[Chicago] threading is slow

[Brantley Harris]

I agree with Mr. Griffin here.


On Wed, Mar 6, 2013 at 5:35 PM, Daniel Griffin <dgriff1 at gmail.com> wrote:

> What sort of speed are you looking for here? Does the ordering matter? If
> not then you can just do a multiprocessing Pool and call map with a chunk
> of the million int pairs.
>
>
> On Wed, Mar 6, 2013 at 4:31 PM, Oren Livne <livne at uchicago.edu> wrote:
>
>>  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> 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> 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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>>>
>>>
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>>
>>
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>>
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>>
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