Parallel processing in R

//Parallel processing in R

I’ve tried a few different approaches for parallel processing in R but the function I’ve found easiest to use is foreach. The only trick is that you need to register a “parallel backend” – doMC works well for unix systems (including OS X).

Specifying the .combine argument allows you to customise how the results are aggregated at the end of the loop.

Simple example:

require(doMC)
require(foreach)
registerDoMC(cores=3)
n=10
result = foreach(j = 1:n, .combine=rbind) %dopar% {
  # EXPERIMENT
  # last line is returned as a row in the result matrix
  rep(j,4)
}

The output:

result
          [,1] [,2] [,3] [,4]
result.1     1    1    1    1
result.2     2    2    2    2
result.3     3    3    3    3
result.4     4    4    4    4
result.5     5    5    5    5
result.6     6    6    6    6
result.7     7    7    7    7
result.8     8    8    8    8
result.9     9    9    9    9
result.10   10   10   10   10
By | 2014-09-26T04:47:56+00:00 September 26th, 2014|Statistics|0 Comments

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