Mining gradual patterns invokes a number of iterations for generating, adjusting, measuring, and comparing gradual tendencies between numeric attributes of imprecise or uncertain databases. Gradual tendencies are complex correlations of the form {The hight/lower X, the hight/lower Y}. Automatic extraction of such gradual patterns involves huge amounts of processing time, load balance, and high memory consumption. When managing large databases, taking this into account is challenging. In this paper, we show a framework and an algorithm based on rank correlation and fuzzy orderings for mining gradual patterns from imprecise or uncertain data. We also present an approach to improve performance of the algorithm using the parallel programming model of OpenMP and the Yale Sparse Matrix Format to reduce memory consumption. Through an experimental study, we show the performance of our approach with respect to the number of attributes of the databases and the number of cores available.