A Comparison of Serial & Parallel Particle Filters for Time Series Analysis

Abstract

<p>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 </p><p>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 </p><p>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.</p>