multithreading - Python Multiprocessing with a single function

Question

I have a simulation that is currently running, but the ETA is about 40 hours -- I'm trying to speed it up with multi-processing.

It essentially iterates over 3 values of one variable (L), and over 99 values of of a second variable (a). Using these values, it essentially runs a complex simulation and returns 9 different standard deviations. Thus (even though I haven't coded it that way yet) it is essentially a function that takes two values as inputs (L,a) and returns 9 values.

Here is the essence of the code I have:

STD_1 = []
STD_2 = []
# etc.

for L in range(0,6,2):
    for a in range(1,100):
        ### simulation code ###
        STD_1.append(value_1)
        STD_2.append(value_2)
        # etc.

Here is what I can modify it to:

master_list = []

def simulate(a,L):
    ### simulation code ###
    return (a,L,STD_1, STD_2 etc.)

for L in range(0,6,2):
    for a in range(1,100): 
        master_list.append(simulate(a,L))

Since each of the simulations are independent, it seems like an ideal place to implement some sort of multi-threading/processing.

How exactly would I go about coding this?

EDIT: Also, will everything be returned to the master list in order, or could it possibly be out of order if multiple processes are working?

EDIT 2: This is my code -- but it doesn't run correctly. It asks if I want to kill the program right after I run it.

import multiprocessing

data = []

for L in range(0,6,2):
    for a in range(1,100):
        data.append((L,a))

print (data)

def simulation(arg):
    # unpack the tuple
    a = arg[1]
    L = arg[0]
    STD_1 = a**2
    STD_2 = a**3
    STD_3 = a**4
    # simulation code #
    return((STD_1,STD_2,STD_3))

print("1")

p = multiprocessing.Pool()

print ("2")

results = p.map(simulation, data)

EDIT 3: Also what are the limitations of multiprocessing. I've heard that it doesn't work on OS X. Is this correct?

Answer 1

• Wrap the data for each iteration up into a tuple.
• Make a list data of those tuples
• Write a function f to process one tuple and return one result
• Create p = multiprocessing.Pool() object.
• Call results = p.map(f, data)

This will run as many instances of f as your machine has cores in separate processes.

Edit1: Example:

from multiprocessing import Pool

data = [('bla', 1, 3, 7), ('spam', 12, 4, 8), ('eggs', 17, 1, 3)]

def f(t):
    name, a, b, c = t
    return (name, a + b + c)

p = Pool()
results = p.map(f, data)
print results

Edit2:

Multiprocessing should work fine on UNIX-like platforms such as OSX. Only platforms that lack os.fork (mainly MS Windows) need special attention. But even there it still works. See the multiprocessing documentation.