  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.
