The particle filter is a Bayesian estimation technique based on Monte Carlo simulation. It is ideal for non-linear, nonGaussian dynamical systems with applications in many areas, such as computer vision, robotics, and econometrics. Practical use has so far been limited, because of steep computational requirements. In this study, we investigate how to design a particle filter framework for complex estimation problems using many-core architectures. We develop a robotic arm application as a highly flexible estimation problem to push estimation rates and accuracy to new levels. By varying filtering and model parameters, we evaluate our particle filter extensively and derive rules of thumb for good configurations. Using our robotic arm application, we achieve a few hundred state estimations per second with one million particles. With our framework, we make a significant step towards a wider adoption of particle filters and enable studies into filtering setups for even larger estimation problems.