%0 Journal Article
%T SPRINT: A new parallel framework for R
%A Jon Hill
%A Matthew Hambley
%A Thorsten Forster
%A Muriel Mewissen
%A Terence M Sloan
%A Florian Scharinger
%A Arthur Trew
%A Peter Ghazal
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-558
%X We have designed and built a prototype framework that allows the addition of parallelised functions to R to enable the easy exploitation of HPC systems. The Simple Parallel R INTerface (SPRINT) is a wrapper around such parallelised functions. Their use requires very little modification to existing sequential R scripts and no expertise in parallel computing. As an example we created a function that carries out the computation of a pairwise calculated correlation matrix. This performs well with SPRINT. When executed using SPRINT on an HPC resource of eight processors this computation reduces by more than three times the time R takes to complete it on one processor.SPRINT allows the biostatistician to concentrate on the research problems rather than the computation, while still allowing exploitation of HPC systems. It is easy to use and with further development will become more useful as more functions are added to the framework.The last few years have seen the widespread introduction of high-throughput and highly parallel post genomic experiments to biological research, leading to hardware bottlenecks in the analysis of such high-dimensional data. Microarray-based techniques are a prominent example, allowing for simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples [1]. These measurements can represent the expression of all genes in the human genome across thousands of cancer tissue samples, or the individual gene sequence differences between thousands of patients [2,3]. These studies have generated an unprecedented amount of data and tested the limits of existing bioinformatics computing infrastructure, for example, whole genome analysis becomes intractable for any experiment with more than a few hundred arrays, depending on hardware available. Emerging whole genome associative studies and clinical projects will require from several hundreds to several thousands of microarray experiments. The complexity
%U http://www.biomedcentral.com/1471-2105/9/558