Speaker
Description
Modern computational neuroscience seeks to explain the dynamics and function of the brain by constructing models with ever more biological detail. This can, for example, take the form of sophisticated connectivity schemes [1] or involve the simultaneous simulation of multiple brain areas [2]. To enable progress in these studies, the simulation of models needs to become faster, calling for more efficient implementations of the underlying simulators. Performance benchmarking guides software development since it is hard to predict the impact of algorithm adaptations on the performance of complex software such as neuronal network simulators [3]. The particular challenge for these simulators is that executing benchmarks naturally involves the simulation of a diverse range of network models as they may uncover different performance limitations due to their variation in size, synaptic density and distribution of delays [4]. In addition, maintaining an accessible library of past results while keeping track of metadata that specifies hardware, software, simulator and model configurations is a difficult task. Here, we introduce beNNch [5] – a recently developed framework for benchmarking neuronal network simulations – and walk through a typical use case, highlighting how it simplifies workflows and enables sustainable use of computing resources.
References
[1] Billeh, Y. N., Cai, B., Gratiy, S. L., Dai, K., Iyer, R., Gouwens, N. W., et al. (2020). Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 106, 388-403.e18. doi: 10.1016/j.neuron.2020.01.040
[2] Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., and van Albada, S. J. (2018a). Multi-scale account of the network structure of macaque visual cortex. Brain Struct Funct. 223, 1409–1435. doi: 10.1007/s00429-017-1554-4
[3] Jordan, J., Ippen, T., Helias, M., Kitayama, I., Sato, M., Igarashi, J., 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] Albers, J., Pronold, J., Kurth, A. C., Vennemo, S. B., Haghighi Mood, K., Patronis, A., et al. (in press). A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations. Front. Neuroinform. doi: 10.3389/fninf.2022.837549
[5] https://github.com/INM-6/beNNch
Acknowledgements
We thank the members of the NEST development community for their contributions to the concepts and implementation of the NEST simulator, and our colleagues in the Simulation and Data Laboratory Neuroscience of the Jülich Supercomputing Centre for continuous collaboration. We 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). 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.
This project has received funding from EU Grant 945539 (HBP) and 754304 (DEEP-EST); Helmholtz IVF Grant SO-902 (ACA); Joint lab SMHB; the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—368482240/GRK2416; and the Helmholtz Metadata Collaboration (HMC) ZT-I-PF-3-026.
| Preferred form of presentation | Talk & (optional) poster |
|---|---|
| Topic area | simulator technology and performance |
| Keywords | spiking neuronal networks, benchmarking, large-scale simulation, high-performance computing, workflow, metadata |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
