title: Using IPython/Jupyter cluster with R kernel
body: <p>I would like to use IPython/Jupyter to set up 100 jobs on a computing cluster to perform some calculations.</p>
  
  <p>In Python, I would do the following</p>
  
  <pre><code>from IPython.parallel import Client
  c = Client()
  c[:].apply_sync(lambda : "Hello, World")
  </code></pre>
  
  <p>Or, using ipython-cluster-helper</p>
  
  <pre><code>with cluster_view(scheduler="lsf", queue="myqueue", num_jobs=100) as view:
      result = view.map(myfunc, params)
  </code></pre>
  
  <p>Is it possible to access IPython's parallel abilities from an R kernel?</p>
  
  <p>If yes, how?</p>
  
  <p>There are some parallel processing capabilities in R, but this would need to be a function provided by the IRkernel.</p>
  
  <p>I would expect that if I run the line below (with a corresponding function to access the IPython/Jupyter cluster), I would see parallel execution times:</p>
  
  <pre><code>parallel_access_func(1:4, function(x) { re = as.character(Sys.time()); Sys.sleep(5); re })
  </code></pre>