Particle filtering on GPU architectures for manufacturing applications

Abstract

Particle filters are nonlinear estimators that can be used to detect anomalies in manufacturing processes. Although promising, their high computational cost often prevents their implementation in real-time applications. Recently, the introduction of graphics processing units (GPUs) has enabled the acceleration of computationally intensive processes with their massive parallel capabilities. This article presents the acceleration of the particle filter and the auxiliary particle filter, two of the most important particle methods, on a GPU using NVIDIA CUDA technology. This is illustrated via simulation for a remelting process where the accelerated algorithms return accurate estimates while still being two orders of magnitude faster than the physical process even for calculations that involve millions of particles.