Speaker
Description
Simulating brain-scale models requires parallel computers to provide enough memory to represent network connectivity and efficient instantiation of complex network connectivity on massively parallel computers. While scalable data structures and algorithms for storing and accessing connections in parallel are available [1-3], efficient parallel instantiation of such networks has received less attention. Network connectivity can be defined either rule-based [4] or through explicit tabulation of connections, e.g., using the SONATA format [5]. Even for models of limited size and complexity, such as a model of the mouse cortex with more than 9 million point neurons connected by 25 billion synapses, SONATA specification files comprise nearly 500 GB of data in mostly binary format (HDF5). We present here an implementation of direct support for efficient instantiation of networks from SONATA specifications in the NEST simulator [6] as a result of the HBP NEST-SONATA infrastructure voucher.
Acknowledgements
This work has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreement 945539 (HBP SGA3; HEP, NH, HBM, SBV, SK) and was supported by the Allen Institute, by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R01EB029813, and the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Numbers R01NS122742 and U24NS124001 (KD, AA). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 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.
References
[1] Kunkel et al. (2014). Spiking network simulation code for petascale computers. Front. Neuroinform. 8:78. doi: 10.3389/fninf.2014.00078
[2] Ippen et al. (2017). Constructing Neuronal Network Models in Massively Parallel Environments. Front. Neuroinform. 11:30. doi: 10.3389/fninf.2017.00030
[3] Jordan et al. (2018). Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Front. Neuroinform. 12:2. doi: 10.3389/fninf.2018.00002
[4] Senk et al. (2022). Connectivity concepts in neuronal network modeling. PLoS Comput Biol 18(9): e1010086 doi: 10.1371/journal.pcbi.1010086
[5] Dai et al. (2020). The SONATA data format for efficient description of large-scale network models. PLoS Comput Biol 16(2): e1007696 doi: 10.1371/journal. pcbi.1007696
[6] Gewaltig & Diesmann (2007). NEST (Neural Simulation Tool) Scholarpedia 2(4):1430 doi: 10.4249/scholarpedia.1430
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | simulator technology and performance |
| Keywords | Simulation, Modeling, Large-scale networks, High-performance computing, Connectome |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
