How to Run Functions in Parallel

How to run functions in parallel?

You could use threading or multiprocessing.

Due to peculiarities of CPython, threading is unlikely to achieve true parallelism. For this reason, multiprocessing is generally a better bet.

Here is a complete example:

from multiprocessing import Process

def func1():
  print 'func1: starting'
  for i in xrange(10000000): pass
  print 'func1: finishing'

def func2():
  print 'func2: starting'
  for i in xrange(10000000): pass
  print 'func2: finishing'

if __name__ == '__main__':
  p1 = Process(target=func1)
  p1.start()
  p2 = Process(target=func2)
  p2.start()
  p1.join()
  p2.join()

The mechanics of starting/joining child processes can easily be encapsulated into a function along the lines of your runBothFunc:

def runInParallel(*fns):
  proc = []
  for fn in fns:
    p = Process(target=fn)
    p.start()
    proc.append(p)
  for p in proc:
    p.join()

runInParallel(func1, func2)

Python execute a function in parallel in loop

The problem could be that the parallel processes is not set up to handle iterables correctly. Instead of subprocess.run, I would recommend using
multiprocessing.pool or multiprocessing.starmap as specified in these docs.

This could look something like this:

    import multiprocessing as mp

    # Step 1: Use multiprocessing.Pool() and specify number of cores to use (here I use 4).
    pool = mp.Pool(4)

    # Step 2: Use pool.starmap which takes a multiple iterable arguments
    results = pool.starmap(My_Function, [(variable1,variable2,variable3) for i in data])
    
    # Step 3: Don't forget to close
    pool.close()

How to run functions with arguments in parallel?

You should modify runInParallel to do iterable unpacking.

import time
from multiprocessing import Process

def worker(ii):
    time.sleep(ii)
    print("Working")

def runInParallel(*fns):
    proc = []
    for fn in fns:
        func, *args = fn
        p = Process(target=func, args=args)
        p.start()
        proc.append(p)
    for p in proc:
        p.join()

if __name__ == '__main__':
    start = time.time()
    runInParallel((worker, 2), (worker, 3), (worker, 5), (worker, 2))
    print("Total time taken: ", time.time()-start)

Python run function parallel

The reason for this issue is because, the list mega_summe_list is not shared between the processes.

When you invoke parallel processing in python all the functions and variables are imported and run independently in different processes.

So, for instance when you start 5 processes, 5 different copies of these variables are imported and run independently. So, when you access mega_summe_list in main it is still empty, because it is empty in this process.

To enable synchronization between processes, you can use a list proxy from the multiprocessing package.
A Multiprocessing manager maintains an independent server process where in these python objects are held.

Below is the code used to create a multiprocessing Manager List,

from multiprocessing import Manager
mega_summe_list = Manager().List()

Above code can be used instead of mega_summe_list = [] while using multiprocessing.

Below is an example,

from multiprocessing.pool import Pool
from multiprocessing import Manager

def repeat_test(_):
    global b, mp_list
    a = [1,2,3]
    b += a
    mp_list += a # Multiprocessing Manager List
    a = []

if __name__ == "__main__":
    b = []
    mp_list = Manager().list()

    p = Pool(5)
    p.map(repeat_test, range(5))
    print("a: {0}, \n mp_list: {1}".format(b, mp_list))

Output:

b: [],
 mp_list: [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]

Hope this solves your problem.

Python: running n number of functions in parallel

You can pass arguments to the Process class (docs) for example:

p = Process(target=func, args=('func1', ))

and func looks like:

def func(name):
    print '%s: starting' % name
    for i in xrange(10000000): pass
    print '%s: finishing' % name

As a side note you might want to be using Thread instead of Process - see here for a comparison: https://stackoverflow.com/a/3046201/419115

Most optimal way to execute timed functions in parallel using node?

Nodejs runs your Javascript in a single thread unless you explicitly create a WorkerThread and run some code in that. True parallel execution where both jobs are running code that uses the CPU will only be accomplished if you either run each task in a WorkerThread or child process to get it out of the main thread.

Let me repeat, true parallel execution requires more than one thread or process in nodejs and nodejs does not do that by default so you will have to create a WorkerThread or child_process.

So, if you have code that takes more than a few ms to do its work and you want it to run at a fairly precise time, then you can't count on the main Javascript thread to do that because it might be busy at that precise time. Timers in Javascript will run your code no earlier than the scheduled time, and when that scheduled time comes around, the event loop is ready to run them, but they won't actually run until whatever was running before finishes and returns control back to the event loop so the event loop can run the code attached to your timer.

So, if all you're mostly doing is I/O kind of work (reading/writing files or network), then your actual Javascript execution time is probably only milliseconds and nodejs can be very, very responsive to run your timers pretty close to "on time". But, if you have computationally expensive things that keep the CPU busy for much longer, then you can't count on your timers to run "on time" if you run that CPU-heavy stuff in the main thread.

What you can do, is start up a WorkerThread, set the timer in the WorkerThread and run your code in the worker thread. As long as you don't ask that WorkerThread to run anything else, it should be ready to run that timer pretty much "on time".

Now WorkerThreads do share some resources with the main thread so they aren't 100% independent (though they are close to independent). If you want 100% independence, then you can start a nodejs child process that runs a node script, sets its own timers and runs its own work in that other process.

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All that said, the single threaded model works very, very well at reasonably high scale for code that is predominantly I/O code because nodejs uses non-blocking I/O so while it's waiting to read or write from file or network, the main thread is free and available to run other things. So, it will often give the appearance of running things in parallel because progress is being made on multiple fronts. The I/O itself inside the nodejs library is either natively non-blocking (network I/O) or is happening in an OS-native thread (file I/O) and the programming interface to Javascript is callback or promise based so it is also non-blocking.

I mention all this because you don't say what your two operations that you want to run in parallel are (including your actual code allows us to write more complete answers). If they are I/O or even some crypto, then they may already be non-blocking and you may achieve desired parallelism without having to use additional threads or processes.

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