001044145 001__ 1044145
001044145 005__ 20250722202239.0
001044145 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-03047
001044145 037__ $$aFZJ-2025-03047
001044145 041__ $$aEnglish
001044145 1001_ $$0P:(DE-Juel1)191583$$aVillamar, Jose$$b0$$eCorresponding author
001044145 1112_ $$a34th Annual Computational Neuroscience Meeting$$cFlorence$$d2025-07-05 - 2025-07-05$$gCNS$$wItaly
001044145 245__ $$aEfficient simulations of spiking neural networks using NEST GPU
001044145 260__ $$c2025
001044145 3367_ $$2DRIVER$$alecture
001044145 3367_ $$031$$2EndNote$$aGeneric
001044145 3367_ $$2BibTeX$$aMISC
001044145 3367_ $$0PUB:(DE-HGF)17$$2PUB:(DE-HGF)$$aLecture$$blecture$$mlecture$$s1753162551_23346$$xAfter Call
001044145 3367_ $$2ORCID$$aLECTURE_SPEECH
001044145 3367_ $$2DataCite$$aText
001044145 520__ $$aEfficient simulation of large-scale spiking neuronal networks is important for neuroscientific research, and both the simulation speed and the time it takes to instantiate the network in computer memory are key factors. In recent years, hardware acceleration through highly parallel GPUs has become increasingly popular. NEST GPU is a GPU-based simulator under the NEST Initiative written in CUDA-C++ that demonstrates high simulation speeds with models of various network sizes on single-GPU and multi-GPU systems [1,2,3].Using a single NVIDIA RTX4090 GPU we have simulated networks on the magnitude of 80 thousand neurons and 200 million synapses with a real time factor of 0.4; and using 12000 NVIDIA A100 GPUs on the LEONARDO cluster we have managed to simulate networks on the magnitude of 3.3 billion neurons and 37 trillion synapses with a real time factor of 20.In this showcase, we will demonstrate the capabilities of the GPU simulator and present our roadmap to integrate this technology into the ecosystem of the CPU-based simulator NEST [4].For this, we will focus on three aspects of the simulation across model scales, namely network construction speed, state propagation speed, and energy efficiency.Furthermore, we will present our efforts to statistically validate our simulation results against those of NEST (CPU) using established network models.Additionally, we will also present NESTML [5], a domain-specific modeling language to create new neuron models and automatically generate code for the NEST GPU backend.Lastly, the current state of the technology in terms of available features and interfaces will be shown as well as the roadmap for full integration to the NEST ecosystem.[1]	Golosio et al. Front. Comput. Neurosci. 15:627620, 2021.[2]	Tiddia et al. Front. Neuroinform. 16:883333, 2022.[3]	Golosio et al. Appl. Sci. 13, 9598, 2023.[4]	Graber, S., et al. NEST 3.8 (3.8). Zenodo. 10.5281/zenodo.12624784[5]	https://nestml.readthedocs.io/
001044145 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001044145 536__ $$0G:(DE-HGF)POF4-5235$$a5235 - Digitization of Neuroscience and User-Community Building (POF4-523)$$cPOF4-523$$fPOF IV$$x1
001044145 536__ $$0G:(DE-Juel-1)HiRSE-20250220$$aHiRSE - Helmholtz Platform for Research Software Engineering (HiRSE-20250220)$$cHiRSE-20250220$$x2
001044145 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x3
001044145 588__ $$aDataset connected to DataCite
001044145 7001_ $$0P:(DE-HGF)0$$aSergi, Luca$$b1
001044145 7001_ $$0P:(DE-HGF)0$$aTiddia, Gianmarco$$b2
001044145 7001_ $$0P:(DE-Juel1)186954$$aBabu, Pooja$$b3
001044145 7001_ $$aPontisso, Luca$$b4
001044145 7001_ $$aSimula, Francesco$$b5
001044145 7001_ $$aLonardo, Alessandro$$b6
001044145 7001_ $$aPastorelli, Elena$$b7
001044145 7001_ $$aPaolucci, Pier Stanislao$$b8
001044145 7001_ $$aGolosio, Bruno$$b9
001044145 7001_ $$0P:(DE-Juel1)162130$$aSenk, Johanna$$b10
001044145 8564_ $$uhttps://doi.org/10.5281/zenodo.15754814
001044145 8564_ $$uhttps://juser.fz-juelich.de/record/1044145/files/CNS_2025_NEST_GPU_Showcase.pdf$$yOpenAccess
001044145 909CO $$ooai:juser.fz-juelich.de:1044145$$popenaire$$popen_access$$pVDB$$pdriver
001044145 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191583$$aForschungszentrum Jülich$$b0$$kFZJ
001044145 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186954$$aForschungszentrum Jülich$$b3$$kFZJ
001044145 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162130$$aForschungszentrum Jülich$$b10$$kFZJ
001044145 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)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001044145 9131_ $$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
001044145 9141_ $$y2025
001044145 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001044145 920__ $$lyes
001044145 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001044145 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
001044145 980__ $$alecture
001044145 980__ $$aVDB
001044145 980__ $$aUNRESTRICTED
001044145 980__ $$aI:(DE-Juel1)IAS-6-20130828
001044145 980__ $$aI:(DE-Juel1)JSC-20090406
001044145 9801_ $$aFullTexts