<p>There are a few answers on SO, such as <a href="https://stackoverflow.com/questions/14106010/parallel-execution-of-random-forest-in-r">parallel execution of random forest in R</a> and <a href="https://stackoverflow.com/questions/7830255/suggestions-for-speeding-up-random-forests/7831848#7831848">Suggestions for speeding up Random Forests</a>, that I would take a look at.</p>

<p>Those posts are helpful, but are a bit older. the <code>ranger</code> package is an especially fast implementation of random forest, so if you are new to this it might be the easiest way to speed up your model training. <a href="http://arxiv.org/pdf/1508.04409.pdf" rel="nofollow noreferrer">Their paper</a> discusses the tradeoffs of some of the available packages - depending on your data size and number of features, which package gives you the best performance will vary.</p>
Parallelizing random forests
<p>Through searching and asking, I've found many packages I can use to make use of all the cores of my server, and many packages that can do random forest. </p>

<p>I'm quite new at this, and I'm getting lost between all the ways to parallelize the training of my random forest. Could you give some advice on reasons to use and/or avoid each of them, or some specific combinations of them (and with or without <code>caret</code> ?) that have made their proof ?</p>

<p>Packages for parallelization : </p>

<p><code>doParallel</code>, </p>

<p><code>doSNOW</code>, </p>

<p><code>doSMP</code> (discontinued ?), </p>

<p><code>doMC</code> </p>

<p>(and what about <code>mclapply</code> ?)</p>

<hr>

<p>Packages for random forest : </p>

<p>[<code>caret</code> + some of the following] </p>

<p><code>rf</code>, </p>

<p><code>parRF</code>, </p>

<p><code>randomForest</code>, </p>

<p><code>ranger</code>, </p>

<p><code>Rborist</code>,</p>

<p><code>parallelRandomForest</code> (crashes my R Studio session...)</p>

<p>Thanks</p>