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

Hongyi Zhao hongyi.zhao at gmail.com
Mon Oct 12 10:20:49 EDT 2020 

On Mon, Oct 12, 2020 at 9:33 PM Andrea Gavana <andrea.gavana at gmail.com> wrote:
>
> Hi,
>
> On Mon, 12 Oct 2020 at 14:38, Hongyi Zhao <hongyi.zhao at gmail.com> wrote:
>>
>> On Sun, Oct 11, 2020 at 3:42 PM Evgeni Burovski
>> <evgeny.burovskiy at gmail.com> wrote:
>> >
>> > On Sun, Oct 11, 2020 at 9:55 AM Evgeni Burovski
>> > <evgeny.burovskiy at gmail.com> wrote:
>> > >
>> > > The script seems to be computing the particle numbers for an array of chemical potentials.
>> > >
>> > > Two ways of speeding it up, both are likely simpler then using dask:
>> > >
>> > > First: use numpy
>> > >
>> > > 1. Move constructing mu_all out of the loop (np.linspace)
>> > > 2. Arrange the integrands into a 2d array
>> > > 3. np.trapz along an axis which corresponds to a single integrand array
>> > > (Or avoid the overhead of trapz by just implementing the trapezoid formula manually)
>> >
>> >
>> > Roughly like this:
>> > https://gist.github.com/ev-br/0250e4eee461670cf489515ee427eb99
>>
>> I've done the comparison of the real execution time for your version
>> I've compared the execution efficiency of your above method and the
>> original method of the python script by directly using fermi() without
>> executing vectorize() on it. Very surprisingly, the latter is more
>> efficient than the former, see following for more info:
>>
>> $ time python fermi_integrate_np.py
>> [[1.03000000e+01 4.55561775e+17]
>>  [1.03001000e+01 4.55561780e+17]
>>  [1.03002000e+01 4.55561786e+17]
>>  ...
>>  [1.08997000e+01 1.33654085e+21]
>>  [1.08998000e+01 1.33818034e+21]
>>  [1.08999000e+01 1.33982054e+21]]
>>
>> real    1m8.797s
>> user    0m47.204s
>> sys    0m27.105s
>> $ time python mu.py
>> [[1.03000000e+01 4.55561775e+17]
>>  [1.03001000e+01 4.55561780e+17]
>>  [1.03002000e+01 4.55561786e+17]
>>  ...
>>  [1.08997000e+01 1.33654085e+21]
>>  [1.08998000e+01 1.33818034e+21]
>>  [1.08999000e+01 1.33982054e+21]]
>>
>> real    0m38.829s
>> user    0m41.541s
>> sys    0m3.399s
>>
>> So, I think that the benchmark dataset used by you for testing code
>> efficiency is not so appropriate. What's your point of view on this
>> testing results?
>
>
>
>   Evgeni has provided an interesting example on how to speed up your code - granted, he used toy data but the improvement is real. As far as I can see, you haven't specified how big are your DOS etc... vectors, so it's not that obvious how to draw any conclusions. I find it highly puzzling that his implementation appears to be slower than your original code.
>
> In any case, if performance is so paramount for you, then I would suggest you to move in the direction Evgeni was proposing, i.e. shifting your implementation to C/Cython or Fortran/f2py.

If so, I think that the C/Fortran based implementations should be more
efficient than the ones using Cython/f2py.


> I had much better results myself using Fortran/f2py than pure NumPy or C/Cython, but this is mostly because my knowledge of Cython is quite limited. That said, your problem should be fairly easy to implement in a compiled language.
>
> Andrea.
>
>
>>
>>
>> Regards,
>> HY
>>
>> >
>> >
>> >
>> > > Second:
>> > >
>> > > Move the loop into cython.
>> > >
>> > >
>> > >
>> > >
>> > > вс, 11 окт. 2020 г., 9:32 Hongyi Zhao <hongyi.zhao at gmail.com>:
>> > >>
>> > >> 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>
>> > >> >> >>> >> _______________________________________________
>> > >> >> >>> >> NumPy-Discussion mailing list
>> > >> >> >>> >> NumPy-Discussion at python.org
>> > >> >> >>> >> https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >> >> >>> >
>> > >> >> >>> > _______________________________________________
>> > >> >> >>> > NumPy-Discussion mailing list
>> > >> >> >>> > NumPy-Discussion at python.org
>> > >> >> >>> > https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >> >> >>>
>> > >> >> >>>
>> > >> >> >>>
>> > >> >> >>> --
>> > >> >> >>> Hongyi Zhao <hongyi.zhao at gmail.com>
>> > >> >> >>> _______________________________________________
>> > >> >> >>> NumPy-Discussion mailing list
>> > >> >> >>> NumPy-Discussion at python.org
>> > >> >> >>> https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >> >> >
>> > >> >> > _______________________________________________
>> > >> >> > NumPy-Discussion mailing list
>> > >> >> > NumPy-Discussion at python.org
>> > >> >> > https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >> >>
>> > >> >>
>> > >> >>
>> > >> >> --
>> > >> >> Hongyi Zhao <hongyi.zhao at gmail.com>
>> > >> >> _______________________________________________
>> > >> >> NumPy-Discussion mailing list
>> > >> >> NumPy-Discussion at python.org
>> > >> >> https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >> >
>> > >> > _______________________________________________
>> > >> > NumPy-Discussion mailing list
>> > >> > NumPy-Discussion at python.org
>> > >> > https://mail.python.org/mailman/listinfo/numpy-discussion
>> > >>
>> > >>
>> > >>
>> > >> --
>> > >> Hongyi Zhao <hongyi.zhao at gmail.com>
>> > >> _______________________________________________
>> > >> NumPy-Discussion mailing list
>> > >> NumPy-Discussion at python.org
>> > >> https://mail.python.org/mailman/listinfo/numpy-discussion
>> > _______________________________________________
>> > NumPy-Discussion mailing list
>> > NumPy-Discussion at python.org
>> > https://mail.python.org/mailman/listinfo/numpy-discussion
>>
>>
>>
>> --
>> Hongyi Zhao <hongyi.zhao at gmail.com>
>> _______________________________________________
>> NumPy-Discussion mailing list
>> NumPy-Discussion at python.org
>> https://mail.python.org/mailman/listinfo/numpy-discussion
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion



-- 
Hongyi Zhao <hongyi.zhao at gmail.com>

More information about the NumPy-Discussion mailing list