A Comparison of Serial & Parallel Particle Filters for Time Series Analysis
This paper discusses the application of parallel programming techniques to the estimation of hidden Markov models via the use of a particle filter. It highlights how the Thrust parallel programming
language can be used to implement a particle filter in parallel. The impact of a parallel particle filter on the running times of three different models is investigated. For particle filters using a large number
of particles, Thrust provides a speed-up of five to ten times over a serial C++ implementation, which is less than reported in other research.
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