Skip to main content
Simple Parallel Statistical Computing in R
UW Biostatistics Working Paper Series
  • Anthony Rossini, University of Washington
  • Luke Tierney, University of Iowa
  • Na Li, University of Washington
Date of this Version
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 speed-ups for some computations at little additional development cost. The particular implementation can be deployed in heterogeneous computing environments.
Citation Information
Anthony Rossini, Luke Tierney and Na Li. "Simple Parallel Statistical Computing in R" (2003)
Available at: