Hi Jiaqi, Most likely you're seeing the LAPACK-level parallelization. I'm not an expert on LAPACK using multiple CPU cores, but I haven't seen parallelization of LAPACK result in any useful speedup. Try setting the following environment variables (these are used by different LAPACK implementations):
OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 MKL_DYNAMIC=FALSE MKL_NUM_THREADS=1 Best, Anton On Thu, 21 May 2020 at 23:55, Zhou Jiaqi <jiaqi.z...@uclouvain.be> wrote: > > Hi everyone, > > I am writing to discuss the parallelization of Kwant. > > I installed Kwant with MUMPS from > Ubuntu(https://launchpad.net/ubuntu/+source/mumps). When I run a Kwant script > (which will be shown in the end) in the normal way on a 4-core laptop (2 > threads per core), the screenshot (the output of htop, see > https://drive.google.com/open?id=1TwEuc21DMjRnVQ9yG3XpkVilhFiAKaeb) shows > that all the 8 CPU threads are involved, but 21 python threads. > > However, according to the tutorial: " Kwant uses only the sequential, single > core version of MUMPS. The advantages due to MUMPS as used by Kwant are thus > independent of the number of CPU cores of the machine on which Kwant runs.” > > So it confuses me whether Kwant 1.4.1 has already employed parallelization or > not, and why there are 21 python threads? Moreover, I find the usage of > concurrent package slows down the calculation. > > Thanks a lot for your time, and it would be greatly appreciated if anyone > could share some news about the Kwant parallelization development. > > Best regards, > > Jiaqi > UCLouvain > > ______________________ > > import kwant > import tbmodels > import numpy as np > import matplotlib.pyplot as plt > > model = tbmodels.Model.from_wannier_files( > hr_file='graphene_hr.dat', > wsvec_file='graphene_wsvec.dat', > xyz_file='graphene_centres.xyz', > win_file='graphene.win', > h_cutoff=0.01) > > lattice = model.to_kwant_lattice() > > wire = kwant.Builder() > def shape(p): > x, y, z = p > return -5 < x < 5 and -5 < y < 5 and -1 < z < 1 > wire[lattice.shape(shape, (0, 0, 0))] = 0 > model.add_hoppings_kwant(wire) > > sym_lead_x = kwant.TranslationalSymmetry(lattice.vec((-2, 0, 0))) > lead_x = kwant.Builder(sym_lead_x) > def lead_shape(p): > x, y, z = p > return -5 <= x <= 5 and -5 < y < 5 and -1 < z < 1 > lead_x[lattice.shape(lead_shape, (0, 0, 0))] = 0 > model.add_hoppings_kwant(lead_x) > wire.attach_lead(lead_x) > wire.attach_lead(lead_x.reversed()) > > syst = wire.finalized() > > def trans(energy): > smatrix = kwant.smatrix(syst, energy) > data = smatrix.transmission(1, 0) > return data > > def main(): > energies = np.linspace(0, 1, 100) > tc = map(trans, energies) # transmission coefficient > te = zip(energies, tc) > lte = list(te) > print(lte) > > if __name__ == '__main__': > main()
