Parallel Optimisation of Bootstrapping in R

T. M. Sloan; M. Piotrowski; T. Forster; P. Ghazal

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arXiv · 1401.6389

Parallel Optimisation of Bootstrapping in R

T. M. Sloan·M. Piotrowski·T. Forster·P. Ghazal

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Abstract

Bootstrapping is a popular and computationally demanding resampling method used for measuring the accuracy of sample estimates and assisting with statistical inference. R is a freely available language and environment for statistical computing popular with biostatisticians for genomic data analyses. A survey of such R users highlighted its implementation of bootstrapping as a prime candidate for parallelization to overcome computational bottlenecks. The Simple Parallel R Interface (SPRINT) is a package that allows R users to exploit high performance computing in multi-core desktops and supercomputers without expert knowledge of such systems. This paper describes the parallelization of bootstrapping for inclusion in the SPRINT R package. Depending on the complexity of the bootstrap statistic and the number of resamples, this implementation has close to optimal speed up on up to 16 nodes of a supercomputer and close to 100 on 512 nodes. This performance in a multi-node setting compares favourably with an existing parallelization option in the native R implementation of bootstrapping.

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BibTeX RIS

T. M. Sloan, M. Piotrowski, T. Forster, P. Ghazal. 2014-01-24. Parallel Optimisation of Bootstrapping in R. https://arxiv.org/abs/1401.6389

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