Load Balanced Resampling for Real-Time Particle Filtering on Graphics Processing Units

Abstract

The application of particle filters to real-time systems is often limited because of their computational complexity, and hence the use of graphics processing units (GPUs) that contain hundreds of processing elements on a chip is very promising. However, parallel implementations of particle filters with state-of-the-art systematic resampling on a GPU suffer from a severe workload imbalance problem, which results in fluctuation of the computation speed and hinders their application to real-time systems. We analyze the computational load imbalance of the systematic resampling method in conventional implementations, and show that the workload imbalance is proportional to the variance of weights in particle filters. Then, we propose a load balanced particle replication (LBPR) algorithm for systematic resampling, which shows almost constant execution speed and outperforms the conventional algorithm in terms of the worst-case computation time. The proposed algorithm has been implemented on an NVIDIA GTX580 GPU.