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Copyright c○2003 by the authors. Simple Parallel Statistical Computing in R Theoretically, many modern statistical procedures are trivial to parallelize. However, practical deployment of a parallelized implementation which is robust and reliably runs on different computational cluster configurations and environments is far from trivial. We present a framework for the R statistical computing language that provides a simple yet powerful programming interface to a computational cluster. This interface allows the development of R functions that distribute independent computations across the nodes of the computational cluster. The resulting framework allows statisticians to obtain significant speedups for some computations at little additional development cost. The particular Modern computer processors are now sufficiently powerful to make many statistical computations seem instantaneous. However, important situations still exist where a single result can require days to compute.
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Anthony Rossini
Dana-Farber Cancer Institute
Luke Tierney
University of Iowa
Na Li
Southwest Petroleum University
Journal of Computational and Graphical Statistics
University of Minnesota System
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Rossini et al. (Thu,) studied this question.
synapsesocial.com/papers/6a0ee65db7cc3b883f22dde1 — DOI: https://doi.org/10.1198/106186007x178979