Efficient parallel implementation of a Kalman filter for single output systems on multicore computational platforms

Parallelization and cache memory bandwidth demand of a Kalman filter for single output systems on multicore computers are investigated and exemplified by an adaptive filtering application. By breaking the data dependencies through a re-organization of calculations, an almost completely parallel algorithm is obtained. Analysis of the resulting algorithm brings about an estimate of the memory bandwidth necessary for a linear in the number of cores speedup. An evaluation of the parallel algorithm on two different shared-memory multicore architectures has been performed. It is found that linear speedup in the number of used cores can indeed be achieved provided a sufficient memory bandwidth is offered by the hardware.