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The famous websites provide tons of useful information:
- <a href="http://r-pbd.org/"
target="_blank">Programming with Big Data in R</a>
- Training of <a href="http://rdav.nics.tennessee.edu/"
target="_blank">Remote Data Analysis and Visualization Center</a>
- CRAN Task View:
<a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html"
target="_blank">High-Performance and Parallel Computing with R</a>
- Section
<a href="http://www.math.ncu.edu.tw/~chenwc/R_note/index.php?item=Rmpi">LAM/MPI/Rmpi</a>"
of
<a href="http://www.math.ncu.edu.tw/~chenwc/R_note">R_note</a>
website
- <a href="http://math.acadiau.ca/ACMMaC/Rmpi/"
target="_blank">Rmpi Tutorial</a>
by
<a href="http://math.acadiau.ca/ACMMaC/"
target="_blank">Acadia Centre for Mathematical Modelling and Computation</a>
- <a href="http://www.stats.uwo.ca/faculty/yu/Rmpi/"
target="_blank">Rmpi for R</a>
by
<a href="http://www.stats.uwo.ca/faculty/yu/"
target="_blank">Dr. Hao Yu</a>
The famous book provides other parallel techniques in R:
- Q.E. McCallum and S. Weston (2011),
<i><a href="http://shop.oreilly.com/product/0636920021421.do"
target="_blank">Parallel R</a> Data Analysis in the Distributed World</i>,
O'Reilly Media.
The famous mailing list provides a discussion group about high-performance computing in <code>R</code>:
- <a href="https://stat.ethz.ch/mailman/listinfo/r-sig-hpc"
target="_blank">R-sig-hpc</a> mailing list.
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