fix typo in psum example

docs/source/parallel/parallel_mpi.txt

@@ -1,154 +1,154 b''
 .. _parallelmpi:
 
 =======================
 Using MPI with IPython
 =======================
 
 Often, a parallel algorithm will require moving data between the engines. One
 way of accomplishing this is by doing a pull and then a push using the
 multiengine client. However, this will be slow as all the data has to go
 through the controller to the client and then back through the controller, to
 its final destination.
 
 A much better way of moving data between engines is to use a message passing
 library, such as the Message Passing Interface (MPI) [MPI]_. IPython's
 parallel computing architecture has been designed from the ground up to
 integrate with MPI. This document describes how to use MPI with IPython.
 
 Additional installation requirements
 ====================================
 
 If you want to use MPI with IPython, you will need to install:
 
 * A standard MPI implementation such as OpenMPI [OpenMPI]_ or MPICH.
 * The mpi4py [mpi4py]_ package.
 
 .. note::
 
     The mpi4py package is not a strict requirement. However, you need to
     have *some* way of calling MPI from Python. You also need some way of
     making sure that :func:`MPI_Init` is called when the IPython engines start
     up. There are a number of ways of doing this and a good number of
     associated subtleties. We highly recommend just using mpi4py as it
     takes care of most of these problems. If you want to do something
     different, let us know and we can help you get started.
 
 Starting the engines with MPI enabled
 =====================================
 
 To use code that calls MPI, there are typically two things that MPI requires.
 
 1. The process that wants to call MPI must be started using
    :command:`mpiexec` or a batch system (like PBS) that has MPI support.
 2. Once the process starts, it must call :func:`MPI_Init`.
 
 There are a couple of ways that you can start the IPython engines and get
 these things to happen.
 
 Automatic starting using :command:`mpiexec` and :command:`ipcluster`
 --------------------------------------------------------------------
 
 The easiest approach is to use the `MPI` Launchers in :command:`ipcluster`,
 which will first start a controller and then a set of engines using
 :command:`mpiexec`::
 
     $ ipcluster start -n 4 --engines=MPIEngineSetLauncher
 
 This approach is best as interrupting :command:`ipcluster` will automatically
 stop and clean up the controller and engines.
 
 Manual starting using :command:`mpiexec`
 ----------------------------------------
 
 If you want to start the IPython engines using the :command:`mpiexec`, just
 do::
 
     $ mpiexec -n 4 ipengine --mpi=mpi4py
 
 This requires that you already have a controller running and that the FURL
 files for the engines are in place. We also have built in support for
 PyTrilinos [PyTrilinos]_, which can be used (assuming is installed) by
 starting the engines with::
 
     $ mpiexec -n 4 ipengine --mpi=pytrilinos
 
 Automatic starting using PBS and :command:`ipcluster`
 ------------------------------------------------------
 
 The :command:`ipcluster` command also has built-in integration with PBS. For
 more information on this approach, see our documentation on :ref:`ipcluster
 <parallel_process>`.
 
 Actually using MPI
 ==================
 
 Once the engines are running with MPI enabled, you are ready to go. You can
 now call any code that uses MPI in the IPython engines. And, all of this can
 be done interactively. Here we show a simple example that uses mpi4py
 [mpi4py]_ version 1.1.0 or later.
 
 First, lets define a simply function that uses MPI to calculate the sum of a
 distributed array. Save the following text in a file called :file:`psum.py`:
 
 .. sourcecode:: python
 
     from mpi4py import MPI
     import numpy as np
 
     def psum(a):
         locsum = np.sum(a)
         rcvBuf = np.array(0.0,'d')
         MPI.COMM_WORLD.Allreduce([locsum, MPI.DOUBLE],
             [rcvBuf, MPI.DOUBLE],
             op=MPI.SUM)
         return rcvBuf
 
 Now, start an IPython cluster::
 
     $ ipcluster start --profile=mpi -n 4
     
 .. note::
 
     It is assumed here that the mpi profile has been set up, as described :ref:`here
     <parallel_process>`.
 
 Finally, connect to the cluster and use this function interactively. In this
 case, we create a random array on each engine and sum up all the random arrays
 using our :func:`psum` function:
 
 .. sourcecode:: ipython
 
     In [1]: from IPython.parallel import Client
 
     In [2]: c = Client(profile='mpi')
 
     In [3]: view = c[:]
 
-    In [4]: view.activate() # enabe magics
+    In [4]: view.activate() # enable magics
 
     # run the contents of the file on each engine:
     In [5]: view.run('psum.py')
 
     In [6]: view.scatter('a',np.arange(16,dtype='float'))
 
     In [7]: view['a']
     Out[7]: [array([ 0.,  1.,  2.,  3.]),
              array([ 4.,  5.,  6.,  7.]),
              array([  8.,   9.,  10.,  11.]),
              array([ 12.,  13.,  14.,  15.])]
 
     In [7]: %px totalsum = psum(a)
     Parallel execution on engines: [0,1,2,3]
 
     In [8]: view['totalsum']
     Out[8]: [120.0, 120.0, 120.0, 120.0]
 
 Any Python code that makes calls to MPI can be used in this manner, including
 compiled C, C++ and Fortran libraries that have been exposed to Python.
 
 .. [MPI] Message Passing Interface.  http://www-unix.mcs.anl.gov/mpi/
 .. [mpi4py] MPI for Python. mpi4py: http://mpi4py.scipy.org/
 .. [OpenMPI] Open MPI. http://www.open-mpi.org/
 .. [PyTrilinos] PyTrilinos. http://trilinos.sandia.gov/packages/pytrilinos/