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GeNN: a code generation framework for accelerated brain simulations
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 (GP...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703976/ https://www.ncbi.nlm.nih.gov/pubmed/26740369 http://dx.doi.org/10.1038/srep18854 |
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author | Yavuz, Esin Turner, James Nowotny, Thomas |
author_facet | Yavuz, Esin Turner, James Nowotny, Thomas |
author_sort | Yavuz, Esin |
collection | PubMed |
description | 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/. |
format | Online Article Text |
id | pubmed-4703976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47039762016-01-19 GeNN: a code generation framework for accelerated brain simulations Yavuz, Esin Turner, James Nowotny, Thomas Sci Rep Article 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/. Nature Publishing Group 2016-01-07 /pmc/articles/PMC4703976/ /pubmed/26740369 http://dx.doi.org/10.1038/srep18854 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Yavuz, Esin Turner, James Nowotny, Thomas GeNN: a code generation framework for accelerated brain simulations |
title | GeNN: a code generation framework for accelerated brain simulations |
title_full | GeNN: a code generation framework for accelerated brain simulations |
title_fullStr | GeNN: a code generation framework for accelerated brain simulations |
title_full_unstemmed | GeNN: a code generation framework for accelerated brain simulations |
title_short | GeNN: a code generation framework for accelerated brain simulations |
title_sort | genn: a code generation framework for accelerated brain simulations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703976/ https://www.ncbi.nlm.nih.gov/pubmed/26740369 http://dx.doi.org/10.1038/srep18854 |
work_keys_str_mv | AT yavuzesin gennacodegenerationframeworkforacceleratedbrainsimulations AT turnerjames gennacodegenerationframeworkforacceleratedbrainsimulations AT nowotnythomas gennacodegenerationframeworkforacceleratedbrainsimulations |