TY  - EJOUR
AU  - Golosio, Bruno
AU  - Villamar, Jose
AU  - Tiddia, Gianmarco
AU  - Pastorelli, Elena
AU  - Stapmanns, Jonas
AU  - Fanti, Viviana
AU  - Paolucci, Pier Stanislao
AU  - Morrison, Abigail
AU  - Senk, Johanna
TI  - Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
PB  - arXiv
M1  - FZJ-2023-03232
PY  - 2023
N1  - 29 pages, 9 figures. This project was also funded by the Italian PNRR MUR project PE0000013-FAIR, funded by NextGenerationEU.
AB  - Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory.On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized.On the software side, code generation approaches ensure highly optimized code, at the expense of repeated code regeneration and recompilation after modifications to the network model.Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules.We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models:a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules.With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies, while still meeting the flexibility demands of explorative network modeling.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.34734/FZJ-2023-03232
UR  - https://juser.fz-juelich.de/record/1014310
ER  -