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Editorial Team
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Asked: 2026-06-04T15:17:48+00:00

I’m trying to figure out multi-threading programming in python. Here’s the simple task with

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I’m trying to figure out multi-threading programming in python. Here’s the simple task with which I want to compare serial and parallel speeds. 

import threading
import Queue
import time
import math

def sinFunc(offset, n):
  result = []
  for i in range(n):
    result.append(math.sin(offset + i * i))
  return result

def timeSerial(k, n):
  t1 = time.time()    
  answers = []
  for i in range(k):
    answers.append(sinFunc(i, n))
  t2 = time.time()
  print "Serial time elapsed: %f" % (t2-t1)

class Worker(threading.Thread):

  def __init__(self, queue, name):
    self.__queue = queue
    threading.Thread.__init__(self)
    self.name = name

  def process(self, item):
    offset, n = item
    self.__queue.put(sinFunc(offset, n))
    self.__queue.task_done()
    self.__queue.task_done()

  def run(self):
    while 1:
        item = self.__queue.get()
        if item is None:
            self.__queue.task_done()
            break
        self.process(item)

def timeParallel(k, n, numThreads):
  t1 = time.time()    
  queue = Queue.Queue(0)
  for i in range(k):
    queue.put((i, n))
  for i in range(numThreads):
    queue.put(None)    
  for i in range(numThreads):
    Worker(queue, i).start()
  queue.join()
  t2 = time.time()
  print "Serial time elapsed: %f" % (t2-t1)

if __name__ == '__main__':

  n = 100000
  k = 100
  numThreads = 10

  timeSerial(k, n)
  timeParallel(k, n, numThreads)

#Serial time elapsed: 2.350883
#Serial time elapsed: 2.843030

Can someone explain to me what’s going on? I’m used to C++, and a similar version of this using the module sees the speed-up we would expect.

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1 Answer

  1. Editorial Team

    Other answers have referred to the issue of the GIL being the problem in cpython. But I felt there was a bit of missing information. This will cause you performance issues in situations where the code you are running in threads is CPU bound. In your case here, yes doing many calculations in threads is going to most likely result in dramatically degraded performance.

    But, if you were doing something that was more IO bound, such as reading from many sockets in a network application, or calling out to subprocess, you can get performance increases from threads. A simple example for your code above would be to add a stupidly simple call out to the shell:

    import os

def sinFunc(offset, n):
  result = []
  for i in xrange(n):
    result.append(math.sin(offset + i * i))
  os.system("echo 'could be a database query' >> /dev/null; sleep .1")
  return result

    That call might have been something real like waiting on the filesystem. But you can see that in this example, threading will start to prove beneficial, as the GIL can be released when the thread is waiting on IO and other threads will continue to process. Even so, there is still a sweet spot for when more threads start to become negated by the overhead of creating them and synchronizing them.

    For CPU bound code, you would make use of multiprocessing

    From article: http://www.informit.com/articles/article.aspx?p=1850445&seqNum=9

    …threading is more appropriate for I/O-bound applications (I/O releases
    the GIL, allowing for more concurrency)…

    Similar question references about threads vs processes:
    https://stackoverflow.com/a/1227204/496445
    https://stackoverflow.com/a/990436/496445

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