Speaker
Description
Spiking neural network simulations are establishing themselves as an effective tool for studying the dynamics of neuronal populations and the relationship between these dynamics and brain functions. Further, advances in computational resources and technologies are increasingly enabling large-scale simulations capable of describing brain regions in detail. NEST GPU [1,2] is a GPU-based simulator under the NEST Initiative written in CUDA-C++ for large-scale simulations of spiking neural networks. Here we evaluated its performance on the simulation of a multi-area model of macaque vision-related cortex [3, 4], made up of about 4 million neurons and 24 billion synapses. The outcome of the simulations is compared against that obtained using NEST 3.0 on a high-performance computing cluster. The results show an optimal match with the NEST statistical measures of neural activity, together with remarkable achievements in terms of simulation time per second of biological activity. Indeed, using 32 compute nodes equipped with an NVIDIA V100 GPU each, NEST GPU simulated a second of biological time of the full-scale macaque cortex model in its metastable state 3.1x faster than NEST running on the same number of compute nodes equipped with two AMD EPYC 7742 (2x64 cores).
References
[1] Golosio et al. (2021), Frontiers in Computational Neuroscience, DOI:10.3389/fncom.2021.627620
[2] Source Code: https://github.com/nest/nest-gpu
[3] Schmidt et al. (2018), Brain Struct Funct, DOI:10.1007/s00429-017-1554-4
[4] Schmidt et al. (2018), PLOS Computational Biology, DOI:10.1371/journal.pcbi.1006359
Acknowledgements
This study was supported by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 945539 (HBP SGA3) and No. 785907 (HBP SGA2), the Priority Program 2041 (SPP 2041) “Computational Connectomics” (DFG), the Helmholtz IVF Grant SO-092 (ACA), the Joint Lab SMHB, and the INFN APE Parallel/Distributed Computing laboratory. We acknowledge 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.
| Preferred form of presentation | Talk & (optional) poster |
|---|---|
| Topic area | simulator technology and performance |
| Speaker time zone | UTC+1 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
