SPRINT: A new parallel framework for R Export

@article{citeulike:3835932, abstract = {BACKGROUND:Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest.RESULTS: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.CONCLUSION: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.}, author = {Hill, Jon and Hambley, Matthew and Forster, Thorsten and Mewissen, Muriel and Sloan, Terence and Scharinger, Florian and Trew, Arthur and Ghazal, Peter}, citeulike-article-id = {3835932}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-558}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19114001}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19114001}, doi = {10.1186/1471-2105-9-558}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {r}, number = {1}, pages = {558+}, posted-at = {2009-04-06 11:15:26}, priority = {0}, title = {SPRINT: A new parallel framework for R}, url = {http://dx.doi.org/10.1186/1471-2105-9-558}, volume = {9}, year = {2008} }

TY - JOUR ID - citeulike:3835932 L3 - citeulike-article-id:3835932 N2 - BACKGROUND:Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest.RESULTS: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.CONCLUSION: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. IS - 1 JF - BMC Bioinformatics SN - 1471-2105 TI - SPRINT: A new parallel framework for R VL - 9 SP - 558 KW - r AU - Hill, Jon AU - Hambley, Matthew AU - Forster, Thorsten AU - Mewissen, Muriel AU - Sloan, Terence AU - Scharinger, Florian AU - Trew, Arthur AU - Ghazal, Peter PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-558 ER -