Results of simulation using `fixed_indegree` seem to depend on `total_num_virtual_procs`
26 Jul
2023
26 Jul
'23
12:10 p.m.
Hello everyone,
I am writing to the NEST mailing list as I have encountered an issue in
my simulation script, and I would greatly appreciate your insights.
The problem I am facing is that the membrane potential time course for a
simulation with a fixed indegree seems to be dependent on the number of
virtual processes (`total_num_virtual_procs`). Interestingly, this
behavior does not occur for simulations with all-to-all connectivity.
The simulation involves a network with the following architecture: 5
`spike_generator` nodes connected to 5 `parrot_neuron` nodes with
(all-to-all), which are further connected to 5 `iaf_psc_delta` neurons
(all-to-all). The connections between the `iaf_psc_delta` neurons have
an `fixed_indegree` of 2. A voltmeter is used to monitor the membrane
potentials of these 5 neurons, and the simulation runs for 100 ms.
The issue arises when I vary the number of virtual processes
(`total_num_virtual_procs`). I have tested the values 1, 2, and 4, and I
have noticed that the plot of the membrane potentials at the end of the
simulation differs for each `total_num_virtual_procs` value.
Additionally, when I print the table of connections, I observe that the
weights between the `iaf_psc_delta` neurons are also influenced by the
number of virtual processes.
I have attached the simulation script and three images showing the plot
for each `total_num_virtual_procs` value. The experiments were conducted
on my personal Ubuntu machine.
The version of NEST that I am using is the latest at the time of writing
this message, with commit `b9514cce789fb0b2c0dfbd9d0894d3986f743021`,
authored by Abigail Morrison <morrison(a)fz-juelich.de> on Tue Jul 18
23:46:22 2023 +0200.
I would be grateful for any suggestions or explanations regarding this
issue, as it hinders the reproducibility of my results.
Thank you in advance for your valuable help and support.
Best regards,
Jesus Espinoza.
--
Jesús Espinoza
Bergische Universität Wuppertal
Fakultät für Mathematik und Naturwissenschaften Wissenschaftliches
Rechnen und Hochleistungsrechnen Gaußstraße 20
42119 Wuppertal
Germany
E-Mail: espinozavalverd{at}uni-wuppertal.de
26 Jul
26 Jul
4:53 p.m.
Dear Jesús,
The NEST Simulator documentation on parallel computing has a section
regarding reproducibility:
https://nest-simulator.readthedocs.io/en/stable/hpc/parallel_computing.html
"To achieve the same simulation results even when using different
parallelization strategies, the number of virtual processes has to be
kept constant. A simulation with a specific number of virtual processes
will always yield the same results, no matter how they are distributed
over threads and processes, given that the seeds for the random number
generators of the different virtual processes are the same (see Random
numbers in NEST)."
Each virtual process has its own instance (and unique seed) in the
random number generator. This will result in different typologies and
random current/spikes generated. What you are describing is expected
behavior.
Sincerely,
Wouter
On 26-Jul-23 12:10, Jesus Espinoza wrote:
Hello everyone,
I am writing to the NEST mailing list as I have encountered an issue in
my simulation script, and I would greatly appreciate your insights.
The problem I am facing is that the membrane potential time course for a
simulation with a fixed indegree seems to be dependent on the number of
virtual processes (`total_num_virtual_procs`). Interestingly, this
behavior does not occur for simulations with all-to-all connectivity.
The simulation involves a network with the following architecture: 5
`spike_generator` nodes connected to 5 `parrot_neuron` nodes with
(all-to-all), which are further connected to 5 `iaf_psc_delta` neurons
(all-to-all). The connections between the `iaf_psc_delta` neurons have
an `fixed_indegree` of 2. A voltmeter is used to monitor the membrane
potentials of these 5 neurons, and the simulation runs for 100 ms.
The issue arises when I vary the number of virtual processes
(`total_num_virtual_procs`). I have tested the values 1, 2, and 4, and I
have noticed that the plot of the membrane potentials at the end of the
simulation differs for each `total_num_virtual_procs` value.
Additionally, when I print the table of connections, I observe that the
weights between the `iaf_psc_delta` neurons are also influenced by the
number of virtual processes.
I have attached the simulation script and three images showing the plot
for each `total_num_virtual_procs` value. The experiments were conducted
on my personal Ubuntu machine.
