[Chicago] threading is slow

[Oren Livne]

Thanks so much for all your answers!

I have a text file with a million int pairs, each of which can be 
processed immediately. I would like to set up a queue to read lines from 
the file and feed a thread pool that will process it in parallel and 
output into (say) another queue, to be processed by another thread that 
prints the results.

On 3/6/2013 5:19 PM, Brantley Harris wrote:
> Whoa, back up.  What are you trying to do with threads?
>
>
> On Wed, Mar 6, 2013 at 5:05 PM, Daniel Griffin <dgriff1 at gmail.com 
> <mailto:dgriff1 at gmail.com>> wrote:
>
>     Python has a GIL so threads mostly sort of suck. Use
>     multiprocessing, twisted or celery.
>
>
>     On Wed, Mar 6, 2013 at 3:29 PM, Oren Livne <livne at uchicago.edu
>     <mailto:livne at uchicago.edu>> wrote:
>
>         Dear All,
>
>         I am new to python multithreading. It seems that using
>         threading causes a slow down with more threads rather than a
>         speedup. should I be using the multiprocessing module instead?
>         Any good examples for threads reading from a queue with
>         multiprocessing?
>
>         Thanks so much,
>         Oren
>
>         #!/usr/bin/env python
>         '''Sum up the first 100000000 numbers. Time the speed-up of
>         using multithreading.'''
>         import threading, time, numpy as np
>
>         class SumThread(threading.Thread):
>             def __init__(self, a, b):
>                 threading.Thread.__init__(self)
>                 self.a = a
>                 self.b = b
>                 self.s = 0
>
>             def run(self):
>                 self.s = sum(i for i in xrange(self.a, self.b))
>
>         def main(num_threads):
>             start = time.time()
>             a = map(int, np.core.function_base.linspace(0, 100000000,
>         num_threads + 1, True))
>             # spawn a pool of threads, and pass them queue instance
>             threads = []
>             for i in xrange(num_threads):
>                 t = SumThread(a[i], a[i + 1])
>                 t.setDaemon(True)
>                 t.start()
>                 threads.append(t)
>
>             # Wait for all threads to complete
>             for t in threads:
>                 t.join()
>
>             # Fetch results
>             s = sum(t.s for t in threads)
>             print '#threads = %d, result = %10d, elapsed Time: %s' %
>         (num_threads, s, time.time() - start)
>
>         for n in 2 ** np.arange(4):
>             main(n)
>
>         Output:
>         #threads = 1, result = 4999999950000000, elapsed Time:
>         12.3320000172
>         #threads = 2, result = 4999999950000000, elapsed Time:
>         16.5600001812  ???
>         #threads = 4, result = 4999999950000000, elapsed Time:
>         16.7489998341  ???
>         #threads = 8, result = 4999999950000000, elapsed Time:
>         16.6720001698  ???
>
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