[Chicago] threading is slow

[Philip Doctor]

I'll let Weezer know.


On Thu, Mar 7, 2013 at 2:39 PM, JS Irick <hundredpercentjuice at gmail.com>wrote:

> Sweaters are made of yarn, not thread.
>
>
> On Thu, Mar 7, 2013 at 10:03 AM, Alex MacKay <chicagomackay at gmail.com>wrote:
>
>> Has anyone used Python threads to make a sweater?
>>
>>  On Mar 6, 2013, at 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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>
>
> --
> ====
> JS Irick
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