#Suppose you have a collection of tasks, which in this example I'll assume is just running a function f.
#If these tasks are completely separate and independent the most then you can parallelize them easily.
#In this gist I'll show the simplest possible way to do this using mpi4py.
#There are better ways to do this, in particular if the tasks vary significantly in time taken to run.

import mpi4py.MPI

def f(i):
  "A fake task - in this case let just open a file and write a number to it"
  #open file with name based on task number
  f=open("%d.txt"%i,"w")
  #write some info to it
  f.write("%d * 10 = %d\n"%(i, 10*i))
  #close the file
  f.close()

#A list of all the tasks to do.  In your case you will probably build this task list in a more complex way.
#You don't even need to build it in advance for this approach to work
task_list = range(100)

#main program loop.  This is the unparallelized verion, for comparison
for task in task_list:
  f(task)
  
#And now moving on the parallel version
  
#mpi4py has the notion of a "communicator" - a collection of processors
#all operating together, usually on the same program.  Each processor 
#in the communicator is identified by a number, its rank,  We'll use that
#number to split the tasks

#find out which number processor this particular instance is,
#and how many there are in total
rank = mpi4py.MPI.COMM_WORLD.Get_rank()
size = mpi4py.MPI.COMM_WORLD.Get_size()

#parallelized version
#the enumerate function gives us a number i in addition
#to the task.  (In this specific case i is the same as task!  But that's
#not true usually)
for i,task in enumerate(task_list):
  #This is how we split up the jobs.
  #The % sign is a modulus, and the "continue" means
  #"skip the rest of this bit and go to the next time
  #through the loop"
  # If we had e.g. 4 processors, this would mean
  # that proc zero did tasks 0, 4, 8, 12, 16, ...
  # and proc one did tasks 1, 5, 9, 13, 17, ...
  # and do on.
  if i%size!=rank: continue
  print "Task number %d (%d) being done by processor %d of %d" % (i, task, rank, size)
  f(task)