  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.
