collision detection
which makes them an effective tool for designing complex organic shapes with many
applications in various areas of research including surgical simulation systems. How-
ever, slow rendering speeds can adversely affect the performance of simulation and
modelling systems. In addition, when the models are incorporated in a surgical sim-
ulation system, interactive and smooth cutting becomes a required feature for many
procedures.
In this research, we propose a comprehensive framework for high-performance
rendering and physically-based animation of tissues modelled using implicit surfaces.
Our goal is to address performance and scalability issues that arise in rendering
complex implicit models as well as in dynamic interactions between surgical tool and
models.
Complexmodelscanbecreatedwithimplicitprimitives, blendingoperators, affine
transformations, deformations and constructive solid geometry in a design environ-
ment that organizes all these in a scene graph data structure called the BlobTree. We
show that the BlobTree modelling approach provides a very compact data structure
which supports the requirements above, as well as incremental changes and trivial col-
lision detection. A GPU-assisted surface extraction algorithm is proposed to support
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interactive modelling of complex BlobTree models.
Using a finite element approach we discretize those models for accurate physically-
based animation. Our system provides an interactive cutting ability using smooth
intersection surfaces. We show an application of our system in a human skull cran-
iotomy simulation.
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Contents
Supervisory Committee ii
Abstract iii
Table of Contents v
List of Tables viii
List of Figures x
Acknowledgements xvi
Dedication xvii
1 Introduction 1
1.1 General aim of this Research . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Limitation of Current Models . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Background Material 7
2.1 Implicit Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Sweep Surfaces and Sketching . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 Surface extraction methods . . . . . . . . . . . . . . . . . . . 12
2.3.2 GPU-Accelerated rendering of implicits . . . . . . . . . . . . . 15
2.3.3 Volume mesh cutting . . . . . . . . . . . . . . . . . . . . . . . 16
3 High Performance Rendering on Multi-Core Architectures 19
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3.1 Architecture Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Naming Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.1 BlobTree Linearization . . . . . . . . . . . . . . . . . . . . . . 24
3.3.2 Surface Extraction . . . . . . . . . . . . . . . . . . . . . . . . 26
3.4 MPU size Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.6 Chapter Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4 GPU discretization 43
4.1 Many-cores architectures . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Data Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3 Memory foot prints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4 Stackless BlobTree traversal . . . . . . . . . . . . . . . . . . . . . . . 49
4.5 GPU Surface Extraction Algorithm . . . . . . . . . . . . . . . . . . . 53
4.6 Analysis and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5 Deformable Model 59
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.2 Physical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3 Collision and Contact with tools . . . . . . . . . . . . . . . . . . . . . 62
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
6 Real-time Cutting 70
6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.2 Tetrahedral Mesh Data structure . . . . . . . . . . . . . . . . . . . . 72
6.3 Cutting Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3.1 Edge Intersections . . . . . . . . . . . . . . . . . . . . . . . . 77
6.3.2 Produce cut-node list . . . . . . . . . . . . . . . . . . . . . . . 78
6.3.3 Filter intersected-edges . . . . . . . . . . . . . . . . . . . . . . 80
6.3.4 Compute configuration codes for cut elements . . . . . . . . . 80
6.3.5 Topological Updates . . . . . . . . . . . . . . . . . . . . . . . 83
6.4 Cutting Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
7 Evaluation, Analysis and Comparisons 89
7.1 Previous work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
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7.2 Architectural constraints . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
7.3.1 The Eggshell Model . . . . . . . . . . . . . . . . . . . . . . . . 91
7.3.2 Craniotomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8 Conclusions 97
8.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Bibliography 100
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List of Tables
Table 3.1 Comparison of speedups and field value evaluations per triangle
(FVEPT) for polygonization of Tower model with different SIMD
instruction sets. Note that FVEPT was 17 before adding SIMD
optimizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Table 3.2 Comparison of our polygonization method against Schmidt et al.
’s [62] when rendering Medusa model at 5 different resolutions
on one single core with AVX instructions. All timings are in
milliseconds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Table 4.1 Memory footprint of the input BlobTree in our GPU polygoniza-
tion algorithm in bytes. The entire BlobTree for a model with
64K nodes (primitives and operators) takes up about 20 MiB in
our current system. . . . . . . . . . . . . . . . . . . . . . . . . 49
Table 4.2 Stackless BlobTree traversal improved the performance of our
BlobTree field evaluation significantly. Here is the comparison
between our novel stackless approach versus the stack-based im-
plementation for various models. Timings are the average of 100
runs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Table 5.1 The following deformable models are used in our experiments. . 65
Table 6.1 The following look-up table is used to differentiate between differ-
ent cutting configurations based on the number of cut-edges and
cut-nodes. All cases subdivide tetrahedral elements into smaller
cells except for case Z where the original cell is left intact. . . . 81
Table 6.2 Lookup table for generating sub-elements for type A configuration 82
Table 6.3 Lookup table for generating sub-elements for type B configura-
tion. Sub-elements are separated by semi-colon to fit on the line. 82
Table 6.4 Mesh quality measurements. . . . . . . . . . . . . . . . . . . . . 86
ix
Table 7.1 Segmented brain data-set statistics. . . . . . . . . . . . . . . . . 93
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List of Figures
Figure 2.1 BlobTree structureofacoffeemugcreatedwithCSGandskeletal
implicit primitives. . . . . . . . . . . . . . . . . . . . . . . . . . 10
Figure 3.1 The MPU is our unit of computation per each core illustrated
as a 2D cross section here. Field-values due to every 4 or 8
points are computed in parallel with SSE or AVX instructions,
respectively. When the field at a vertex is zero no iso-surface will
pass in the neighborhood of a unit circle (sphere in 3D) centered
at that vertex. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Figure 3.2 Left Column: The one-time preparation steps before scheduling
kernel functions for computation. Middle Column: The early
discard kernel function. Right Column: The MPU processing
kernel function. . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Figure 3.3 Thereductionprocesscombinesalltransformationnodesatprim-
itives. After the operation, all internal operators are associated
with identity transformation matrices. . . . . . . . . . . . . . . 24
Figure 3.4 The BlobTree is flattened to a structure of arrays (SOA), and
is stored at aligned memory addresses for performance reasons.
Each row represents an array of values of the same type. N is
determined in such a way that the entire structure fits in the
cache memory of the processor. . . . . . . . . . . . . . . . . . 25
Figure 3.5 Top: A cell edge is intersected with part of the surface shown
in blue. By performing one field evaluation using AVX or two
with SSE instructions the interval containing the intersection
point can be identified. The final root is computed using linear
interpolation within the interval marked with bold line segment. 28
Figure 3.6 AveragetimespentpereachMPU whenpolygonizingtheTowers
model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31