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000173374 037__ $$aFZJ-2014-06784
000173374 041__ $$aEnglish
000173374 1001_ $$0P:(DE-Juel1)159392$$aSchenck, Wolfram$$b0$$eCorresponding Author$$ufzj
000173374 1112_ $$aSupercomputing 2014$$cNew Orleans$$d2014-11-16 - 2014-11-21$$gSC14$$wUSA
000173374 245__ $$aPerformance Model for Large-Scale Neural Simulations with NEST
000173374 260__ $$c2014
000173374 3367_ $$033$$2EndNote$$aConference Paper
000173374 3367_ $$2BibTeX$$aINPROCEEDINGS
000173374 3367_ $$2DRIVER$$aconferenceObject
000173374 3367_ $$2ORCID$$aCONFERENCE_POSTER
000173374 3367_ $$2DataCite$$aOutput Types/Conference Poster
000173374 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1570523876_5983$$xAfter Call
000173374 520__ $$aNEST is a simulator for large networks of spiking point neurons for neuroscience research. A typical NEST simulation consists of two stages: first the network is wired up, and second the dynamics of the network is simulated. Our work aims at developing a performance model for the second stage, the simulation stage, by a semi-empirical approach. We collected measurements of the runtime performance of NEST under varying parameter settings on the JUQUEEN supercomputer at Forschungszentrum Jülich, and subsequently fitted a theoretical model to this data. This performance model defines the simulation time as weighted sum of algorithmic complexities which have been identified in the NEST source code. After parameter fitting, the coefficient of determination on the training data is close to 1.0, and the model can be used to successfully extrapolate NEST simulation times. Furthermore, recommendations for algorithmic improvements of the NEST code can be derived from the modeling results.
000173374 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
000173374 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x1
000173374 536__ $$0G:(DE-HGF)B1175.01.12$$aW2Morrison - W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (B1175.01.12)$$cB1175.01.12$$x2
000173374 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000173374 7001_ $$0P:(DE-Juel1)157723$$aAdinets, Andrey$$b1$$ufzj
000173374 7001_ $$0P:(DE-Juel1)151167$$aZaytsev, Yury$$b2$$ufzj
000173374 7001_ $$0P:(DE-Juel1)144441$$aPleiter, Dirk$$b3$$ufzj
000173374 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b4$$ufzj
000173374 773__ $$y2014
000173374 8564_ $$uhttp://sc14.supercomputing.org/sites/all/themes/sc14/files/archive/tech_poster/tech_poster_pages/post138.html
000173374 909CO $$ooai:juser.fz-juelich.de:173374$$pVDB
000173374 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)159392$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
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000173374 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vComputational Science and Mathematical Methods$$x0
000173374 9132_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x1
000173374 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
000173374 9141_ $$y2014
000173374 920__ $$lyes
000173374 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000173374 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x1
000173374 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000173374 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x3
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