On 03/11/2013 10:10, capple...@gmail.com wrote:
On Friday, November 1, 2013 10:35:47 PM UTC-4, smhall05 wrote:
I am using a basic multiprocessing snippet I found:
#-
from multiprocessing import Pool
def f(x):
return x*x
if __na
On Friday, November 1, 2013 10:35:47 PM UTC-4, smhall05 wrote:
> I am using a basic multiprocessing snippet I found:
>
>
>
> #-
>
> from multiprocessing import Pool
>
>
>
> def f(x):
>
> return x*x
>
>
>
> if __name__ == '__main__'
On Nov 2, 2013, at 11:44 AM, Sherard Hall wrote:
> Thank you for the response. Processing time is very important so I suspect
> having to write to disk will take more time than letting the other processes
> complete without finding the answer. So I did some profiling one process
> finds the an
Thank you for the response. Processing time is very important so I suspect
having to write to disk will take more time than letting the other
processes complete without finding the answer. So I did some profiling one
process finds the answer in about 250ms, but since I can't stop the other
processe
On Nov 2, 2013, at 1:03 AM, smhall05 wrote:
> On Friday, November 1, 2013 10:52:40 PM UTC-4, MRAB wrote:
>> On 02/11/2013 02:35, smhall05 wrote:
>>
>>> I am using a basic multiprocessing snippet I found:
>>>
>>> #-
>>> from multiprocessing imp
On Friday, November 1, 2013 10:52:40 PM UTC-4, MRAB wrote:
> On 02/11/2013 02:35, smhall05 wrote:
>
> > I am using a basic multiprocessing snippet I found:
>
> >
>
> > #-
>
> > from multiprocessing import Pool
>
> >
>
> > def f(x):
>
> >
On 02/11/2013 02:35, smhall05 wrote:
I am using a basic multiprocessing snippet I found:
#-
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker pro
I am using a basic multiprocessing snippet I found:
#-
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
pool = Pool(processes=4) # start 4 worker processes
result = pool.apply_async(f, [
