000834370 001__ 834370
000834370 005__ 20220930130125.0
000834370 0247_ $$2doi$$a10.3389/fninf.2017.00040
000834370 0247_ $$2Handle$$a2128/14769
000834370 0247_ $$2WOS$$aWOS:000406560700001
000834370 0247_ $$2altmetric$$aaltmetric:21546644
000834370 0247_ $$2pmid$$apmid:28701946
000834370 037__ $$aFZJ-2017-04342
000834370 082__ $$a610
000834370 1001_ $$0P:(DE-Juel1)151364$$aKunkel, Susanne$$b0$$eCorresponding author
000834370 245__ $$aThe NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code
000834370 260__ $$aLausanne$$bFrontiers Research Foundation$$c2017
000834370 3367_ $$2DRIVER$$aarticle
000834370 3367_ $$2DataCite$$aOutput Types/Journal article
000834370 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1570522936_2022
000834370 3367_ $$2BibTeX$$aARTICLE
000834370 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000834370 3367_ $$00$$2EndNote$$aJournal Article
000834370 520__ $$aNEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.
000834370 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000834370 536__ $$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
000834370 536__ $$0G:(DE-Juel1)jinb33_20121101$$aBrain-Scale Simulations (jinb33_20121101)$$cjinb33_20121101$$fBrain-Scale Simulations$$x2
000834370 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000834370 588__ $$aDataset connected to CrossRef
000834370 7001_ $$0P:(DE-Juel1)159392$$aSchenck, Wolfram$$b1$$eCorresponding author
000834370 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2017.00040$$gVol. 11, p. 40$$p40$$tFrontiers in neuroinformatics$$v11$$x1662-5196$$y2017
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.pdf$$yOpenAccess
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.gif?subformat=icon$$xicon$$yOpenAccess
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
000834370 8564_ $$uhttps://juser.fz-juelich.de/record/834370/files/fninf-11-00040.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000834370 8767_ $$82016-0073112-6$$92017-06-07$$d2017-07-03$$eAPC$$jDeposit$$lDeposit: Frontiers
000834370 909CO $$ooai:juser.fz-juelich.de:834370$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000834370 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151364$$aForschungszentrum Jülich$$b0$$kFZJ
000834370 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)159392$$aForschungszentrum Jülich$$b1$$kFZJ
000834370 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000834370 9141_ $$y2017
000834370 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000834370 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000834370 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000834370 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT NEUROINFORM : 2015
000834370 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000834370 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000834370 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000834370 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000834370 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000834370 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000834370 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000834370 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000834370 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000834370 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x1
000834370 980__ $$ajournal
000834370 980__ $$aVDB
000834370 980__ $$aI:(DE-Juel1)JSC-20090406
000834370 980__ $$aI:(DE-82)080012_20140620
000834370 980__ $$aAPC
000834370 980__ $$aUNRESTRICTED
000834370 9801_ $$aAPC
000834370 9801_ $$aFullTexts