Hi, On Mon, 12 Oct 2020 at 14:38, Hongyi Zhao <hongyi.z...@gmail.com> wrote:
> On Sun, Oct 11, 2020 at 3:42 PM Evgeni Burovski > <evgeny.burovs...@gmail.com> wrote: > > > > On Sun, Oct 11, 2020 at 9:55 AM Evgeni Burovski > > <evgeny.burovs...@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.z...@gmail.com>: > > >> > > >> On Sun, Oct 11, 2020 at 2:02 PM Andrea Gavana < > andrea.gav...@gmail.com> wrote: > > >> > > > >> > > > >> > > > >> > On Sun, 11 Oct 2020 at 07.52, Hongyi Zhao <hongyi.z...@gmail.com> > wrote: > > >> >> > > >> >> On Sun, Oct 11, 2020 at 1:33 PM Andrea Gavana < > andrea.gav...@gmail.com> wrote: > > >> >> > > > >> >> > > > >> >> > > > >> >> > On Sun, 11 Oct 2020 at 07.14, Andrea Gavana < > andrea.gav...@gmail.com> wrote: > > >> >> >> > > >> >> >> Hi, > > >> >> >> > > >> >> >> On Sun, 11 Oct 2020 at 00.27, Hongyi Zhao < > hongyi.z...@gmail.com> wrote: > > >> >> >>> > > >> >> >>> On Sun, Oct 11, 2020 at 1:48 AM Robert Kern < > robert.k...@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.z...@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.z...@gmail.com> > > >> >> >>> >> _______________________________________________ > > >> >> >>> >> NumPy-Discussion mailing list > > >> >> >>> >> NumPy-Discussion@python.org > > >> >> >>> >> https://mail.python.org/mailman/listinfo/numpy-discussion > > >> >> >>> > > > >> >> >>> > _______________________________________________ > > >> >> >>> > NumPy-Discussion mailing list > > >> >> >>> > NumPy-Discussion@python.org > > >> >> >>> > https://mail.python.org/mailman/listinfo/numpy-discussion > > >> >> >>> > > >> >> >>> > > >> >> >>> > > >> >> >>> -- > > >> >> >>> Hongyi Zhao <hongyi.z...@gmail.com> > > >> >> >>> _______________________________________________ > > >> >> >>> NumPy-Discussion mailing list > > >> >> >>> NumPy-Discussion@python.org > > >> >> >>> https://mail.python.org/mailman/listinfo/numpy-discussion > > >> >> > > > >> >> > _______________________________________________ > > >> >> > NumPy-Discussion mailing list > > >> >> > NumPy-Discussion@python.org > > >> >> > https://mail.python.org/mailman/listinfo/numpy-discussion > > >> >> > > >> >> > > >> >> > > >> >> -- > > >> >> Hongyi Zhao <hongyi.z...@gmail.com> > > >> >> _______________________________________________ > > >> >> NumPy-Discussion mailing list > > >> >> NumPy-Discussion@python.org > > >> >> https://mail.python.org/mailman/listinfo/numpy-discussion > > >> > > > >> > _______________________________________________ > > >> > NumPy-Discussion mailing list > > >> > NumPy-Discussion@python.org > > >> > https://mail.python.org/mailman/listinfo/numpy-discussion > > >> > > >> > > >> > > >> -- > > >> Hongyi Zhao <hongyi.z...@gmail.com> > > >> _______________________________________________ > > >> NumPy-Discussion mailing list > > >> NumPy-Discussion@python.org > > >> https://mail.python.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > -- > Hongyi Zhao <hongyi.z...@gmail.com> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
