Matrix Profile (MP) has been proposed as a powerful technique for knowledge extraction from time series. Several algorithms have been proposed for computing it, e.g., STAMP and STOMP. Currently, MP is computed based on 1NN search in all subsequences of the time series. In this paper, we claim that a kNN MP can be more useful than the 1NN MP for knowledge extraction, and propose an efficient technique to compute such a MP. We also propose an algorithm for parallel execution of kNN MP by using multiple cores of an off-the-shelf computer. We evaluated the performance of our solution by using multiple real datasets. The results illustrate the superiority of kNN MP for knowledge discovery compared to 1NN MP.