Skip to main content

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)

Project details

Release history Release notifications

History Node


History Node


History Node


History Node


History Node


History Node


This version
History Node


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
sharedmem-0.3.tar.gz (13.2 kB) Copy SHA256 hash SHA256 Source None Jul 31, 2015

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page