Parallel rollout is a formal method of combining multiple heuristic policies available to a sequential decision maker in the framework of Markov Decision Processes (MDPs). The method improves the performances of all of the heuristic policies adapting to the different stochastic system trajectories. From an inherent multi-level parallelism in the method, in this paper we propose a parallelized version of parallel rollout algorithm, and evaluate its performance on a multi-class task scheduling problem by using OpenMP and MPI programming model. We analyze and compare the performance in two versions of parallelized codes, e.g., OpenMP and MPI on several execution environment. We show that the performance using OpenMP API is higher than MPI due to lower overhead in data synchronization across processors.