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| Home > Publications database > Scalable Construction of Spiking Neural Networks using up to thousands of GPUs |
| Preprint | FZJ-2026-01020 |
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2025
arXiv
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Please use a persistent id in citations: doi:10.48550/arXiv.2512.09502
Abstract: Diverse scientific and engineering research areas deal with discrete, time-stamped changes in large systems of interacting delay differential equations. Simulating such complex systems at scale on high-performance computing clusters demands efficient management of communication and memory. Inspired by the human cerebral cortex — a sparsely connected network of $\mathcal{O}(10^{10})$ neurons, each forming $\mathcal{O}(10^{3})$--$\mathcal{O}(10^{4})$ synapses and communicating via short electrical pulses called spikes — we study the simulation of large-scale spiking neural networks for computational neuroscience research. This work presents a novel network construction method for multi-GPU clusters and upcoming exascale supercomputers using the Message Passing Interface (MPI), where each process builds its local connectivity and prepares the data structures for efficient spike exchange across the cluster during state propagation. We demonstrate scaling performance of two cortical models using point-to-point and collective communication, respectively.
Keyword(s): Distributed, Parallel, and Cluster Computing (cs.DC) ; Neural and Evolutionary Computing (cs.NE) ; Computational Physics (physics.comp-ph) ; Neurons and Cognition (q-bio.NC) ; FOS: Computer and information sciences ; FOS: Physical sciences ; FOS: Biological sciences
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