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001014311 0247_ $$2doi$$a10.3390/app13179598
001014311 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-03233
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001014311 1001_ $$0P:(DE-HGF)0$$aGolosio, Bruno$$b0
001014311 245__ $$aRuntime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
001014311 260__ $$aBasel$$bMDPI$$c2023
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001014311 500__ $$aThis project was also funded by the Italian PNRR MUR project PE0000013-FAIR, funded by NextGenerationEU.
001014311 520__ $$aSimulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies while still meeting the flexibility demands of explorative network modeling.
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001014311 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001014311 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
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001014311 536__ $$0G:(DE-Juel1)jinb33_20220812$$aBrain-Scale Simulations (jinb33_20220812)$$cjinb33_20220812$$fBrain-Scale Simulations$$x5
001014311 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x6
001014311 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x7
001014311 7001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b1
001014311 7001_ $$0P:(DE-HGF)0$$aTiddia, Gianmarco$$b2$$eCorresponding author
001014311 7001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b3
001014311 7001_ $$0P:(DE-HGF)0$$aStapmanns, Jonas$$b4
001014311 7001_ $$0P:(DE-HGF)0$$aFanti, Viviana$$b5
001014311 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b6
001014311 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b7
001014311 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b8
001014311 773__ $$0PERI:(DE-600)2704225-X$$a10.3390/app13179598$$n17$$p9598, 29 pages$$tApplied Sciences$$v13$$x2076-3417$$y2023
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001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Physics, University of Cagliari, 09042 Monserrato, Italy$$b0
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001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Physics, University of Cagliari, 09042 Monserrato, Italy$$b2
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, 09042 Monserrato, Italy$$b2
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Istituto Nazionale di Fisica Nucleare, Sezione di Roma, 00185 Roma, Italy$$b3
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Institute 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$$b4
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department of Physics, University of Cagliari, 09042 Monserrato, Italy$$b5
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, 09042 Monserrato, Italy$$b5
001014311 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Istituto Nazionale di Fisica Nucleare, Sezione di Roma, 00185 Roma, Italy$$b6
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