A on the GPU

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A on the GPU

• by Nikhil Haldar-Sinha and Dan Marino

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Outline

• Why we care
• The GPU
• How to exploit it
• Our Algorithm
• Generation
• Heuristics
• Choosing the path
• Conclusions

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Why We Care
From Stream Programming Environments
Hanrahan, 2004.
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The GPU

• Streaming processor, SIMD
• Parallel Processor
• Specialized for graphics
• No branching
• Loops must be unrolled
• No integer ALU
• Meant to be completely linear

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Fixed Function Pipeline

• Transform Vertices from 3-space to screen space
• Apply textures
• Sort them so you know whats in the front
• Give the pixel a color

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Computation on the GPU

• Rasterization is the part we change
• Render to a bitmap instead of screen
• Pass our data in as textures
• Render quads in screenspace
• Limitation Cannot read and write from a texture
  at the same time

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Our Algorithm Overview

• Representation in a texture
• Fragment Shaders to calculate heuristics
• Use the video card to find lowest value
• Generate choices and control flow on the CPU

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Representation

• The Grid problem is a grid, so it easily fits
  into a 2d texture
• Value is a color in a pixel
• Values are set in the CPU

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Heuristics (Pass 1)

• Pass 1
• Pass in a goal state texture
• Pass in a current state texture
• Using these two values, calculate the
  contribution to the heuristic for the current
  pixel
• Pass 2
• Pass in first pass result. Add up all the
  pixels.

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Finding lowest value

• Render each value at the same x, y location.
  Colorf(state)
• zhg
• Camera at x, y, -1, looking at x, y, 0.
• Pixels will be sorted in hardware
• Read the color back, and do f-1(Color) to get the
  state

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Generation of Next Choices

• This is done on the CPU
• Create new textures
• Repeat previous steps

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Conclusion

• Proper exploitation of the GPU requires
  parallelization of algorithms
• A is difficult to parallelize. Most parts have
  to happen in sequence.
• Possibly
• Better use each pixel be a unit trying to
  move.
• Problem No branching, loops must be unrolled

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Questions?