The molecular weight distribution (MWD) is at the core of establishing key quality indices for free radical polymerization processes. Due to large-scale features of the rigorous model, consisting of a very large number of differential and algebraic equations, dynamic simulation of MWD is always challenging. A sequential variable decoupling method (SVD) has been proposed to calculate the MWD for any reasonably large chain-length number. In the current paper, parallel computing methods were developed to accelerate the MWD simulation. Both coarse-grained and fine-grained parallelism methods have been proposed. A theoretical analysis of the proposed methods was conducted to demonstrate its high efficiency in parallel computing. Both Intel multi-core-processor-based and NVIDIA graphics-processing-unit-based parallel computing platforms were implemented, achieving significant speedups in computation for MWD simulation. (c) 2012 Elsevier Ltd. All rights reserved.