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

Andrea Gavana andrea.gavana at gmail.com
Mon Oct 12 09:33:06 EDT 2020 

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. 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
>
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