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@INPROCEEDINGS{Villamar:1014309,
author = {Villamar, Jose and Golosio, Bruno and Tiddia, Gianmarco and
Pastorelli, Elena and Stapmanns, Jonas and Fanti, Viviana
and Diesmann, Markus and Paolucci, Pier Stanislao and
Morrison, Abigail and Senk, Johanna},
title = {{A}ccelerating {N}euronal {N}etwork {C}onstruction through
{D}ynamic {GPU} {M}emory {I}nstantiation},
reportid = {FZJ-2023-03231},
year = {2023},
note = {This project was also funded by the Italian PNRR MUR
project PE0000013-FAIR, funded by NextGenerationEU.},
abstract = {Efficient simulation of large-scale spiking neuronal
networks is important for neuroscientific research, and both
the simulation speed and the time it takes to instantiate
the network in computer memory are key factors. In recent
years, hardware acceleration through highly parallel GPUs
has become increasingly popular. Similarly, code generation
approaches have been utilized to optimize software
performance, albeit at the cost of repeated code
regeneration and recompilation after modifications to the
network model [1].To address the need for greater
flexibility in iterative model changes, we propose a new
method for creating network connections dynamically and
directly in GPU memory. This method uses a set of commonly
used high-level connection rules [2], enabling interactive
network construction.We validate the simulation performance
with both consumer and data center GPUs on a cortical
microcircuit of about 77,000 leaky-integrate-and-fire neuron
models and 300 million synapses [3], and a two-population
recurrently connected network designed to allow benchmarking
of a variety of connection rules.We implement our proposed
method in NEST GPU [4,5] and demonstrate the same or shorter
network construction and simulation times compared to other
state-of-the-art simulation technologies. Moreover, our
approach meets the flexibility demands of explorative
network modeling by enabling direct and dynamic changes to
the network in GPU memory.[1] Knight, J.C.; Nowotny, T. GPUs
Outperform Current HPC and Neuromorphic Solutions in Terms
of Speed and Energy When Simulating a Highly-Connected
Cortical Model. Frontiers in Neuroscience 2018, 12.
https://doi.org/10.3389/fnins.2018.00941.[2] Senk, J.;
Kriener, B.; Djurfeldt, M.; Voges, N.; Jiang, H.J.;
Schüttler, L.; Gramelsberger, G.; Diesmann, M.; Plesser,
H.E.; van Albada, S.J. Connectivity concepts in neuronal
network modeling. PLOS Computational Biology 2022, 18,
e1010086. https://doi.org/10.1371/journal.pcbi.1010086.[3]
Potjans, T.C.; Diesmann, M. The Cell-Type Specific Cortical
Microcircuit: Relating Structure and Activity in a
Full-Scale Spiking Network Model. Cerebral Cortex 2014, 24,
785–806. https://doi.org/10.1093/cercor/bhs358.[4]
Golosio, B.; Tiddia, G.; De Luca, C.; Pastorelli, E.;
Simula, F.; Paolucci, P.S. Fast Simulations of
Highly-Connected Spiking Cortical Models Using GPUs.
Frontiers in Computational Neuroscience 2021, 15.
https://doi.org/10.3389/fncom.2021.627620.[5] Tiddia, G.;
Golosio, B.; Albers, J.; Senk, J.; Simula, F.; Pronold, J.;
Fanti, V.; Pastorelli, E.; Paolucci, P.S.; van Albada, S.J.
Fast Simulation of a Multi-Area Spiking Network Model of
Macaque Cortex on an MPI-GPU Cluster. Frontiers in
Neuroinformatics 2022, 16.
https://doi.org/10.3389/fninf.2022.883333.},
month = {Jun},
date = {2023-06-15},
organization = {NEST Conference, Virtual (Germany), 15
Jun 2023 - 16 Jun 2023},
subtyp = {After Call},
cin = {INM-6 / IAS-6 / INM-10},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
I:(DE-Juel1)INM-10-20170113},
pnm = {5232 - Computational Principles (POF4-523) / 5235 -
Digitization of Neuroscience and User-Community Building
(POF4-523) / HBP SGA3 - Human Brain Project Specific Grant
Agreement 3 (945539) / MetaMoSim - Generic metadata
management for reproducible high-performance-computing
simulation workflows - MetaMoSim (ZT-I-PF-3-026) / JL SMHB -
Joint Lab Supercomputing and Modeling for the Human Brain
(JL SMHB-2021-2027) / Brain-Scale Simulations
$(jinb33_20220812)$ / ICEI - Interactive Computing
E-Infrastructure for the Human Brain Project (800858)},
pid = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5235 /
G:(EU-Grant)945539 / G:(DE-Juel-1)ZT-I-PF-3-026 /
G:(DE-Juel1)JL SMHB-2021-2027 /
$G:(DE-Juel1)jinb33_20220812$ / G:(EU-Grant)800858},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/1014309},
}