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

Hongyi Zhao hongyi.zhao at gmail.com
Sun Oct 11 06:31:20 EDT 2020 

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 can't find the cython part suggested by you, i.e., move the loop
into cython. Furthermore, I also learned that the numpy array is
optimized and has the performance close to C/C++.

>
>
>
> > 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
> >> >> >>> >
> >> >> >>> > _______________________________________________
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> >> >> >>>
> >> >> >>>
> >> >> >>>
> >> >> >>> --
> >> >> >>> 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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> >> >>
> >> >>
> >> >>
> >> >> --
> >> >> Hongyi Zhao <hongyi.zhao at gmail.com>
> >> >> _______________________________________________
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> >> >> NumPy-Discussion at python.org
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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>

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