This paper presents a method based on the use of Bayesian modal parameter recursive estimation based on a particular Kalman filter algorithm with decoupled distributions for mass and stiffness. Particular Kalman filtering is a combination of two widely used Bayesian estimation methods working together: the particle filter (also called sequential Monte Carlo samplings) and the Kalman filter. Usual system identification techniques for civil and mechanical structures assume the availability of large set of data derived from a stationary quasi steady structure. On the opposite, several scenarios involve time varying structures. For example, due to interaction with aerodynamics in aeronautics, some critical parameter may have to be monitored, for instability monitoring (leading possibly to flutter) of in flight data due to fuel consumption and speed change. This relates to the monitoring of time varying structural parameters such as frequencies and damping ratios. The main idea of a particular Kalman filter is to consider stochastic particles evolving in the parameter space. For each particle, a corresponding linear state is recursively estimated by applying a Kalman filter to the mechanical system, whose modal parameters are driven by the evolution of this time-varying particle. The weight of each particle is computed from the likelihood of the parameter sample it represents and its corresponding state. This result in a bank of adaptive coupled Kalman filters combined with their particle filter. However, the system parametrization is relatively large. In order to provide fast and convincing results for large time varying structure, such as an airplane, the execution time of the method has to be improved. In particular, the particle evolutions can be run in parallel, Within the Cloud2sm project, A Quadro k6000 card of 3072 cores clocked to 3 GB/s has been used. This paper will show a GPGPU implementation of the particular Kalman filter and the first results will be discussed.