4.8: Parallelism: Using the MPI backend

This example demonstrates how to use the MPI backend for simulating dipoles using hnn_core.

hnn_core can take advantage of MPI libraries such as OpenMPI to run a single simulation using multiple CPU processors. MPI lets you divide parts of your simulated network across CPUs while allowing the CPUs to "talk" to each other, and can therefore enable significant speed-up of individual simulations.

Note that to use MPI parallelism, you need either the conda install or the MPI Installation dependencies described in our Installation Guide here.

Note that MPI parallelism is distinct from hnn_core's use of Joblib parallelism, which can be found here.


        # Authors: Mainak Jas 
#          Blake Caldwell 
#          Austin Soplata

Let us import what we need from hnn_core:


        import matplotlib.pyplot as plt

from hnn_core import simulate_dipole, jones_2009_model
from hnn_core.viz import plot_dipole

Following our Alpha example, we will create our network and add a ~10 Hz "bursty" drive:


        net = jones_2009_model()

weights_ampa = {'L2_pyramidal': 5.4e-5, 'L5_pyramidal': 5.4e-5}
net.add_bursty_drive(
    'bursty', tstart=50., burst_rate=10, burst_std=20., numspikes=2,
    spike_isi=10, n_drive_cells=10, location='distal',
    weights_ampa=weights_ampa, event_seed=278)

Finally, we will simulate using the MPIBackend class. This will start the simulation across the number of processors (cores) specified by n_procs using MPI.


        from hnn_core import MPIBackend

with MPIBackend(n_procs=4, mpi_cmd='mpiexec'):
    dpls = simulate_dipole(net, tstop=210., n_trials=1)

Out:

MPI will run 1 trial(s) sequentially by distributing network neurons over 4 processes. /opt/anaconda3/envs/website-redesign-mpi/bin/nrniv:10: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html from pkg_resources import working_set /opt/anaconda3/envs/website-redesign-mpi/bin/nrniv:10: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html from pkg_resources import working_set /opt/anaconda3/envs/website-redesign-mpi/bin/nrniv:10: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html from pkg_resources import working_set /opt/anaconda3/envs/website-redesign-mpi/bin/nrniv:10: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html from pkg_resources import working_set numprocs=4 Loading custom mechanism files from /opt/anaconda3/envs/website-redesign-mpi/lib/python3.12/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so Building the NEURON model Loading custom mechanism files from /opt/anaconda3/envs/website-redesign-mpi/lib/python3.12/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so Loading custom mechanism files from /opt/anaconda3/envs/website-redesign-mpi/lib/python3.12/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so Loading custom mechanism files from /opt/anaconda3/envs/website-redesign-mpi/lib/python3.12/site-packages/hnn_core/mod/arm64/.libs/libnrnmech.so [Done] Trial 1: 0.03 ms... Trial 1: 10.0 ms... Trial 1: 20.0 ms... Trial 1: 30.0 ms... Trial 1: 40.0 ms... Trial 1: 50.0 ms... Trial 1: 60.0 ms... Trial 1: 70.0 ms... Trial 1: 80.0 ms... Trial 1: 90.0 ms... Trial 1: 100.0 ms... Trial 1: 110.0 ms... Trial 1: 120.0 ms... Trial 1: 130.0 ms... Trial 1: 140.0 ms... Trial 1: 150.0 ms... Trial 1: 160.0 ms... Trial 1: 170.0 ms... Trial 1: 180.0 ms... Trial 1: 190.0 ms... Trial 1: 200.0 ms...


        plot_dipole(dpls, show=False)
plt.show()

Out: