Particle Swarm Optimization (PSO) has received attention in many research fields and real-world applications for solving optimization problems in the areas of intelligent transportation systems, wireless sensor networks, finance, and engineering. Factor that affects the performance of PSO is its ability of the exploration in a multi-dimensional search space, which can increase the execution time quite significantly. The parallel implementation of PSO is a way to address this. In this paper, we implement and compare the parallel implementation of PSO using two different parallelization techniques using MapReduce programming, 1) all nodes in the cluster work on the same population, and 2) each node in cluster has its own population. Both of the parallel implementations are compared based on performance and speedup. Parallel implementation of the PSO algorithm makes the algorithm faster and scalable in order to find best solutions while working with large datasets in high dimensional search spaces.