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| Home > Publications database > Integrating brain structure and dynamics on supercomputers |
| Contribution to a conference proceedings/Contribution to a book | FZJ-2014-04318 |
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2014
Springer
Cham Heidelberg New York Dordrecht London
ISBN: 978-3-319-12083-6 (print), 978-3-319-12084-3 (electronic)
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Please use a persistent id in citations: doi:10.1007/978-3-319-12084-3_3
Abstract: Large-scale simulations of neuronal networks provide a unique view onto brain dynamics, complementing experiments, small-scale simulations, and theory. They enable the investigation of integrative models to arrive at a multi-scale picture of brain dynamics relating macroscopic imaging measures to the microscopic dynamics. Recent years have seen rapid development of the necessary simulation technology. We give an overview of design features of the NEural Simulation Tool (NEST) that enable simulations of spiking point neurons to be scaled to hundreds of thousands of processors. The performance of supercomputing applications is traditionally assessed using scalability plots. We discuss reasons why such measures should be interpreted with care in the context of neural network simulations. The scalability of neural network simulations on available supercomputers is limited by memory constraints rather than computational speed. This calls for future generations of supercomputers that are more attuned to the requirements of memory-intensive neuroscientific applications.
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