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Easy routines for coding on sharedmem machines

Project description

Dispatch your trivially parallizable jobs with sharedmem.

Now also supports Python 3.

  • sharedmem.empty creates numpy arrays to child processes.

  • sharedmem.MapReduce dispatches work to child processes.

  • sharedmem.MapReduce.ordered and sharedmem.MapReduce.critical provides the equivlant concept of OpenMP ordered and OpenMP critical sections.

Functions and variables are inherited from a fork and copy-on-write. Pickability is not a concern.

Easier to use than multiprocessing.Pool, at the cost of not supporting Windows.

For documentation, please refer to .

>>> input = numpy.arange(1024 * 1024 * 128, dtype='f8')
>>> output = sharedmem.empty(1024 * 1024 * 128, dtype='f8')
>>> with MapReduce() as pool:
>>>    chunksize = 1024 * 1024
>>>    def work(i):
>>>        s = slice (i, i + chunksize)
>>>        output[s] = input[s]
>>>        return i, sum(input[s])
>>>    def reduce(i, r):
>>>        print('chunk', i, 'done')
>>>        return r
>>>    r =, range(0, len(input), chunksize), reduce=reduce)
>>> print numpy.sum(r)

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