From Turing Machine to
Global Illumination
Outline
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My first computer (CASIO fx3600)
Turning machine and von Neumann
architecture
GPU pipeline
Local and global illumination
Shadow and reflection through texture
Programmable GPUs
Calculator vs. Computer
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What is the difference between
a calculator and a computer?
Doesn’t a compute-r just
“compute”?
The Casio fx3600p calculated
can be programmed (38 steps
allowed).
Turing Machine
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Can be adapted to simulates the logic of any
computer that could possibly be constructed.
von Neumann architecture implements a
universal Turing machine.
Look them up at Wikipedia!
Outline
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My first computer (CASIO fx3600)
Turning machine and von Neumann
architecture
GPU pipeline
Local and global illumination
Shadow and reflection through texture
Programmable GPUs
Simplified View
Transform
(& Lighting)
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Rasterization
The Data Flow:
3D Polygons (+Colors, Lights, Normals, Texture
Coordinates…etc.)
 2D Polygons
 2D Pixels (I.e., Output Images)
Outline
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My first computer (CASIO fx3600)
Turning machine and von Neumann
architecture
GPU pipeline
Local and global illumination
Shadow and reflection through texture
Programmable GPUs
Global Effects
shadow
multiple reflection
translucent surface
Local vs. Global
How Does GPU Draw This?
Quiz
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Q1: A straightforward GPU pipeline give us
local illumination only. Why?
Hint: How is an object drawn? Do they
consider the relationship with other objects?
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Q2: What typical effects are missing?
Shadow, reflection, and refraction…
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Wait but I’ve seen shadow and reflection in
games before…
With Shadows
Without Shadows
Outline
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My first computer (CASIO fx3600)
Turning machine and von Neumann
architecture
GPU pipeline
Local and global illumination
Shadow and reflection through texture
Programmable GPUs
Adding “Memory” to the GPU
Computation
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Modern GPUs allow:
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The usage of multiple textures.
Rendering algorithms that use multiple passes.
Transform
(& Lighting)
Rasterization
Textures
Faked Global Illumination
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Shadow, Reflection, BRDF…etc.
In theory, real global illumination is not
possible in current graphics pipeline:
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Conceptually a loop of individual polygons.
No interaction between polygons.
Can this be changed by multi-pass rendering?
Case Study: Shadow Map
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Using two textures: color and depth
Relatively straightforward design using pixel
(fragment) shaders on GPUs.
Eye’s View
Light’s View
Depth/Shadow Map
Image Source: Cass Everitt et al., “Hardware Shadow
Mapping” NVIDIA SDK White Paper
Basic Steps of Shadow Maps
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Render the scene from the light’s point of
view,
Use the light’s depth buffer as a texture
(shadow map),
Projectively texture the shadow map onto
the scene,
Use “texture color” (comparison result) in
fragment shading.
Outline
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My first computer (CASIO fx3600)
Turning machine and von Neumann
architecture
GPU pipeline
Local and global illumination
Shadow and reflection through texture
Programmable GPUs
PC Graphics Architecture
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Two buses on PC: System Bus (CPU-Memory)
and Peripheral I/O Bus.
Before AGP: narrow path (I/O Bus) between
main memory and graphics memory (for
frame buffer, Z buffer, texture, vertex
data…etc.)
AGP and PCI-e speed up the link between
host PC and graphics processor (GPU)
Source: http://www.karbosguide.com/hardware/module2d03a.htm
NVIDIA Geforce 6800
NVIDIA Geforce 8800
NVIDIA Fermi (Geforce 400 and 500 Series)
From NVIDIA Fermi Architecture Whitepaper
http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
How to Program a GPU?
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Writing a 3D graphics application program
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Typically in DirectX or OpenGL
Still CPU programming in C/C++
The APIs and drivers do the dirty work for you.
Writing GPU shaders
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Typically in GLSL or Cg
Still drawing 3D objects
Working like plug-in’s to the 3D rendering
pipeline
GPGPU
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General-purpose GPU computing
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No longer restricted to graphics applications.
To utilize the abundant “GFLOPs” in GPU.
Could be implemented in GPU shaders
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By clever transformation of problem domains.
Textures to store the data structures
However, shaders could not perform memory
writes with calculated addresses (a.k.a. scatter
operations)
GPU as a Parallel Computing
Platform
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Treating GPUs as parallel machinery
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NVIDIA CUDA
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Not quite the same as shared-memory multiprocessor.
A special kind of memory hierarchy.
Widely adopted in real-world applications
OpenCL
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For non-NV GPUs and multi-core CPUs
Branch Divergence on GPU
Warp
…
if x1 – x0 > y1 – y0:
xMajorIteration()
else:
yMajorIteration()
…
½ performance for each branch!
Examples
GPU Shading Effects
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Reflection and refraction
Relief on surface
Ambient occlusion and lighting
Real-Time Rendering of
Splashing Water
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Particle system simulation for
real-time interaction with
terrains and dynamic objects.
Reconstruction of the splash
surface with 2D metaballs
Ray Tracing on GPU
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Using OpenCL or NVIDIA CUDA
Or use NVIDIA OptiX