The sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter suffers from low computational efficiency since a large number of particles are often required, especially when there are a large number of targets and dense clutter. In order to speed up the computation, an algorithmic framework for parallel SMC-PHD filtering based on multiple processors is proposed. The algorithm makes full parallelization of all four steps of the SMC-PHD filter and the computational load is approximately equal among parallel processors, rendering a high parallelization benefit when there are multiple targets and dense clutter. The parallelization is theoretically unbiased as it provides the same result as the serial implementation, without introducing any approximation. Experiments on multi-core computers have demonstrated that our parallel implementation has gained considerable speedup compared to the serial implementation of the same algorithm.