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Multiplexing Exercises
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Monte Carlo π
=============

A simple toy problem to get a handle on multiple engines is a Monte Carlo approximation
of π.

Let's say we have a dartboard with a round target inscribed on a square board.  If you
threw darts randomly, and they land evenly distributed on the square board, how many 
darts would you expect to hit the target?


.. image:: figs/darts.png


.. literalinclude:: _static/code/mcpi.py
    :language: python

It takes a lot of samples to get a good approximation.  Can you write a function that
will use your engines to break up the work?

`mcpi solution <_static/soln/mcpi_soln.py>`_

Remote Iterators
================

Can you create an object that iterates through a remote iterable?

.. sourcecode:: ipython

    In [10]: dview['a'] = range(5)
    
    In [11]: ra = remote_iterator(dview, 'a')

`remote iter solution <_static/soln/remoteiter.py>`_