Parallel high-performance computing of Bayes estimation for signal processing and metrology - Garcia, Elmar; Zschiegner, Nils; Hausotte, Tino (Proceedings of the IEEE International Conference on Computing, Management & Telecommunications, 2013)
The Bayesian theorem is the most used instrument for stochastic inferencing in nonlinear dynamic systems. The algorithmic implementations of the recursive Bayesian estimation for arbitrary systems are the particle filters (PFs). They are sampling-based sequential Monte-Carlo methods, which generate a set of samples to compute an approximation of the Bayesian posterior probability density function. Thus, the PF faces the problem of high computational burden, since it converges to the true posterior when number of particles N-P -> infinity. In order to solve these computational problems a highly parallelized C++ library, called Parallel Bayesian Toolbox (PBT), for implementing Bayes filters (BFs) was developed and released as open-source software, for the first time [1]. It features a high level language interface for numerical calculations and very efficient usage of available central processing units (CPUs) and graphics processing units (GPUs). This significantly increases the computational throughput without the need of special hardware such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs).
UnivIS-Import:2015-04-16:Pub.2013.tech.FT.lfmt.parall_1
Keywords: Particle filter; Kalman filter; Bayesan estimation; stochastic signal processing; parallel computing; CUDA; open source