AbstractThe concept of parallelism of computations is very important to obtain the results required by the algorithms of big data processing applied in several domains, like ML, AI, neural networks, approximate computing, etc. In this chapter the use of and stations and of their respective generation/synchronization policies are described [12]. Three elementary models implementing different synchronization policies are analyzed. The first synchronizes the executions of n parallel tasks with similar characteristics, the second investigates the impact of the variability of the parallel task execution times on the synchronization times, and the third synchronizes on the fastest task. In spite of their simplicity, these three models can be combined with other components to implement very complex models.