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DirectCompute Accelerated Separable Filtering

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DirectCompute Accelerated Separable Filtering. Separable Filters. Much faster than executing a box filter Classically performed by the Pixel Shader Consists of a horizontal and vertical pass Source image over-sampling increases with kernel size Shader is usually TEX instruction limited.

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Presentation Transcript
separable filters
Separable Filters
  • Much faster than executing a box filter
  • Classically performed by the Pixel Shader
  • Consists of a horizontal and vertical pass
  • Source image over-sampling increases with kernel size
    • Shader is usually TEX instruction limited

AMD‘s Favorite Effects

separable who cares
Separable? – Who Cares 
  • In many cases developers use this technique even though the filter may not actually be separable
    • Results are often still acceptable
    • Much faster than performing a real box filter
    • Accelerates many bilateral cases

AMD‘s Favorite Effects

typical pipeline steps
Typical Pipeline Steps

Source

RT

Intermediate

RT

Destination RT

Horizontal Pass

Vertical Pass

AMD‘s Favorite Effects

use bilinear hw filtering
Use Bilinear HW filtering?
  • Bilinear filter HW can halve the number of ALU and TEX instructions
    • Just need to compute the correct sampling offsets
  • Not possible with more advanced filters
    • Usually because weighting is a dynamic operation
    • Think about bilateral cases...

AMD‘s Favorite Effects

where to start with directcompute
Where to start with DirectCompute
  • Is the Pixel Shader version TEX or ALU limited?
    • You need to know what to optimize for!
    • Use IHV tools to establish this
  • Achieving peak performance is not easy – so write a highly configurable kernel
    • Will allow you to easily experiment and fine tune

AMD‘s Favorite Effects

thread group shared memory tgsm
Thread Group Shared Memory (TGSM)
  • TGSM can be used to reduce TEX ops
  • TGSM can also be used to cache results
    • Thus saving ALU ops too
  • Load a sensible run length – base this on HW wavefront/warp size (AMD = 64, NVIDIA = 32)
    • Choose a good common factor (multiples of 64)

AMD‘s Favorite Effects

kernel 1
Kernel #1

128 threads load 128 texels

  • Redundant compute threads 

...........

Kernel Radius

128 – ( Kernel Radius * 2 ) threads compute results

AMD‘s Favorite Effects

avoid redundant threads
Avoid Redundant Threads
  • Should ensure that all threads in a group have useful work to do – wherever possible
  • Redundant threads will not be reassigned work from another group
  • This would involve alot of redundancy for a large kernel diameter

AMD‘s Favorite Effects

kernel 2
Kernel #2

Kernel Radius * 2 threads

load 1 extra texel each

128 threads load 128 texels

  • No redundant compute threads 

...........

Kernel Radius

128 threads compute results

AMD‘s Favorite Effects

multiple pixels per thread
Multiple Pixels per Thread
  • Allows for natural vectorization
    • 4 works well on AMD HW
    • Doesn‘t hurt performance on scalar HW
  • Possible to cache TGSM reads on General Purpose Registers (GPRs)
    • Quartering TGSM reads - absolute winner!!

AMD‘s Favorite Effects

kernel 3
Kernel #3

Kernel Radius * 2 threads

load 1 extra texel each

32 threads load 128 texels

  • Compute threads not a multiple of 64 

...........

Kernel Radius

32 threads compute 128 results

AMD‘s Favorite Effects

multiple lines per thread group
Multiple Lines per Thread Group
  • Process multiple lines per thread group
    • Better than one long line
    • 2 or 4 works well
  • Improved texture cache efficiency
  • Compute threads back to a multiple of 64

AMD‘s Favorite Effects

kernel 4
Kernel #4

Kernel Radius * 4 threads

load 1 extra texel each

64 threads load 256 texels

...........

...........

Kernel Radius

64 threads compute 256 results

AMD‘s Favorite Effects

kernel diameter
Kernel Diameter
  • Kernel diameter needs to be > 7 to see a DirectCompute win
    • Otherwise the overhead cancels out the advantage
  • The larger the kernel diameter the greater the win

AMD‘s Favorite Effects

use packing in tgsm
Use Packing in TGSM
  • Use packing to reduce storage space required in TGSM
    • Only have 32k per SIMD
  • Reduces reads/writes from TGSM
  • Often a uint is sufficient for color filtering
  • Use SM5.0 instructions f32tof16(), f16tof32()

AMD‘s Favorite Effects

high definition ambient occlusion
High Definition Ambient Occlusion

Depth + Normals

=

*

HDAO buffer

Original Scene

Final Scene

AMD‘s Favorite Effects

perform at half resolution
Perform at Half Resolution
  • HDAO at full resolution is expensive
  • Running at half resolution captures more occlusion – and is obviously much faster
  • Problem: Artifacts are introduced when combined with the full resolution scene

AMD‘s Favorite Effects

bilateral dilate blur
Bilateral Dilate & Blur

HDAO buffer doesn‘t match with scene

A bilateral dilate & blur fixes the issue

AMD‘s Favorite Effects

new pipeline
New Pipeline...

½ Res

Still much faster than performing at full res!

Horizontal Pass

Vertical Pass

Bilinear Upsample

Intermediate UAV

Dilated & Blurred

AMD‘s Favorite Effects

pixel shader vs directcompute
Pixel Shader vs DirectCompute

*Tested on a range of AMD and NVIDIA DX11 HW, DirectCompute is between ~2.53x to ~3.17x faster than the Pixel Shader

AMD‘s Favorite Effects

depth of field
Depth of Field
  • Many techniques exist to solve this problem
  • A common technique is to figure out how blurry a pixel should be
    • Often called the Cirle of Confusion (CoC)
  • A Gaussian blur weighted by CoC is a pretty efficient way to implement this effect

AMD‘s Favorite Effects

the pipeline
The Pipeline...

Vertical Pass

Horizontal Pass

Intermediate UAV

CoC

AMD‘s Favorite Effects

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Shogun 2: DoF OFF

AMD‘s Favorite Effects

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Shogun 2: DoF ON

AMD‘s Favorite Effects

pixel shader vs directcompute1
Pixel Shader vs DirectCompute

*Tested on a range of AMD and NVIDIA DX11 HW, DirectCompute is between ~1.48x to ~1.86x faster than the Pixel Shader

AMD‘s Favorite Effects

summary
Summary
  • DirectCompute greatly accelerates larger kernel diameter filters
  • Allows for filtering at full resolution
  • For access to source code:

AMD‘s Favorite Effects

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AMD‘s Favorite Effects

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