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Real-Time Reyes-Style Adaptive Surface Subdivision

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Presentation on theme: "Real-Time Reyes-Style Adaptive Surface Subdivision"— Presentation transcript:

1 Real-Time Reyes-Style Adaptive Surface Subdivision
Anjul Patney, John Owens University of California, Davis

2 Offline Rendering Looks realistic Virtually no visible artifacts
Renders on clusters of CPUs Slow: hours per frame Flexible rendering pipelines Images courtesy: Pixar

3 Real-Time Rendering Looks good enough Minor artifacts are OK
Renders on commodity GPUs Fast: 60+ frames per second Restrictive rendering pipeline Images courtesy:

4 There is a gap Geometric complexity Shading complexity Special Effects
From Polygon Meshes to Smooth Surfaces Shading complexity From HLSL to RenderMan shaders Special Effects Motion Blur, depth-of-field Other complex effects Global Illumination, Subsurface scattering, ambient occlusion

5 But commodity GPUs… Vertex Processors Fragment Composite Host
Rasterization Primitive Setup Fragment Crossbar Framebuffer Ref: NVIDIA GeForce 6 Architecture

6 … have changed a lot General-Purpose Processors Host Thread Issue
Setup / Rasterize General-Purpose Processors Thread Processor Framebuffer Ref: NVIDIA G80 Architecture

7 How does this affect things?
Increased programmability Arbitrary computation Dynamic memory management Irregular data structures Flexible Rendering Compute for graphics Offline quality in real-time ? But we must be careful

8 There is a gap Geometric complexity Shading complexity Special Effects
From Polygon Meshes to Smooth Surfaces Shading complexity From HLSL to RenderMan shaders Special Effects Motion Blur, depth-of-field Other complex effects Global Illumination, Subsurface scattering, ambient occlusion

9 Real-Time Reyes-Style Adaptive Surface Subdivision
Anjul Patney and John D. Owens SIGGRAPH Asia 2008

10 Outline Motivation Reyes Subdivision – algorithm
Challenges Parallel formulation Subdivision on GPU – implementation Issues Solutions Results

11 Outline Motivation Reyes Subdivision – algorithm
Challenges Parallel formulation Subdivision on GPU – implementation Issues Solutions Results

12 Motivation Polygon-based Rendering is insufficient
Undesirable artifacts, especially along silhouettes Complicated model representation Model resolution is view-independent Can we expect performance from irregular computation on GPUs? Can GPUs support completely new pipelines?

13 Image courtesy: www.ign.com

14 Enter Reyes Industry standard in high-quality rendering
Bound, Cull and Split Dice (tessellate) Shade Sample and Filter Higher-order surfaces Split surfaces Shaded grids Final Pixels Industry standard in high-quality rendering Forms the architecture beneath RenderMan Pipeline features Input: Parametric Surfaces Rendering primitive: 0.5 x 0.5 pixel micropolygons Adaptive tessellation Per-micropolygon programmable shading Stochastic sampling Micropolygon grids

15 Outline Motivation Reyes Subdivision – algorithm
Challenges Parallel formulation Subdivision on GPU – implementation Issues Solutions Results

16 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

17 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

18 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

19 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

20 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

21 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

22 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

23 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

24 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

25 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

26 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

27 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

28 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

29 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

30 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

31 Reyes Subdivision Bound and Cull Diceable? Dice Split No Yes

32 What is bad? Depth first subdivision is recursive!
List of primitives is not static Cull, split may destroy or generate primitives Unbounded memory Dicing produces a huge number of micropolygons Emphasize a real-time implementation

33 Can we do this in parallel?

34 Can we do this in parallel?

35 Can we do this in parallel?
A lot of independent operations Our simplest model: 5k primitives, 1.2M micropolygons Massively Parallel workload Regular Computation Bound/Split/Dice all primitives together SPMD friendly Emphasize the importance of moving geometry processing to the GPU (no unnecessary transfers)

36 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes

37 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes

38 Parallel Reyes Subdivision
No Bound and Cull Diceable? Dice Split Yes No Yes

39 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

40 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

41 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

42 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

43 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

44 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

45 Parallel Reyes Subdivision
Bound and Cull Diceable? Dice Split No Yes No Yes

46 Analogy: A Dynamic Work Queue
B C D E F G H I Explain here: why the work queue? A A B C C E E F H Cull Split No Action

47 How can we do these efficiently?
Creating new primitives How to dynamically allocate space? Culling unneeded primitives How to avoid fragmentation?

