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Media type:
E-Article
Title:
Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers
Contributor:
Jordan, Jakob
[Author];
Ippen, Tammo
[Author];
Helias, Moritz
[Author];
Kitayama, Itaru
[Author];
Sato, Mitsuhisa
[Author];
Igarashi, Jun
[Author];
Diesmann, Markus
[Author];
Kunkel, Susanne
[Author]
Published:
Frontiers Research Foundation, 2018
Published in:Frontiers in neuroinformatics 12, 2 (2018). doi:10.3389/fninf.2018.00002
Language:
English
DOI:
https://doi.org/10.3389/fninf.2018.00002
ISSN:
1662-5196
Origination:
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.