The analysis of post genomic data is increasingly becoming harder to perform on standard computing infrastructures due to the sheer amount of data involved requiring more disk space and longer processing times. High Performance Computing (HPC) is an obvious answer to the need for more computing power. Access to computer clusters is common now with HPC resources becoming available to all through local or national initiatives such as the UK supercomputing service HECToR. However, the transition from general computing, such as R Language and Environment for Statistical Computing, to parallel computing is not straight forward. Software application and tools have to be adapted to take advantage of the extra computing power. SPRINT aims to provide bioinformaticians using R/Bioconductor to analyse microarray data with easy access to HPC providing maximum performance but requiring minimal expert knowledge and minimal changes to existing R scripts. The SPRINT framework consists of an HPC harness and a library of parallelized R functions. SPRINT is very flexible; it runs on a range of HPC systems and allows the addition of user contributed functions. It handles functions that are trivial to parallelize, functions that are non trivial to parallelize and functions generating very large output.