04. Use MPI backend for parallelization

This example demonstrates how to use the MPI backend for simulating dipoles using HNN-core.

The MPI backend allows running the simulation in parallel across neurons in the network even with a single trial. For this, you will need the MPI related software installed. Note that if you want to simulate in parallel across trials, the Joblib backend allows this without the need to install and configure MPI.

# Authors: Mainak Jas <mjas@mgh.harvard.edu>
#          Blake Caldwell <blake_caldwell@brown.edu>

Let us import hnn_core

import os.path as op

import hnn_core
from hnn_core import simulate_dipole, jones_2009_model

Following the alpha example, we add a ~10 Hz “bursty” drive starting at 50 ms and continuing to the end of the simulation. Each burst consists of a pair (2) of spikes, spaced 10 ms apart. The occurrence of each burst is jittered by a random, normally distributed amount (20 ms standard deviation). We repeat the burst train 10 times, each time with unique randomization.

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=8)

Finally, to simulate we use the MPIBackend class. This will start the simulation across the number of processors (cores) specified by n_procs using MPI. The 'mpiexec' launcher is used from openmpi, which must be installed on the system

from hnn_core import MPIBackend

with MPIBackend(n_procs=2, mpi_cmd='mpiexec'):
    dpls = simulate_dipole(net, tstop=310., n_trials=1)

trial_idx = 0
dpls[trial_idx].plot()

Out:

MPI will run over 2 processes
Running 1 trials...
numprocs=2
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
Building the NEURON model
Loading custom mechanism files from /home/ryan/hnn-core/hnn_core/mod/x86_64/.libs/libnrnmech.so
[Done]
running trial 1 on 2 cores
Simulation time: 0.03 ms...
Simulation time: 10.0 ms...
Simulation time: 20.0 ms...
Simulation time: 30.0 ms...
Simulation time: 40.0 ms...
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Simulation time: 130.0 ms...
Simulation time: 140.0 ms...
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Simulation time: 170.0 ms...
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Simulation time: 300.0 ms...

<Figure size 640x480 with 1 Axes>