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 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.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
