Florimbi Giordana, Torti Emanuele, Masoli Stefano, D'Angelo Egidio, Leporati Francesco
Custom Computing and Programmable Systems Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Neurocomputational Laboratory, Neurophysiology Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
Front Comput Neurosci. 2021 Mar 16;15:630795. doi: 10.3389/fncom.2021.630795. eCollection 2021.
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 μm 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.
在现代计算建模中，神经科学家需要重现大规模网络的持久活动，其中神经元由高度复杂的数学模型描述。这些方面极大地增加了模拟的计算负荷，而利用并行系统来减少处理时间可以有效地执行这些模拟。图形处理单元（GPU）设备通过在桌面提供高性能计算来满足这一需求。在这项工作中，作者描述了一种在多GPU系统上实现的新型颗粒层模拟器开发，该系统能够在三维空间中重建小脑颗粒层并重现其神经元活动。考虑到在三维空间中定向的轴突/树突场几何形状，并遵循文献中提供的收敛/发散率，这种重建具有高度的新颖性和逼真性。神经元使用霍奇金和赫胥黎模型进行建模。该网络通过重现文献中充分记录的典型行为（如中心-环绕组织）进行验证。对于一个体积为600×150×1200μm、包含432,000个颗粒、972个高尔基细胞、32,399个肾小球和4,051个苔藓纤维的网络，在英特尔i9处理器上进行重建需要235秒。利用单GPU和多GPU桌面系统（分别配备一个或两个NVIDIA RTX 2080 GPU）重现10秒的活动分别仅需4.34小时和3.37小时。此外，如果在一个或两个NVIDIA V100 GPU上运行，代码分别仅需3.52小时和2.44小时。实现的相关加速比（单GPU版本高达约38倍，多GPU版本高达约55倍）清楚地表明，GPU技术非常适合逼真的大型网络模拟。
