The document presents an exploration of GPU ray tracing using CUDA, detailing the development of both sequential CPU and parallel GPU ray tracers. It outlines the principles of ray tracing, CUDA's architecture, and performance results highlighting the speed differences between CPU and GPU implementations. Future work is suggested to include spatial partitioning and optimization for multiple GPUs.

1. 1
GPU Ray Tracing
with CUDA
BY TOM PITKIN
Bill Clark, PhD
Stu Steiner, MS, PhC

2. Objectives

Develop a sequential CPU and parallel GPU ray tracer

Illustrate the difference in rendering speed and design of a CPU and
GPU ray tracer
2

3. Outline

Introduction to Ray Tracing

CUDA

Parallelization with CUDA / Results

Future Work

Questions
3

4. What is Ray Tracing?

Rendering technique used in computer graphics

Simulates the behavior of light

Can produce advanced optical effects
4

5. Light in the Physical World
5
Light Source
Film
Object with
Red Reflectivity
Pinhole

6. The Virtual Camera Model

Eye Position – camera location in 3D space

Reference Point – point in 3D space where the camera is pointing

Orientation Vectors (u, v, n) – camera orientation in 3D space

Image Plane – projected plane of the camera’s field of view
Reference Point
v (Up Vector)
n
u
Eye Position
6

7. Ray Generation

Map the physical screen to the image plane

Divide the image plane into a uniform grid of pixel locations

7
Send a ray through the center of each pixel location
𝐼𝑚𝑎𝑔𝑒 𝑃𝑙𝑎𝑛𝑒 𝐻𝑒𝑖𝑔ℎ𝑡
𝑆𝑐𝑟𝑒𝑒𝑛 𝐻𝑒𝑖𝑔ℎ𝑡
Pixel
Eye Position
𝐼𝑚𝑎𝑔𝑒 𝑃𝑙𝑎𝑛𝑒 𝑊𝑖𝑑𝑡ℎ
𝑆𝑐𝑟𝑒𝑒𝑛 𝑊𝑖𝑑𝑡ℎ

8. Ray Intersection Testing

Ray – Sphere Intersection

Ray – Triangle Intersection
8

9. Phong Reflection Model
Ambient
+
Diffuse
+
Specular
9
=
Phong Reflection

10. Specular Reflection

Recursive Ray Tracing
10

11. Outline

Introduction to Ray Tracing

CUDA

Parallelization with CUDA / Results

Future Work

Questions
11

12. What is CUDA?

Compute Unified Device Architecture (CUDA)

Parallel computing platform

Developed by Nvidia
12

13. Kernel Functions

Specifies the code to be executed in parallel

Single Program, Multiple Data (SPMD)
13

14. Kernel Execution

Grids

Blocks

Threads
14

15. Memory Model

Global Memory

Constant Memory

Texture Memory

Registers

Local Memory

Shared Memory
15

16. Outline

Introduction to Ray Tracing

CUDA

Parallelization with CUDA / Results

Future Work

Questions
16

17. Thread Organization

2D array of blocks

2D array of threads

17
Each thread represents
a ray
Block (0, 0)
Block (1, 0)
Block (2, 0)
Block (0, 1)
Block (1, 1)
Block (2, 1)
Image Plane

18. Testing Environment

OS – Ubuntu Gnome Remix 13.04

CPU – Core i7-920


Core Clock – 2.66 GHz
GPU – Nvidia GTX 570

Core Clock - 742 MHz

CUDA Core - 480

Memory Clock - 3800 MHz

Video Memory - GDDR5 1280MB
18

19. Test Objects

Teapot



Surfaces: 1
Triangles: 992
Al



Surfaces: 174
Triangles: 7,124
Crocodile

Surfaces: 6

Triangles: 34,404
19

20. Single Kernel
20
Single Kernel
Single Thread
160 (0.16 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
23,003 (23 sec)
411 (0.41 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
55,260 (55.26 sec)
5,867 (5.87 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
1,617,160 (26.95 min)
1
10
100
1,000
10,000
Milliseconds
100,000
1,000,000
10,000,000

21. Kernel Complexity and Size

Driver timeout

Register Spilling
21

22. Replacing Recursion

Iterative Loop

Layer based stack


Layers store color values returned from rays
Final image from convex combination of layers
22

23. Multi-Kernel
23
Multi-Kernel
Single Kernel (Previous Kernel)
381 (0.38 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
160 (0.16 sec)
967 (0.97 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
411 (0.41 sec)
13,217 (13.22 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
5,867 (5.87 sec)
1
10
100
1,000
Milliseconds
10,000
100,000

24. Multi-Kernel with Single-Precision Floating Points
24
Multi-Kernel with Single-Precision
Floating Points
Multi-Kernel (Previous Kernel)
46 (0.05 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
381 (0.38 sec)
118 (0.12 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
967 (0.97 sec)
1,556 (1.56 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
13,217 (13.22 sec)
1
10
100
1,000
Milliseconds
10,000
100,000

25. Caching Surface Data

Object’s surface data stored on shared memory

All threads in same block have access to cached surface data

Removes duplicate memory requests

Data reuse
25

26. Multi-Kernel with Surface Caching
26
Multi-Kernel with Surface Caching
Multi-Kernel with Single-Precision
Floating Points (Previous Kernel)
30 (0.03 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
46 (0.05 sec)
133 (0.13 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
118 (0.12 sec)
1,007 (1.01 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
1,556 (1.56 sec)
1
10
100
Milliseconds
1,000
10,000

27. Simplifying Mesh Data
27

Triangle data originally stored as three points (vertices)

Optimize data by storing triangles as one point (vertex) and two edges

Calculate edges on host before kernel call
0.5, 1
0, 0
0.5, 1
1, 0

28. Multi-Kernel with Mesh Optimization
28
Multi-Kernel with Mesh Optimization
Multi-Kernel with Surface Caching
(Previous Kernel)
27 (0.03 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
30 (0.03 sec)
127 (0.13 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
133 (0.13 sec)
873 (0.87 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
1,007 (1.01 sec)
1
10
100
Milliseconds
1,000
10,000

29. Final Results
29
Multi-Kernel with Intersection
Optimization
Single Thread
27 (0.03 sec)
Teapot
(Surfaces: 1)
(Triangles: 992)
23,003 (23 sec)
127 (0.13 sec)
Al
(Surfaces: 174)
(Triangles: 7,124)
55,260 (55.26 sec)
873 (0.87 sec)
Crocodile
(Surfaces: 6)
(Triangles: 34,404)
1,617,160 (26.95 min)
1
10
100
1,000
10,000
Milliseconds
100,000
1,000,000
10,000,000

30. Outline

Introduction to Ray Tracing

CUDA

Parallelization with CUDA / Results

Future Work

Questions
30

31. Future Work

Spatial partitioning

Multiple GPUs

Optimize code for different GPUs
31

32. Questions?
32