In modern computational modeling, neuroscientists need to reproduce long-lasting activity of large-scale networks, where neurons are described by highly complex mathematical models. These aspects strongly increase the computational load of the simulations, which can be efficiently performed by exploiting parallel systems to reduce the processing times. Graphics Processing Unit (GPU) devices meet this need providing on desktop High Performance Computing. In this work, authors describe a novel Granular layEr Simulator development implemented on a multi-GPU system capable of reconstructing the cerebellar granular layer in a 3D space and reproducing its neuronal activity. The reconstruction is characterized by a high level of novelty and realism considering axonal/dendritic field geometries, oriented in the 3D space, and following convergence/divergence rates provided in literature. Neurons are modeled using Hodgkin and Huxley representations. The network is validated by reproducing typical behaviors which are well-documented in the literature, such as the center-surround organization. The reconstruction of a network, whose volume is 600 × 150 × 1,200 μm³ with 432,000 granules, 972 Golgi cells, 32,399 glomeruli, and 4,051 mossy fibers, takes 235 s on an Intel i9 processor. The 10 s activity reproduction takes only 4.34 and 3.37 h exploiting a single and multi-GPU desktop system (with one or two NVIDIA RTX 2080 GPU, respectively). Moreover, the code takes only 3.52 and 2.44 h if run on one or two NVIDIA V100 GPU, respectively. The relevant speedups reached (up to ~38× in the single-GPU version, and ~55× in the multi-GPU) clearly demonstrate that the GPU technology is highly suitable for realistic large network simulations.

在现代计算模型中，神经科学家需要重现大型网络的持久活动，在大型网络中，神经元是由高度复杂的数学模型来描述的。这些方面极大地增加了仿真的计算负荷，这可以通过利用并行系统来减少处理时间来有效地执行。图形处理单元（GPU）设备可在台式机高性能计算上满足此需求。在这项工作中，作者描述了在Multi-GPU系统上实施的新型Granular LayEr Simulator开发，该系统能够在3D空间中重建小脑颗粒层并重现其神经元活动。考虑到在3D空间中定向的轴突/树突场几何形状，重建的特点是新颖性和逼真度高，并遵循文献中提供的收敛/分歧率。使用Hodgkin和Huxley表示法对神经元进行建模。通过重现文献中详细记录的典型行为（例如中心周围组织）来验证网络。体积为600×150×1,200μm的网络的重建^3个拥有432,000个颗粒，972个高尔基细胞，32,399个肾小球和4,051个苔藓纤维，在Intel i9处理器上耗时235 s。利用单GPU和多GPU桌面系统（分别具有一个或两个NVIDIA RTX 2080 GPU），10秒钟的活动再现仅需要4.34和3.37 h。此外，如果分别在一个或两个NVIDIA V100 GPU上运行，该代码仅需3.52和2.44小时。达到的相关加速比（在单GPU版本中达到了约38倍，在多GPU版本中达到了约55倍）清楚地证明了GPU技术非常适合现实的大型网络仿真。