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| 001 | 1022261 | ||
| 005 | 20240313103114.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-01379 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-01379 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Lober, Melissa |0 P:(DE-Juel1)190224 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a INM Retreat |c Research Center Jülich |d 2023-10-17 - 2023-10-18 |w Germany |
| 245 | _ | _ | |a Exploiting network topology in brain-scale multi-area model simulations |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 502 | _ | _ | |c RWTH Aachen |
| 520 | _ | _ | |a The communication of spike events constitutes a major bottleneck in simulations of brain-scale networks with realistic connectivity, as for example, the multi-area model of macaque visual cortex [1], [2]. The model consists of 32 cortical microcircuits, each representing a downscaled area of the visual cortex at the resolution of single neurons and synapses. Such models not only have a dense connectivity within areas but also between areas, as approximately half of a neuron’s connections reach beyond its own brain region. Synaptic transmission delays within an area can be as short as 0.1 ms and therefore simulations require frequent spike communication between compute nodes to maintain causality in the network dynamics [3]. This poses a challenge to the conventional round-robin scheme used to distribute neurons uniformly across compute nodes disregarding the network’s specific topology. With this scheme, short-delay connections are present between any pair of compute nodes which significantly impairs communication efficiency.We target this challenge and propose a structure-aware neuron distribution scheme along with a novel spike-communication framework that exploits this approach in order to make communication in large-scale distributed simulations more efficient. In the structure-aware neuron distribution scheme, neurons are placed on the hardware in a way that mimics the network’s topology. Neurons of the same area are placed closer together on the hardware, i.e. onto the same or only few different compute nodes. Investigations of the load balancing in simulation phases where compute nodes operate independently (e.g. update of neuronal state variables) showed that such neuron distributions do not negatively affect compute time. Paired with a communication framework that distinguishes short delay intra-area communication between a small group of compute nodes and long delay inter-area communication between all available compute nodes, the structure-aware approach requires less of the costly global communication and thereby reduces communication time. Our prototype implementation is fully tested and was developed within the neuronal simulator tool NEST [4], [5].For the benchmarking of our approach, we developed a multi-area model that resembles the macaque multi-area model in terms of connectivity and work load, while being more easily scalable as it retains constant activity levels. First weak-scaling experiments showed that our implementation significantly reduces communication time and the effect increases with a rising number of compute nodes. For further analysis, we plan to study how the novel structure-aware communication scheme performs for larger networks, various inter- and intra-area delay distributions, as well as multi-area networks of unbalanced activity or size across areas. [1] Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ (2018) Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function, 223: 1409 https://doi.org/10.1007/s00429-017-1554-4[2] Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. PLOS Computational Biology, 14(9): e1006359. https://doi.org/10.1371/journal.pcbi.1006359[3] Morrison A, Diesmann M (2008) Maintaining Causality in Discrete Time Neuronal Network Simulations. Springer Berlin Heidelberg, pp 267-278. https://doi.org/10.1007/978-3-540-73159-7_10 [4] https://nest-simulator.readthedocs.io/enlatest[5] Gewaltig M-O & Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430 |
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| 700 | 1 | _ | |a Diesmann, Markus |0 P:(DE-Juel1)144174 |b 1 |u fzj |
| 700 | 1 | _ | |a Kunkel, Susanne |0 P:(DE-Juel1)195639 |b 2 |u fzj |
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