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GeNN: a code generation framework for accelerated brain simulations

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posted on 2023-06-09, 00:07 authored by Esin Yavuz, James Turner, Thomas NowotnyThomas Nowotny
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

Funding

Green brain; G0924; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/J019690/1

History

Publication status

  • Published

File Version

  • Published version

Journal

Scientific Reports

ISSN

2045-2322

Publisher

Nature Publishing Group

Volume

6

Page range

18854

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-01-21

First Open Access (FOA) Date

2016-01-21

First Compliant Deposit (FCD) Date

2016-01-21

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