ArticleConcurrency and computation : practice & experience2011
Optimization of a parallel permutation testing function for the SPRINT R package.
Article in Concurrency and computation : practice & experience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Parallel classification and feature selection in microarray data using SPRINT.Concurrency and computation : practice & experience · 2014Article
- Exploiting parallel R in the cloud with SPRINT.Methods of information in medicine · 2013Article
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Authors and funding
9 authors.
Funding
Abstract
The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright © 2011 John Wiley & Sons, Ltd.
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