This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.3389/fninf.2018.00002 in citations.
Please use the identifier: http://hdl.handle.net/2128/17462 in citations.
Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers
Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers
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 incom...
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Personal Name(s): | Jordan, Jakob (Corresponding author) |
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Ippen, Tammo / Helias, Moritz / Kitayama, Itaru / Sato, Mitsuhisa / Igarashi, Jun / Diesmann, Markus / Kunkel, Susanne | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 JARA - HPC; JARA-HPC Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 |
Published in: | Frontiers in neuroinformatics, 12 (2018) S. 2 |
Imprint: |
Lausanne
Frontiers Research Foundation
2018
|
DOI: |
10.3389/fninf.2018.00002 |
PubMed ID: |
29503613 |
Document Type: |
Journal Article |
Research Program: |
Brain-Scale Simulations Human Brain Project Specific Grant Agreement 1 The Human Brain Project Supercomputing and Modelling for the Human Brain Theory, modelling and simulation |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/17462 in citations.
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. |