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@MISC{Villamar:1044145,
      author       = {Villamar, Jose and Sergi, Luca and Tiddia, Gianmarco and
                      Babu, Pooja and Pontisso, Luca and Simula, Francesco and
                      Lonardo, Alessandro and Pastorelli, Elena and Paolucci, Pier
                      Stanislao and Golosio, Bruno and Senk, Johanna},
      title        = {{E}fficient simulations of spiking neural networks using
                      {NEST} {GPU}},
      reportid     = {FZJ-2025-03047},
      year         = {2025},
      abstract     = {Efficient 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/},
      month         = {Jul},
      date          = {2025-07-05},
      organization  = {34th Annual Computational Neuroscience
                       Meeting, Florence (Italy), 5 Jul 2025 -
                       5 Jul 2025},
      subtyp        = {After Call},
      cin          = {IAS-6 / JSC},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)JSC-20090406},
      pnm          = {5232 - Computational Principles (POF4-523) / 5235 -
                      Digitization of Neuroscience and User-Community Building
                      (POF4-523) / HiRSE - Helmholtz Platform for Research
                      Software Engineering (HiRSE-20250220) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5235 /
                      G:(DE-Juel-1)HiRSE-20250220 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)17},
      doi          = {10.34734/FZJ-2025-03047},
      url          = {https://juser.fz-juelich.de/record/1044145},
}