On Mon, Oct 12, 2020 at 9:33 PM Andrea Gavana <andrea.gav...@gmail.com> wrote: > > 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.
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.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 -- Hongyi Zhao <hongyi.z...@gmail.com> _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
