Abstract
We present a solution to two important problems that arise in the simulation of large data-driven neural networks: (a) efficient loading of network descriptions and (b) efficient instantiation of the network by executing the model specification. To address the first problem, we present a general data-format PointBrainH5, to store the network information along with the parallel-distributed RTC algorithm to efficiently load and instantiate a network model. We test data-format and algorithm on a data-driven simulation of the size of a full mouse brain on 4 racks of a IBM Blue Gene/Q. The model comprised 75 million neurons with 664 billion synapses and occupied 15 TB on disk. Loading and instantiation of the network on 4 racks of the BlueGene/Q took 30 min. We observe good scaling for up to 16,384 nodes.
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References
Gewaltig, M.-O., Diesmann, M.: NEST (neural simulation tool). Scholarpedia 2(4), 1430 (2007)
Jones, A.R., Overly, C.C., Sunkin, S.M.: The Allen brain atlas: 5 years and beyond. Nat. Rev. Neurosci. 10(11), 821–828 (2009)
Stephan, M., Docter, J.: JUQUEEN: IBM Blue Gene/Q Supercomputer system at the Jlich supercomputing centre. J. Large-Scale Res. Facil. JLSRF 1, 1 (2015)
Kunkel, S., et al.: Spiking network simulation code for petascale computers. Front. Neuroinformatics 8, 78 (2014)
Markram, H., Muller, E., Ramaswamy, S., Reimann, M.W., Abdellah, M., Sanchez, C.A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492 (2015)
Brette, R., Gerstner, W.: Adaptive exponential integrate-and-fire model as an effective description of neural activity. J. Neurophysiol. 94(5), 3637–3642 (2005)
Fuhrmann, G., Segev, I., Markram, H., Tsodyks, M.: Coding of temporal information by activity-dependent synapses. J. Neurophysiol. 87(1), 140–148 (2002)
Izhikevich, E.M., Edelman, G.M.: Large-scale model of mammalian thalamocortical systems. Proc. Natl. Acad. Sci. 105(9), 3593–3598 (2008)
Ananthanarayanan, R., et al.: The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. IEEE (2009)
Blue Brain Project. Blue Brain 4 (2016). http://www.cscs.ch/computers/blue_brain_4/index.html
The HDF Group Hierarchical Data Format, version 5 (2016). http://www.hdfgroup.org/HDF5
Schumann, T., Delalondre, F.: HDF5 import module for the spiking neuronal simulator NEST. In: JUQUEEN Extreme Scaling Workshop, no. FZJ-2016-01816, pp. 43–48 (2016)
Eroe, C., et al.: Estimation of neuron numbers and densities of the mouse brain (in preparation)
Bos, H., et al.: NEST 2.10.0. Zenodo (2015). doi:10.5281/zenodo.44222
Schumann, T.: Nest-simulator, branch:h5kernel (2016). GitHub repository. https://github.com/tillschumann/nest-simulator/tree/h5kernel
Bos, H., et al.: Nest-simulator, ref:4b0f360 (2016). GitHub repository. https://github.com/nest/nest-simulator
Acknowledgement
The authors of this paper would like to gratefully thank both the HPC and the Visualization teams of the Blue Brain Project, for the many discussions and feedback provided. This work has been funded by both the EPFL Blue Brain Project (funded by the Swiss ETH board) and the European Union Seventh Framework Program (FP7/20072013) under grant agreement no. 604102 (HBP).
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Schumann, T., Erő, C., Gewaltig, MO., Delalondre, F.J. (2017). Towards Simulating Data-Driven Brain Models at the Point Neuron Level on Petascale Computers. In: Di Napoli, E., Hermanns, MA., Iliev, H., Lintermann, A., Peyser, A. (eds) High-Performance Scientific Computing. JHPCS 2016. Lecture Notes in Computer Science(), vol 10164. Springer, Cham. https://doi.org/10.1007/978-3-319-53862-4_14
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DOI: https://doi.org/10.1007/978-3-319-53862-4_14
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