[Numpy-discussion] The mu.py script will keep running and never end.

[Hongyi Zhao]

On Sun, Oct 11, 2020 at 2:02 PM Andrea Gavana <andrea.gavana at gmail.com> wrote:
>
>
>
> On Sun, 11 Oct 2020 at 07.52, Hongyi Zhao <hongyi.zhao at gmail.com> wrote:
>>
>> On Sun, Oct 11, 2020 at 1:33 PM Andrea Gavana <andrea.gavana at gmail.com> wrote:
>> >
>> >
>> >
>> > On Sun, 11 Oct 2020 at 07.14, Andrea Gavana <andrea.gavana at gmail.com> wrote:
>> >>
>> >> Hi,
>> >>
>> >> On Sun, 11 Oct 2020 at 00.27, Hongyi Zhao <hongyi.zhao at gmail.com> wrote:
>> >>>
>> >>> On Sun, Oct 11, 2020 at 1:48 AM Robert Kern <robert.kern at gmail.com> wrote:
>> >>> >
>> >>> > You don't need to use vectorize() on fermi(). fermi() will work just fine on arrays and should be much faster.
>> >>>
>> >>> Yes, it really does the trick. See the following for the benchmark
>> >>> based on your suggestion:
>> >>>
>> >>> $ time python mu.py
>> >>> [-10.999 -10.999 -10.999 ...  20.     20.     20.   ] [4.973e-84
>> >>> 4.973e-84 4.973e-84 ... 4.973e-84 4.973e-84 4.973e-84]
>> >>>
>> >>> real    0m41.056s
>> >>> user    0m43.970s
>> >>> sys    0m3.813s
>> >>>
>> >>>
>> >>> But are there any ways to further improve/increase efficiency?
>> >>
>> >>
>> >>
>> >> I believe it will get a bit better if you don’t column_stack an array 6000 times - maybe pre-allocate your output first?
>> >>
>> >> Andrea.
>> >
>> >
>> >
>> > I’m sorry, scratch that: I’ve seen a ghost white space in front of your column_stack call and made me think you were stacking your results very many times, which is not the case.
>>
>> Still not so clear on your solutions for this problem. Could you
>> please post here the corresponding snippet of your enhancement?
>
>
> I have no solution, I originally thought you were calling “column_stack” 6000 times in the loop, but that is not the case, I was mistaken. My apologies for that.
>
> The timings of your approach is highly dependent on the size of your “energy” and “DOS” array -

The size of the “energy” and “DOS” array is Problem-related and
shouldn't be reduced arbitrarily.

> not to mention calling trapz 6000 times in a loop.

I'm currently thinking on parallelization the execution of the for
loop, say, with joblib <https://github.com/joblib/joblib>, but I still
haven't figured out the corresponding codes. If you have some
experience on this type of solution, could you please give me some
more hints?

>  Maybe there’s a better way to do it with another approach, but at the moment I can’t think of one...
>
>>
>>
>> Regards,
>> HY
>> >
>> >>
>> >>
>> >>>
>> >>>
>> >>> Regards,
>> >>> HY
>> >>>
>> >>> >
>> >>> > On Sat, Oct 10, 2020, 8:23 AM Hongyi Zhao <hongyi.zhao at gmail.com> wrote:
>> >>> >>
>> >>> >> Hi,
>> >>> >>
>> >>> >> My environment is Ubuntu 20.04 and python 3.8.3 managed by pyenv. I
>> >>> >> try to run the script
>> >>> >> <https://notebook.rcc.uchicago.edu/files/acs.chemmater.9b05047/Data/bulk/dft/mu.py>,
>> >>> >> but it will keep running and never end. When I use 'Ctrl + c' to
>> >>> >> terminate it, it will give the following output:
>> >>> >>
>> >>> >> $ python mu.py
>> >>> >> [-10.999 -10.999 -10.999 ...  20.     20.     20.   ] [4.973e-84
>> >>> >> 4.973e-84 4.973e-84 ... 4.973e-84 4.973e-84 4.973e-84]
>> >>> >>
>> >>> >> I have to terminate it and obtained the following information:
>> >>> >>
>> >>> >> ^CTraceback (most recent call last):
>> >>> >>   File "mu.py", line 38, in <module>
>> >>> >>     integrand=DOS*fermi_array(energy,mu,kT)
>> >>> >>   File "/home/werner/.pyenv/versions/datasci/lib/python3.8/site-packages/numpy/lib/function_base.py",
>> >>> >> line 2108, in __call__
>> >>> >>     return self._vectorize_call(func=func, args=vargs)
>> >>> >>   File "/home/werner/.pyenv/versions/datasci/lib/python3.8/site-packages/numpy/lib/function_base.py",
>> >>> >> line 2192, in _vectorize_call
>> >>> >>     outputs = ufunc(*inputs)
>> >>> >>   File "mu.py", line 8, in fermi
>> >>> >>     return 1./(exp((E-mu)/kT)+1)
>> >>> >> KeyboardInterrupt
>> >>> >>
>> >>> >>
>> >>> >> Any helps and hints for this problem will be highly appreciated?
>> >>> >>
>> >>> >> Regards,
>> >>> >> --
>> >>> >> Hongyi Zhao <hongyi.zhao at gmail.com>
>> >>> >> _______________________________________________
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>> >>> >
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>> >>>
>> >>>
>> >>> --
>> >>> Hongyi Zhao <hongyi.zhao at gmail.com>
>> >>> _______________________________________________
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>> >
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>>
>>
>> --
>> Hongyi Zhao <hongyi.zhao at gmail.com>
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-- 
Hongyi Zhao <hongyi.zhao at gmail.com>