In this paper, we present efficient, scalable, and portable parallel algorithms for the off-line clustering, the on-line retrieval and the update phases of the Text Retrieval (TR) problem based on the vector space model and using clustering to organize and handle a dynamic document collection. The algorithms are running on the Coarse-Grained Multicomputer (CGM) and/or the Bulk Synchronous Parallel (BSP) model which are two models that capture within a few parameters the characteristics of the parallel machine. To the best of our knowledge, our parallel retrieval algorithms are the first ones analyzed under these specific parallel models. For all the phases of the proposed algorithms, we analytically determine the relevant communication and computation cost thereby formally proving the efficiency of the proposed solutions. In addition, we prove that our technique for the on-line retrieval phase performs very well in comparison to other possible alternatives in the typical case of a multiuser information retrieval (IR) system where a number of user queries are concurrently submitted to an IR system. Finally, we discuss external memory issues and show how our techniques can be adapted to the case when processors have limited main memory but sufficient disk capacity for holding their local data.