48 Our Choice – keep it simple…
A B C D E F G H I A B C E F H A C E A child primitive is offset by the queue length

49 …and get rid of the holes later
B C E F H A C E A B C E F H A C E Work-queue stays contiguous Scan-based compact is fast! (Sengupta ‘07)

50 Outline Motivation Reyes Subdivision – algorithm
Challenges Parallel formulation Subdivision on GPU – implementation Issues Solutions Results

51 Platform NVIDIA GeForce 8800 GTX NVIDIA CUDA 1.1
16 SMs, each with 32-wide effective SIMD 16KB shared memory per SM 768 MB total GPU memory, no cache NVIDIA CUDA 1.1 Grid/Block/Thread programming model OpenGL interface through shared buffers

52 Implementation Details
Input primitives – Bicubic Bézier Surfaces Choice of primitive only affects implementation View Dependent Subdivision every frame CPU-GPU input transfer only once Suitable for animating control points Final micropolygons sent to OpenGL as a VBO Flat-shaded and displayed for preview

53 Kernels Implemented Dice Bound/Split
Regular, symmetric computation on a highly parallel workload 256 threads per primitive Primitive information in shared memory Bound/Split Non-trivial to ensure efficiency in implementation

54 Bound/Split: Efficiency Goals
Memory Coherence Off-chip memory accesses must be efficient Computational Efficiency Hardware SIMD must be maximally utilized

55 Memory Coherence After each iteration, work queue is compacted
Primitives always contiguous in memory Structure-Of-Arrays representation Attributes across primitives adjacent in memory 99.5% of all accesses were fully coalesced

56 SIMD Utilization Intra-Primitive parallelism 16 Threads per primitive
A primitive’s control points are mostly independent Execution path divergence is negligible 16 Threads per primitive Vectorized Bound/Split Use shared memory for communication 90.16% of all branches were SIMD coherent 32 threads (1 warp)

57 Outline Motivation Reyes Subdivision – algorithm
Challenges Parallel formulation Subdivision on GPU – implementation Issues Solutions Results

58 Results - Killeroo 11532 patches  14426 grids 5 levels of subdivision
Bound/Split: 6.99 ms Dice: 7.21 ms 4.2 frames per second (subdivision-only: 70) Killeroo NURBS model courtesy headus 3D tools:

59 Results - Teapot 32 patches  4823 grids 11 levels of subdivision
Bound/Split: 3.46 ms Dice: 2.42 ms 12.4 frames per second (subdivision-only: 170)

60 Results – Random Models
Subdivision time proportional to number of micropolygons

61 Overheads GPUs are inefficient at rendering lots of small triangles
Should ideally be zero; this is an acknowledged CUDA limitation

62 Storage Issues Reyes pipeline suffers from unbounded memory demand
A huge number of micropolygons are generated Transparency and Blending preclude early rejection Most implementations use screen-space buckets But how does this work in parallel? Large buckets present a more parallel workload Small buckets have a smaller memory footprint

63 Screen-Space Buckets Acceptable performance! Small memory footprint!

64 Limitations All primitives must be split before dicing
Cracks / Pinholes Uniform Dicing is wasteful Why are each of these limitations important?

65 Limitations All primitives must be split before dicing
Cracks / Pinholes Uniform Dicing is wasteful

66 Limitations All primitives must be split before dicing
Cracks / Pinholes Uniform Dicing is wasteful

67 Limitations All primitives must be split before dicing
Cracks / Pinholes Uniform Dicing is wasteful

68 Conclusions Recursive subdivision maps well to current GPUs
And works fast! It is advantageous to use smooth primitives in interactive rendering Fixed-function tessellation can be emulated Dicing is already very fast (2 Mgrids/sec) It’s time to experiment with alternate pipelines 2 Mgrids/sec == 512M micropolygons/sec

69 Future Work Crack Filling More of Reyes
Add dummy polygons during post-processing More of Reyes Displacement Mapping Offline quality Shading on GPUs Parallel Stochastic Sampling (Wei ‘08) A-buffer

70 Thanks to Feedback and suggestions from Financial support from
Per Christensen, Charles Loop, Dave Luebke, Matt Pharr, and Daniel Wexler Financial support from DOE Early Career PI Award National Science Foundation SciDAC Institute for Ultrascale Visualization Equipment support from NVIDIA

71 Real-Time Reyes-Style Adaptive Surface Subdivision
Backup Slides

72 CUDA Thread Structure Image courtesy: NVIDIA CUDA Programming Guide, 1.1

73 CUDA Memory Architecture
Image courtesy: NVIDIA CUDA Programming Guide, 1.1

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