Parallel computation in Python

Parallel computation in Python is an important aspect to learn for new and pro programmers. Learn more about this function in this guide below.

Parallel computation in Python: Using the multiprocessing module to parallelise tasks

import multiprocessing
def fib(n):
"""computing the Fibonacci in an inefficient way
was chosen to slow down the CPU."""
if n <= 2:
return 1
else:
return fib(n-1)+fib(n-2)
p = multiprocessing.Pool()
print(p.map(fib,[38,37,36,35,34,33]))

Out: [39088169, 24157817, 14930352, 9227465, 5702887, 3524578]

As the execution of each call to fib happens in parallel, the time of execution of the full example is 1.8× faster than if done in a sequential way on a dual processor.

Python 2.2+

Parallel computation in Python: Using a C-extension to parallelize tasks

The idea here is to move the computationally intensive jobs to C (using special macros), independent of Python, and have the C code release the GIL while it’s working.

include “Python.h”


PyObject *pyfunc(PyObject *self, PyObject *args) {

Py_BEGIN_ALLOW_THREADS
Threaded C code

Py_END_ALLOW_THREADS

}

Using Parent and Children scripts to execute code in parallel

child.py
import time
def main():
print "starting work"
time.sleep(1)
print "work work work work work"
time.sleep(1)
print "done working"
if name == 'main':
main()
parent.py
import os
def main():
for i in range(5):
os.system("python child.py &")
if name == 'main':
main()

This is useful for parallel, independent HTTP request/response tasks or Database select/inserts. Command line arguments can be given to the child.py script as well. Synchronization between scripts can be achieved by all scripts regularly checking a separate server (like a Redis instance).

Using PyPar module to parallelize

PyPar is a library that uses the message passing interface (MPI) to provide parallelism in Python. A simple example in PyPar (as seen at https://github.com/daleroberts/pypar) looks like this:

import pypar as pp
ncpus = pp.size()
rank = pp.rank()
node = pp.get_processor_name()
print 'I am rank %d of %d on node %s' % (rank, ncpus, node)
if rank == 0:
msh = 'P0'
pp.send(msg, destination=1)
msg = pp.receive(source=rank-1)
print 'Processor 0 received message "%s" from rank %d' % (msg, rank-1)
else:
source = rank-1
destination = (rank+1) % ncpus
msg = pp.receive(source)
msg = msg + 'P' + str(rank)
pypar.send(msg, destination)
pp.finalize()

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