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I'm running the following program, in order to compare different times between multiprocessing and single core processing.
Here is the script :
from multiprocessing import Pool, cpu_count from time import * #Amount to calculate N=5000 #Fonction that works alone def two_loops(x): t=0 for i in range(1,x+1): for j in range(i): t+=1 return t #Function that need to be called in a loop def single_loop(x): tt=0 for j in range(x): tt+=1 return tt print 'Starting loop function' starttime=time() tot=0 for i in range(1,N+1): tot+=single_loop(i) print 'Single loop function. Result ',tot,' in ', time()-starttime,' seconds' print 'Starting multiprocessing function' if __name__=='__main__': starttime=time() pool = Pool(cpu_count()) res= pool.map(single_loop,range(1,N+1)) pool.close() print 'MP function. Result ',res,' in ', time()-starttime,' seconds' print 'Starting two loops function' starttime=time() print 'Two loops onction. Result ',two_loops(N),' in ', time()-starttime,' seconds'
So basically the functions gives me the sum of all integers between 1 and N (so N(N+1)/2). The two_loops function is the basic one, using two for loops. The single_loop is just created to simulate one loop (the j loop).
When I'm running this script, this works well but I don't get the right result. I get :
Starting loop function Single loop function. Result 12502500 in 0.380275964737 seconds
Starting multiprocessing function MP function. Result [1, 2, 3,... a lot of values here ...,4999, 5000] in 0.683819055557 seconds
Starting two loops function Two loops onction. Result 12502500 in 0.4114818573 seconds
It looks like the script runs, but I can't manage to get the good result. I saw on a the web that the close() function was supposed to do that, but apparently not.
Do you know how I can do ?
Thanks a lot !
I don't understand your question but here's how it can be done:
from concurrent.futures.process import ProcessPoolExecutor from timeit import Timer def two_loops_multiprocessing(): with ProcessPoolExecutor() as executor: executor.map(single_loop, range(N)) if __name__ == "__main__": iterations, elapsed_time = Timer("two_loops(N)", globals=globals()).autorange() print(elapsed_time / iterations) iterations, elapsed_time = Timer("two_loops_multiprocessing()", globals=globals()).autorange() print(elapsed_time / iterations)
multiprocessing — Process-based parallelism — Python 3.8.5 , from multiprocessing import Process, Queue def f(q): q.put([42, None, 'hello']) if Without using the lock output from the different processes is liable to get all� 1. Python Multiprocessing – Objective. Today, in this Python tutorial, we will see Python Multiprocessing.Moreover, we will look at the package and structure of Multiprocessing in Python.
What's happening is that your map function is chopping up your range you provide it and runs the single loop function for all these separate numbers. Look here to see what it does: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.pool.Pool.map And since your single loop just adds 1 to tt for a range up to the value you get the value back. This effectively means you get your range() object back which is the answer you get.
In your other "single loop" you later add all the values you get together to get to a single value here:
for i in range(1,N+1): tot+=single_loop(i)
But you forget to do this with you multiprocessing. What you should do is add a loop after you have called your map function to add them all together and you will get your expected answer.
Besides this your single loop function is basically a two loop function where you moved one loop to a function call. I'm not sure what you are trying to accomplish but there is not a big difference between the two.
Getting Values from Multiprocessing Processes, Getting Values from Multiprocessing Processes from multiprocessing import Pool When done, we close the pool, and then we're printing the result. My main question is if this is the correct, and I should be including the multiprocessing.Pool() inside the loop, or if instead I should create the pool outside loop, i.e.: pool=multiprocessing.Pool() for i in outer_array: partial_func=partial(full_func,arg=i) output[:][i]=pool.map(partial_func,inner_array) Also, I am not sure if I should
Just sum the result list:
res = sum(pool.map(single_loop,range(1,N+1)))
You could avoid calculating the sum in the main thread by using some shared memory, but keep in mind that you will lose more time on synchronization. And again, there's no gain from multiprocessing in this case. It all depends on the specific case. If you needed to call single_loop fewer times and each call would take more time, then multiprocessing would speed up your code.
Quick Tutorial: Python Multiprocessing, The general jist is that multiprocessing allows you to run several functions at the same time. For this tutorial, we are going to use it to make a loop faster by splitting a loop into a number of result = pool. map (f, chunks). multiprocessing.cpu_count() returns the total available processes for your machine. Then loop through each row of params and use multiprocessing.Pool.apply_async to call my_function and save the result. Parameters to my_function are passed using the args argument of apply_async and the callback function is where the result of my_function is
multiprocessing Basics, This example passes each worker a number so the output is a little more interesting. import multiprocessing def worker(num): """thread worker function""" print� As a result, multiprocessed code usually executes in a different order each time it is run, even if each result is always the same. With multiprocessing, using higher-level programming languages
Parallel programming in Python: multiprocessing (part 2) – PDC Blog, In this example, a Queue object named qout is used to save the result. import multiprocessing as mp def square(x, q): q.put(x * x) qout = mp. Inside the loop body on line 3, n is decremented by 1 to 4, and then printed. When the body of the loop has finished, program execution returns to the top of the loop at line 2, and the expression is evaluated again. It is still true, so the body executes again, and 3 is printed. This continues until n becomes 0. At that point, when the
How to do Multiprocessing in Python, Brandon Rohrer:How to do Multiprocessing in Python. on the result placeholder from apply_async() , it would hold up the for loop while it waited for the result. You had to import multiprocessing and then just change from looping through the numbers to creating a multiprocessing.Pool object and using its .map() method to send individual numbers to worker-processes as they become free. This was just what you did for the I/O-bound multiprocessing code, but here you don’t need to worry about the Session
- Where is boucle defined?
- Please don't use python 2: pythonclock.org
- You should put all your mainline (first column) code so it is protected by
- @Marc Sorry I fixed it
- Can explain your code ? I’m confused with the « iterations, elapsed_time » variables you made. My question was simply to make my multiprocessing method to give me the good result, which it doesn’t by giving me a list of numbers
- What is the expected result?
- Well, as I said it's supposed to be 12502500 in this precise case (N=5000). As you can see, it works for the two basics functions, but the multiprocessing only gives me a list, which is absolutely not what I'm wanting
- My goal is to try to make the program run faster by using all available cores. Sure the single_loop and two_loops functions are almost the same, but I'm separating the two loops in order to make one run in multiprocessing. I thought that you can only replace one loop by the multiprocessing command, since the inner loop (the one inside the single_loop function) depends of the first one. So, basically, when you say I have to add a loop to sum all the map() results, it adds processing time so I don't see the point of using multiprocessing then.
- If this is all you are trying to accomplish then no, multiprocessing is not something you want to be doing, if you have more calculation intensive things you need to do then it can be very helpful to use multiprocessing. If what you do in the single loop for example would take several seconds per call then it would possibly speed up your function considerably.
- Of course, this is just a test to try multiprocessing. I plan to use it for way more complex scripts
- But you know what to do now right? Your program was working except there was one thing that you needed to add and then it works as you planned.
- Well sort of. I don't know if using the sum() function (or a for loop) is just going to add much calculus time, isn't it ? I mean, if my goal is to reduce the process time by making the loops run in parallel, then adding a function at the end might waste the effort right ?