Clusters are, by now, one of most popular architectures. Hiding latencies as well as getting an optimal assignment of processors, are two issues for many scientific applications, specially if non dedicated clusters are used. Traditionally, high performance scientific applications are parallelized using MPI libraries. Typical implementations of MPI, minimize dynamic features required to face latencies or shared resource usage. Switching from the MPI process model to a threaded programming model in the parallel environment, can help to achieve efficient overlapping and provide abilities for load balancing. Gains achieved by BICAV (our research group's tomographic reconstruction software) in a multithreaded framework, are exposed.