001014310 001__ 1014310
001014310 005__ 20240313103117.0
001014310 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03232
001014310 037__ $$aFZJ-2023-03232
001014310 041__ $$aEnglish
001014310 1001_ $$0P:(DE-HGF)0$$aGolosio, Bruno$$b0
001014310 245__ $$aRuntime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
001014310 260__ $$barXiv$$c2023
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001014310 3367_ $$028$$2EndNote$$aElectronic Article
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001014310 3367_ $$2BibTeX$$aARTICLE
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001014310 500__ $$a29 pages, 9 figures. This project was also funded by the Italian PNRR MUR project PE0000013-FAIR, funded by NextGenerationEU.
001014310 520__ $$aSimulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory.On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized.On the software side, code generation approaches ensure highly optimized code, at the expense of repeated code regeneration and recompilation after modifications to the network model.Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules.We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models:a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules.With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies, while still meeting the flexibility demands of explorative network modeling.
001014310 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001014310 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001014310 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001014310 536__ $$0G:(DE-Juel-1)ZT-I-PF-3-026$$aMetaMoSim - Generic metadata management for reproducible high-performance-computing simulation workflows - MetaMoSim (ZT-I-PF-3-026)$$cZT-I-PF-3-026$$x3
001014310 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001014310 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x5
001014310 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x6
001014310 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x7
001014310 588__ $$aDataset connected to arXivarXiv
001014310 7001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b1
001014310 7001_ $$0P:(DE-HGF)0$$aTiddia, Gianmarco$$b2$$eCorresponding author
001014310 7001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b3
001014310 7001_ $$0P:(DE-HGF)0$$aStapmanns, Jonas$$b4
001014310 7001_ $$0P:(DE-HGF)0$$aFanti, Viviana$$b5
001014310 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b6
001014310 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b7
001014310 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b8
001014310 8564_ $$uhttps://juser.fz-juelich.de/record/1014310/files/2306.09855.pdf$$yOpenAccess
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001014310 9141_ $$y2023
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aDipartimento di Fisica, Università di Cagliari, Monserrato, Italy$$b0
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aIstituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Monserrato, Italy$$b0
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001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aDipartimento di Fisica, Università di Cagliari, Monserrato, Italy$$b2
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aIstituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Monserrato, Italy$$b2
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aIstituto Nazionale di Fisica Nucleare, Sezione di Roma, Italy$$b3
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aInstitute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52 428 Jülich, Germany$$b4
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aDipartimento di Fisica, Università di Cagliari, Monserrato, Italy$$b5
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aIstituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Monserrato, Italy$$b5
001014310 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aIstituto Nazionale di Fisica Nucleare, Sezione di Roma, Italy$$b6
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001014310 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5235$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x1
001014310 920__ $$lyes
001014310 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001014310 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001014310 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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