add RMS example for iterating over MapResult

docs/source/parallel/asyncresult.txt

@@ -101,11 +101,45 b' Map results are iterable!'
 =========================
 
 When an AsyncResult object has multiple results (e.g. the :class:`~AsyncMapResult`
-object), you can actually iterate through them, and act on the results as they arrive:
+object), you can actually iterate through results themselves, and act on them as they arrive:
 
 .. literalinclude:: ../../examples/parallel/itermapresult.py
     :language: python
     :lines: 20-67
+    
+That is to say, if you treat an AsyncMapResult as if it were a list of your actual
+results, it should behave as you would expect, with the only difference being
+that you can start iterating through the results before they have even been computed.
+
+This lets you do a dumb version of map/reduce with the builtin Python functions,
+and the only difference between doing this locally and doing it remotely in parallel
+is using the asynchronous view.map instead of the builtin map.
+
+
+Here is a simple one-line RMS (root-mean-square) implemented with Python's builtin map/reduce.
+
+.. sourcecode:: ipython
+
+    In [38]: X = np.linspace(0,100)
+
+    In [39]: from math import sqrt
+
+    In [40]: add = lambda a,b: a+b
+
+    In [41]: sq = lambda x: x*x
+
+    In [42]: sqrt(reduce(add, map(sq, X)) / len(X))
+    Out[42]: 58.028845747399714
+
+    In [43]: sqrt(reduce(add, view.map(sq, X)) / len(X))
+    Out[43]: 58.028845747399714
+
+To break that down:
+
+1. ``map(sq, X)`` Compute the square of each element in the list (locally, or in parallel)
+2. ``reduce(add, sqX) / len(X)`` compute the mean by summing over the list (or AsyncMapResult)
+   and dividing by the size
+3. take the square root of the resulting number
 
 .. seealso::