I have just started to get myself acquainted with parallelism in R.
As I am planning to use Microsoft Azure Machine Learning Studio for my project, I have started investigating what Microsoft R Open offers for parallelism, and thus, I found this, in which it says that parallelism is done under the hood that leverages the benefit of all available cores, without changing the R code. The article also shows some performance benchmarks, however, most of them demonstrate the performance benefit in doing mathematical operations.
This was good so far. In addition, I am also interested to know whether it also parallelize the *apply functions under the hood or not. I also found these 2 articles that describe how to parallelize *apply functions in general:
A quick guide to parallel R with snow: describes facilitating parallelism using snow package, par*apply function family, and clusterExport.
A gentle introduction to parallel computing in R: using parallel package, par*apply function family, and binding values to the environment.
So my question is when I will be using *apply functions in Microsoft Azure Machine Learning Studio, will that be parallelized under the hood by default, or I need to make use of packages like parallel, snow, etc.?