Concurrent prime factorization

So now let us have a look at how we can improve the performance of this program by utilizing multiple processes.

In order for us to split this workload up, we'll define an executeProc function, which, instead of generating 10,000 random numbers to be factorized, will generate 1,000 random numbers. We'll create 10 processes, and execute the function 10 times, though, so the total number of calculations should be the exact same as when we performed the sequential test:

import time
import random
from multiprocessing import Process
# This does all of our prime factorization on a given number 'n'
def calculatePrimeFactors(n):
  primfac = []
  d = 2
  while d*d <= n:
    while (n % d) == 0:
      primfac.append(d)  # supposing you want multiple factors repeated
      n //= d
    d += 1
  if n > 1:
    primfac.append(n)
  return primfac
# We split our workload from one batch of 10,000 calculations
# into 10 batches of 1,000 calculations
def executeProc():
  for i in range(1000):
    rand = random.randint(20000, 100000000)
    print(calculatePrimeFactors(rand))
def main():
  print("Starting number crunching")
  t0 = time.time() 
  procs = []
  # Here we create our processes and kick them off
  for i in range(10):
    proc = Process(target=executeProc, args=())
    procs.append(proc)
    proc.start()
  # Again we use the .join() method in order to wait for 
  # execution to finish for all of our processes
  for proc in procs:
    proc.join()
  t1 = time.time()
  totalTime = t1 - t0
  # we print out the total execution time for our 10
  # procs.
  print("Execution Time: {}".format(totalTime))
if __name__ == '__main__':
  main()