Data are often described at several levels of granularity. For instance, data concerning fruits that are purchased can be categorized regarding some criteria (such as size, weight, color, etc.). When dealing with data from the real world, such categories can hardly be defined in a crisp manner. For instance, some fruits may belong both to the small and medium-sized fruits. Data mining methods have been proposed to deal with such data, in order to take benefit from the several levels when extracting relevant patterns. The challenge is to discover patterns that are not too general (as they would not contain relevant novel information) while remaining typical (as detailed data do not embed general and representative information). In this paper, we focus on the extraction of gradual patterns in the context of hierarchical data. Gradual patterns describe covariation of attributes such as the bigger, the more expensive. As our proposal increases the number of combinations to be considered since all levels must be explored, we propose to implement the parallel computation in order to decrease the execution time.