title: Parallel *apply in Azure Machine Learning Studio
body: <p>I have just started to get myself acquainted with parallelism in R. </p>
  
  <p>As I am planning to use <a href="https://studio.azureml.net/" rel="nofollow">Microsoft Azure Machine Learning Studio</a> for my project, I have started investigating what <a href="https://mran.revolutionanalytics.com/documents/rro/multithread/" rel="nofollow">Microsoft R Open</a> offers for parallelism, and thus, I found <a href="https://mran.revolutionanalytics.com/documents/rro/multithread/" rel="nofollow">this</a>, 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.</p>
  
  <p>This was good so far. In addition, I am also interested to know whether it also parallelize the <code>*apply</code> functions under the hood or not. I also found these 2 articles that describes how to parallelize <code>*apply</code> functions in general:</p>
  
  <ol>
  <li><a href="https://www.r-bloggers.com/quick-guide-to-parallel-r-with-snow/" rel="nofollow">Quick guide to parallel R with snow</a>: describes facilitating parallelism using <a href="https://cran.r-project.org/web/packages/snow/snow.pdf" rel="nofollow"><code>snow</code></a> package, <code>par*apply</code> function family, and <code>clusterExport</code>.</li>
  <li><a href="http://www.win-vector.com/blog/2016/01/parallel-computing-in-r/" rel="nofollow">A gentle introduction to parallel computing in R</a>: using <code>parallel</code> package, <code>par*apply</code> function family, and binding values to environment.</li>
  </ol>
  
  <p>So my question is when I will be using <code>*apply</code> 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 <code>parallel</code>, <code>snow</code> etc.?</p>