parmap 1.2dev

parmap
======

This small python module implements two functions: ``map`` and
``starmap``.

What does parmap offer?
-----------------------

- Provide an easy to use syntax for both ``map`` and ``starmap``.
- Parallelize transparently whenever possible.
- Handle multiple (positional -for now-) arguments as needed.

Usage:
------

Here are some examples with some unparallelized code parallelized with
parmap:

::

import parmap
# You want to do:
y = [myfunction(x, argument1, argument2) for x in mylist]
# In parallel:
y = parmap.map(myfunction, mylist, argument1, argument2)

# You want to do:
z = [myfunction(x, y, argument1, argument2) for (x,y) in mylist]
# In parallel:
z = parmap.starmap(myfunction, mylist, argument1, argument2)

# Yoy want to do:
listx = [1, 2, 3, 4, 5, 6]
listy = [2, 3, 4, 5, 6, 7]
param = 3.14
param2 = 42
listz = []
for x in listx:
for y in listy:
listz.append(myfunction(x, y, param1, param2))
# In parallel:
listz = parmap.starmap(myfunction, zip(listx, listy), param1, param2)


map (and starmap on python 3.3) already exist. Why reinvent the wheel?
----------------------------------------------------------------------

Please correct me if I am wrong, but from my point of view, existing
functions have some usability limitations:

- The built-in python function ``map`` [#builtin-map]_
is not able to parallelize.
- ``multiprocessing.Pool().starmap`` [#multiproc-starmap]_
is only available in python-3.3 and later versions.
- ``multiprocessing.Pool().map`` [#multiproc-map]_
does not allow any additional argument to the mapped function.
- ``multiprocessing.Pool().starmap`` allows passing multiple arguments,
but in order to pass a constant argument to the mapped function you
will need to convert it to an iterator using
``itertools.repeat(your_parameter)`` [#itertools-repeat]_

``parmap`` aims to overcome this limitations in the simplest possible way.

Additional features in parmap:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- Create a pool for parallel computation automatically if possible.
- ``parmap.map(..., ..., parallel=False)`` # disables parallelization
- ``parmap.map(..., ..., chunksize=3)`` # size of chunks (see
multiprocessing.Pool().map)
- ``parmap.map(..., ..., pool=multiprocessing.Pool())`` # use an existing
pool

To do:
------

Pull requests and suggestions are welcome.

- See if anyone is interested on this
- Pass keyword arguments to functions?
- Improve exception handling
- Sphinx documentation?

Acknowledgments:
----------------

The original idea for this implementation was given by J.F. Sebastian at
http://stackoverflow.com/a/5443941/446149


References
-----------

.. [#builtin-map] http://docs.python.org/dev/library/functions.html#map
.. [#multiproc-starmap] http://docs.python.org/dev/library/multiprocessing.html#multiprocessing.pool.Pool.starmap
.. [#multiproc-map] http://docs.python.org/dev/library/multiprocessing.html#multiprocessing.pool.Pool.map
.. [#itertools-repeat] http://docs.python.org/2/library/itertools.html#itertools.repeat