GPU acceleration of the particle filter: The Metropolis resampler

We consider deployment of the particle filter on modern massively parallel hardware architectures, such as Graphics Processing Units (GPUs), with a focus on the resampling stage. While standard multinomial and stratified resamplers require a sum of importance weights computed collectively between threads, a Metropolis resampler favourably requires only pair-wise ratios between weights, computed independently by threads, and can be further tuned for performance by adjusting its number of iterations. While achieving respectable results for the stratified and multinomial resamplers, we demonstrate that a Metropolis resampler can be faster where the variance in importance weights is modest, and so is worth considering in a performance-critical context, such as particle Markov chain Monte Carlo and real-time applications.

L.M. Murray (2011). <a href="https://indii.org/research/gpu-acceleration-of-the-particle-filter-the-metropolis-resampler/">GPU acceleration of the particle filter: The Metropolis resampler</a>. <em>DMMD: Distributed machine learning and sparse representation with massive data sets</em>. 

@Article{Murray2011a,
  title = {{GPU} acceleration of the particle filter: The {M}etropolis resampler},
  author = {Lawrence Matthew Murray},
  journal = {DMMD: Distributed machine learning and sparse representation with massive data sets},
  year = {2011},
  url = {http://arxiv.org/abs/1202.6163}
}