Speaker
Description
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.
References
[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.
Acknowledgements
This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the Initiative and Networking Fund of the Helmholtz Association in the framework of the Helmholtz Metadata Collaboration project call (ZT-I-PF-3-026), and the Joint Lab “Supercomputing and Modeling for the Human Brain”, the Italian PNRR MUR project PE0000013-FAIR, funded by NextGenerationEU.
The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA at Forschungszentrum Jülich (computation grant JINB33), and
the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the Grant Agreement No. 800858.
The authors further thank the INFN APE Parallel/Distributed Computing laboratory and the the INM-6/IAS-6.
Part of the work was performed while Gianmarco Tiddia enjoyed a scientific stay at INM-6/IAS-6 in the period 21th of September 2022 to 28th of March 2023.
The authors would also like to thank Markus Diesmann for fruitful discussions.
The authors BG, GT, and JV have equal contribution.
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | simulator technology and performance |
| Keywords | Spiking Neuronal Networks, GPU, Computational Neuroscience, Network Connectivity |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
