Semantics Consistent Parallelism

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Semantics Consistent Parallelism

• Li-Yi Wei
• Microsoft Research

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Parallelism
parallel
parallel
parallel
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Traditional parallelization

• Sequential consistency Lampert
• parallel algorithms do NOT change result
• sorting, FFT, matrix, etc.

sort
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New parallelization (cheating)

• Not all algorithms need to be seq-consistent
• perception (graphics/vision/image/video),
  statistics
• approximate solutions might suffice ? more
  parallel
• Semantic consistency

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Examples
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Pseudo Random Number Generator

• The main source of randomness in programs
• e.g. rand() in c/c
• Desirable properties
• white noise statistics
• repeatable, fast computation
• Traditional sequential method
• e.g. xn (a xn-1 b) mod c

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Parallel PRNG

• input trivially prepared in parallel, e.g. linear
  ramp
• feed input value into hash, independently in
  parallel
• output white noise
• key idea
• borrow cryptographic hash!
• Tzeng and Wei I3D 2008

input
hash
output
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How to start a bar flight
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How to stop the bar flight
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Poisson disk sampling

• A set of samples that are
• as random as possible
• remain a minimum distance r away from each other
• Why pick this problem?
• important algorithm
• - sampling, graphics, statistics
• seemly non-parallelizable

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Dart throwing Cook 1986

• Loop
• random sample from the entire domain
• accept sample if not in conflict with existing
  ones
• O High quality
• ground truth
• X Slow speed
• inherently sequential

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Parallel Poisson disk samplingWei SIGGRAPH 2008

• Samples from a grid
• 1 sample per grid cell
• Sample grid cells far apart in parallel
• Watch out for bias!
• Tricks to avoid bias

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Image cloning
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http//www.freewebs.com/cheesesandwiches/cloning.j
pg http//mahboubian.googlepages.com/dog-bird.jpg
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Poisson image editingPerez et al. SIGGRAPH 2003

• State-of-art for image cloning other stuff
• Solving Poisson equation
• interior detail (source)
• boundary condition (target)
• Heavy computation

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Coordinate interpolation

• Farbman et al. SIGGRAPH 2009
• p ? wi bi
• Easy computation
• Run parallel on a GPU

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Summary

• Conclusion
• sequential consistency too strict
• semantic consistency (perceptual, statistical,
  etc)
• Future work
• parallelism via semantic consistency
• individual algorithms / applications
• general methodology?

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Parallelism

• Processors are becoming parallel
• Intel Larrabee, NVIDIA, AMD/ATI, IBM/Sony Cell,
  etc.
• So are programming interfaces
• BSGP, CUDA, CAL, Ct, DX, OpenGL, etc.
• As well as applications
• To take advantage of parallel environment

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Parallel Algorithm

• Traditional parallelization methods
• Sequential consistency Lampert
• - sorting, FFT, matrix, etc.
• Not all algorithms need to be seq-consistent
• perception (graphics/vision/image/video),
  statistics
• Approximate solutions might suffice
• Opportunities for new parallelization methods

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Image cloning
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