The version of NEST that I am using is the latest at the time of writing
this message, with commit `b9514cce789fb0b2c0dfbd9d0894d3986f743021`,
authored by Abigail Morrison <morrison(a)fz-juelich.de> on Tue Jul 18
23:46:22 2023 +0200.
I would be grateful for any suggestions or explanations regarding this
issue, as it hinders the reproducibility of my results.
Thank you in advance for your valuable help and support.
Best regards,
Jesus Espinoza.
--
Jesús Espinoza
Bergische Universität Wuppertal
Fakultät für Mathematik und Naturwissenschaften Wissenschaftliches
Rechnen und Hochleistungsrechnen Gaußstraße 20
42119 Wuppertal
Germany
E-Mail: espinozavalverd{at}uni-wuppertal.de
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--
Wouter Klijn <w.klijn(a)fz-juelich.de>
Team Leader Multiscale Simulation and Design
Simulation and Datalab Neuroscience
Jülich Supercomputing Centre
Institute for Advanced Simulation
Forschungszentrum Jülich
http://www.fz-juelich.de/ias/jsc/slns
Office: +49 2461 61-3523
Fax # : +49 2461 61-6656
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Vorsitzender des Aufsichtsrats: MinDir Stefan Müller
Geschaeftsfuehrung: Prof. Dr.-Ing. Wolfgang Marquardt (Vorsitzender),
Karsten Beneke (stellv. Vorsitzender), Dr. Ir. Pieter Jansens,
Prof. Dr. Astrid Lambrecht, Prof. Dr. Frauke Melchior
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5:27 p.m.
Thank you for your kind response.
My primary concern revolves around weak scaling. I am specifically interested in
understanding how the weak scaling results for a very large and sparse network (e.g. with
a small `fixed_indegree`) would be affected by the emerging differences in sparsity
patterns and overall dynamics due to an increasing number of virtual processes. My
intuition suggests that these differences may "balance out," thereby making the
scaling results reliable. However, I am curious to know if there exists any mathematical
guarantee or theory that either supports or disproves this hypothesis. Any insights or
references on this matter would be greatly appreciated.
Best,
Jesus Espinoza.
31 Jul
31 Jul
4:42 p.m.
Hello Jesus,
Several aspects come into play in when using simulations to explore the dynamics of
network models.
First, any given simulation will result in one specific realisation of the model dynamics.
If you run a simulation twice under identical circumstances (same random seed, same number
of parallel processes), you must get identical results, otherwise something is wrong with
the simulator.
If you change the random seed in your simulation, then you will get model dynamics that
differ in detail in most cases (see below for exceptions). For networks with
"boring" dynamics such as a Brunel network with asynchronous irregular activity,
this variation will only be in details of spike trains and voltage traces. For models with
more complex dynamics, e.g., switching between "up" and "down" states
at larger, random, intervals, the differences can be larger. But if proper statistical
measures are applied over long enough traces and sufficiently many realisation, consistent
result shall be obtained.
As Wouter already pointed out, changing the number of virtual processes in a NEST
simulation implicitly changes the random number sequences provided to specific neurons
(and used to randomise connections), so even if the rng_seed set in NEST is the same,
varying the number of virtual processes will yield different realisations.
Given that you use `spike_generator`s with fixed input spike trains and then make
all-to-all connections throughout your network, there is no randomness left in your
network (assuming you do not randomize connection weights or delays). Then, random numbers
play no role and you get identical results for any number of VPs.
Overall, changing the number of VPs should only influence the time required to simulate
your model, not the dynamics the model exhibits.
Best regards,
Hans Ekkehard
--
Prof. Dr. Hans Ekkehard Plesser
Head, Department of Data Science
Faculty of Science and Technology
Norwegian University of Life Sciences
PO Box 5003, 1432 Aas, Norway
Phone +47 6723 1560
Email hans.ekkehard.plesser@nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no>
Home http://arken.nmbu.no/~plesser
From: espinozavalverd(a)uni-wuppertal.de <espinozavalverd(a)uni-wuppertal.de>
Date: Wednesday, 26 July 2023 at 17:31
To: users(a)nest-simulator.org <users(a)nest-simulator.org>
Subject: [NEST Users] Re: Results of simulation using `fixed_indegree` seem to depend on
`total_num_virtual_procs`
Sorry, I meant to say Strong scaling.
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4 comments
4 participants
participants (4)
-
espinozavalverd＠uni-wuppertal.de -
Hans Ekkehard Plesser -
Jesus Espinoza
-
Wouter Klijn