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Easy distribution of tasks across multiple CPUs
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.. warning:: This example requires execution on 2 CPUs. It can be run using: ``$ mpirun -n 2 python path/to/parallel_tasks.rst``
All I'm doing here is illustrating the use of ``parallel.map`` with the simplest example I could come up with. I create a list which will have an integer appended to it -- hardly useful, but hopefully demonstrates how a series of data can be mapped onto a function in parallel. In this case, the data is just a list of numbers.
.. doctest::
>>> from cogent.util import parallel
>>> passed_indices = []
>>> series = range(20)
>>> result = parallel.map(passed_indices.append, series)
>>> assert passed_indices == range(0,20,2) or passed_indices == range(1,20,2)
The result is either the list of even numbers up to (but not including) 20 or the list of odd numbers.
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