Uncovering the underlying community structure of the Internet at the AS level is essential way to gain insight both into its structure and its functional organization. Of all the de?nitions of community proposed by researchers, we focused on the k-clique community de?nition as we believe it best catches the characteristics of the Internet AS-level topology. Extracting k-clique communities using the methods available in the literature requires a formidable amount of computational load and memory resources. In this paper we propose a new parallel method that has proved its capability in extracting communities e?ciently and e?ectively from realworld complex networks, including the Internet at the AS level. This innovative method is much less resource intensive than Clique Percolation Method and experimental results show it is always at least an order of magnitude faster.