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So I have an algorithm I am writing, and the function multiprocess is supposed to call another function, CreateMatrixMp(), on as many processes as there are cpus, in parallel. I have never done multiprocessing before, and cannot be certain which one of the below methods is more efficient. The word "efficient" being used in the context of the function CreateMatrixMp() needing to potentially be called thousands of times.I have read all of the documentation on the python multiprocessing module, and have come to these two possibilities:

First is using the Pool class:

def MatrixHelper(self, args): return self.CreateMatrix(*args) def Multiprocess(self, sigmaI, sigmaX): cpus = mp.cpu_count() print('Number of cpu\'s to process WM: %d' % cpus) poolCount = cpus*2 args = [(sigmaI, sigmaX, i) for i in range(self.numPixels)] pool = mp.Pool(processes = poolCount, maxtasksperchild= 2) tempData = pool.map(self.MatrixHelper, args) pool.close() pool.join()

And next is using the Process class:

def Multiprocess(self, sigmaI, sigmaX): cpus = mp.cpu_count() print('Number of cpu\'s to process WM: %d' % cpus) processes = [mp.Process(target = self.CreateMatrixMp, args = (sigmaI, sigmaX, i,)) for i in range(self.numPixels)] for p in processes: p.start() for p in processes: p.join()

Pool seems to be the better choice. I have read that it causes less overhead. And Process does not consider the number of cpus on the machine. The only problem is that using Pool in this manner gives me error after error, and whenever I fix one, there is a new one underneath it. Process seems easier to implement, and for all I know it may be the better choice. What does your experience tell you?

If Pool should be used, then am I right in choosing map()? It would be preferred that order is maintained. I have tempData = pool.map(...) because the map function is supposed to return a list of the results of every process. I am not sure how Process handles its returned data.

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  • This seems similar to stackoverflow.com/questions/18176178/… Thanks Commented Jul 29, 2015 at 21:34
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    @MartinAlderete I have read that post. However, I ask some different questions here that need answering. He is using two target functions while I am using one, he passes no arguments while I pass multiple, and he does not have to be concerned with his target being an instance method, as mine is. I have done a lot of research on this so far, and both Pool and Process seem to behave differently under different contexts, and it certainly seems that one would be better in certain situations, while in others it wouldn't. I thought it appropriate to start a new question. Commented Jul 29, 2015 at 21:41

2 Answers 2

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I think the Pool class is typically more convenient, but it depends whether you want your results ordered or unordered.

Say you want to create 4 random strings (e.g,. could be a random user ID generator or so):

import multiprocessing as mp import random import string # Define an output queue output = mp.Queue() # define a example function def rand_string(length, output): """ Generates a random string of numbers, lower- and uppercase chars. """ rand_str = ''.join(random.choice( string.ascii_lowercase + string.ascii_uppercase + string.digits) for i in range(length)) output.put(rand_str) # Setup a list of processes that we want to run processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)] # Run processes for p in processes: p.start() # Exit the completed processes for p in processes: p.join() # Get process results from the output queue results = [output.get() for p in processes] print(results) # Output # ['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']

Here, the order probably doesn't matter. I am not sure if there is a better way to do it, but if I want to keep track of results in the order in which the functions are called, I typically return tuples with an ID as first item, e.g., 

# define a example function def rand_string(length, pos, output): """ Generates a random string of numbers, lower- and uppercase chars. """ rand_str = ''.join(random.choice( string.ascii_lowercase + string.ascii_uppercase + string.digits) for i in range(length)) output.put((pos, rand_str)) # Setup a list of processes that we want to run processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)] print(processes) # Output # [(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]

This let's me sort the results then:

results.sort() results = [r[1] for r in results] print(results) # Output: # ['KAQo6', '5lUya', 'nj6Q0', 'QQvLr']

The Pool class

Now to your question: How is this different from the Pool class? You'd typically prefer Pool.map to return ordered list of results without going through the hoop of creating tuples and sorting them by ID. Thus, I would say it is typically more efficient.

def cube(x): return x**3 pool = mp.Pool(processes=4) results = pool.map(cube, range(1,7)) print(results) # output: # [1, 8, 27, 64, 125, 216]

Equivalently, there is also an "apply" method:

pool = mp.Pool(processes=4) results = [pool.apply(cube, args=(x,)) for x in range(1,7)] print(results) # output: # [1, 8, 27, 64, 125, 216]

Both Pool.apply and Pool.map will lock the main program until a process has finished.

Now, you also have Pool.apply_async and Pool.map_async, which return the result as soon as the process has finished, which is essentially similar to the Process class above. The advantage may be that they provide you with the convenient apply and map functionality that you know from Python's in-built apply and map

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3 Comments

Thank you for the answer. My main concern in choosing one over the other is the consideration of the machines number of processors. In Pool, you can set it so that there are the same number of processes as cpus. But by using Process, the number of cpus comes in nowhere. Will this be handled on its own? Will it result in slightly more time to go through by using Process? I would like to use Process, and Pool is giving me a very hard time with all of its pickle errors, but Process just doesn't feel concrete enough like Pool does.
I think "Process" is more of a "bare bones" approach, as far as I know, you'd have to manage it manually. When you spawn a process but all CPUs are busy, it will be queued until a CPU becomes free again. This could potentially be a problem if you are dispatching too many processes at once which are waiting (it could easily eat up your system's available memory if the number is "relatively" large)
Yes, the memory was a problem when trying to use Process. I have gotten it to work with Pool, thank you for your advice, I'll mark this as answered.
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You can easily do this with pypeln:

import pypeln as pl stage = pl.process.map( CreateMatrixMp, range(self.numPixels), workers=poolCount, maxsize=2, ) # iterate over it in the main process for x in stage: # code # or convert it to a list data = list(stage)
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pypeln is currently unmaintained, since 2023: github.com/cgarciae/pypeln/issues/112

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