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
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
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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()