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| Poster (After Call) | FZJ-2024-03723 |
; ;
2024
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Please use a persistent id in citations: doi:10.34734/FZJ-2024-03723
Abstract: With the field of dedicated hardware for neuronal simulations growing rapidly over the past years, conventional hardware still serves as an important reference benchmark while maintaining more flexibility at potentially lower cost. Kurth et al. [1] have shown that for a realistic cortical microcircuit model neuronal simulation technology for conventional hardware keeps pace with novel computing architectures, such as SpiNNaker [2], regarding real-time factor as well as energy efficiency. In this project we develop and advance simulation technology for spiking neural networks for conventional computer architectures, thereby challenging and inspiring novel neuromorphic systems. 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 [3]. 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. 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 [4]. This poses a challenge to the conventional round-robin scheme used to distribute neurons uniformly across compute nodes disregarding the network’s specific topology.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. Paired with a communication framework that distinguishes local short delay intra-area communication and global long delay inter-area communication, the structure-aware approach minimizes the costly global communication and thereby reduces simulation time. Our prototype implementation is fully tested and was developed within the neuronal simulator tool NEST [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. We show that the new strategy significantly reduces communication time in weak-scaling experiments and the effect increases with an increasing number of compute nodes.[1] Kurth et al., Neuromorph. Comput. Eng., 2, 021001, 2022[2] Furber & Bogdan, Boston-Delft: now publishers, 2020[3] Schmidt et al., PLoS Comput Biol, 14(10), 1-38, 2018[4] Morrison & Diesmann, Springer Berlin Heidelberg, pp 267-278, 2008[5] Gewaltig & Diesmann, Scholarpedia 2(4):1430 , 2007
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