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| Home > Publications database > Performance Model for Large-Scale Neural Simulations with NEST > print |
| 001 | 173374 | ||
| 005 | 20240313094853.0 | ||
| 037 | _ | _ | |a FZJ-2014-06784 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Schenck, Wolfram |0 P:(DE-Juel1)159392 |b 0 |e Corresponding Author |u fzj |
| 111 | 2 | _ | |a Supercomputing 2014 |g SC14 |c New Orleans |d 2014-11-16 - 2014-11-21 |w USA |
| 245 | _ | _ | |a Performance Model for Large-Scale Neural Simulations with NEST |
| 260 | _ | _ | |c 2014 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1570523876_5983 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a NEST is a simulator for large networks of spiking point neurons for neuroscience research. A typical NEST simulation consists of two stages: first the network is wired up, and second the dynamics of the network is simulated. Our work aims at developing a performance model for the second stage, the simulation stage, by a semi-empirical approach. We collected measurements of the runtime performance of NEST under varying parameter settings on the JUQUEEN supercomputer at Forschungszentrum Jülich, and subsequently fitted a theoretical model to this data. This performance model defines the simulation time as weighted sum of algorithmic complexities which have been identified in the NEST source code. After parameter fitting, the coefficient of determination on the training data is close to 1.0, and the model can be used to successfully extrapolate NEST simulation times. Furthermore, recommendations for algorithmic improvements of the NEST code can be derived from the modeling results. |
| 536 | _ | _ | |a 411 - Computational Science and Mathematical Methods (POF2-411) |0 G:(DE-HGF)POF2-411 |c POF2-411 |f POF II |x 0 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 1 |
| 536 | _ | _ | |a W2Morrison - W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (B1175.01.12) |0 G:(DE-HGF)B1175.01.12 |c B1175.01.12 |x 2 |
| 536 | _ | _ | |a SLNS - SimLab Neuroscience (Helmholtz-SLNS) |0 G:(DE-Juel1)Helmholtz-SLNS |c Helmholtz-SLNS |x 3 |
| 700 | 1 | _ | |a Adinets, Andrey |0 P:(DE-Juel1)157723 |b 1 |u fzj |
| 700 | 1 | _ | |a Zaytsev, Yury |0 P:(DE-Juel1)151167 |b 2 |u fzj |
| 700 | 1 | _ | |a Pleiter, Dirk |0 P:(DE-Juel1)144441 |b 3 |u fzj |
| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 4 |u fzj |
| 773 | _ | _ | |y 2014 |
| 856 | 4 | _ | |u http://sc14.supercomputing.org/sites/all/themes/sc14/files/archive/tech_poster/tech_poster_pages/post138.html |
| 909 | C | O | |p VDB |o oai:juser.fz-juelich.de:173374 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)159392 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)157723 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)151167 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)144441 |
| 910 | 1 | _ | |a Forschungszentrum Jülich GmbH |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)151166 |
| 913 | 2 | _ | |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |
| 913 | 2 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |2 G:(DE-HGF)POF3-500 |v Theory, modelling and simulation |x 1 |
| 913 | 1 | _ | |a DE-HGF |b Schlüsseltechnologien |l Supercomputing |1 G:(DE-HGF)POF2-410 |0 G:(DE-HGF)POF2-411 |2 G:(DE-HGF)POF2-400 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF2 |
| 914 | 1 | _ | |y 2014 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-82)080012_20140620 |k JARA-HPC |l JARA - HPC |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 2 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 3 |
| 980 | _ | _ | |a poster |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-82)080012_20140620 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 981 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
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