Parallelising particle filters with deterministic runtime on distributed memory systems

Particle Filters are parallelisable. However, the resampling step, which is necessary to handle the degeneracy problem, is non-trivial to parallelise. A textbook implementation of this step achieves O (N) time complexity with a very low constant but deriving an efficient parallel implementation of the same is non-trivial. Several alternative solutions have been proposed. A state-of-the-art parallelisable algorithm has O (log
₂
N)
²
time complexity and has been implemented in the Big data MapReduce context. However, portability of this algorithm is difficult to achieve, especially on distributed memory systems. In paper, we reformulate this algorithm for the distributed memory setup and demonstrate that the same algorithm can be almost twice as fast as the exiting state-of-theart algorithm for up to 128 cores. Furthermore, we also show that our algorithm is 20 times faster than the serial version of the algorithm.