We seek to create a parallel search engine which outperforms conventional, loosely coupled distributed systems. We have (1) parallelized the vector space model with 120%-180% parallel efficiency, (2) introduced a highly parallel algorithm for text dimensionality reduction increasing the search accuracy measured with the mean average precision by 4.8 percentage points on the Reuters corpus and (3) developed a middleware for concurrent programming in parallel applications for index maintenance and multi-user operation. Using these building blocks, we present an overall system architecture that addresses the requirements of information retrieval as a persistently deployed parallel service.