In this paper, we revisit, explore and extend the Particle Trajectory Sonification (PTS) model, which supports cluster analysis of high-dimensional data by probing a model space with virtual particles which are ‘gravitationally’ attracted to a mode of the dataset’s potential function. The particles’ kinetic energy progression of as function of time adds directly to a signal which constitutes the sonification. The exponential increase in computation power since

its conception in 1999 enables now for the first time to investigate real-time interactivity in such complex interweaved dynamic sonification models. We speeded up the computation of the PTS model with (i) data optimization via vector quantization, and (ii) parallel computing via OpenCL. We investigated the performance of sonifying high-dimensional complex data under different approaches. The results show a substantial increase in speed when applying vector quantization and parallelism with CPU. GPU parallelism provided a substantial speedup for very large number of

particles comparing to using CPU but did not show enough benefit for a low number of particles due to copying overhead. A hybrid OpenCL implementation is presented to maximize the benefits of both worlds.