Acceleration of sequential Monte Carlo for computationally intensive target distribution by parallel lookup table and GPU computing. (2018) - Zhao, Di
Sequential Monte Carlo (SMC) is the key solver for applications such as object tracking, signal processing and statistical distribution approximation. However, if the target distribution is complicated, the solution speed of the conventional SMC is too slow to satisfy the real-time requirement of applications. In this paper, by the novel idea of GPU-based lookup table (GPULTU), the acceleration method for the conventional SMC (LTU-GPU accelerated SMC) is developed, and the efficiency of LTU-GPU accelerated SMC by a statistical approximation problem is illustrated. Computational results show that LTU-GPU accelerated SMC is significantly faster than the conventional SMC from hours to seconds.