This paper introduces and evaluates a freely available cellular nonlinear network simulator optimized for the effective use of GPUs, to achieve fast modelling and simulations. Its relevance is demonstrated for several applications in nonlinear complex dynamical systems, such as slow-growth phenomena as well as for various image processing applications such as edge detection. The simulator is designed as a Jupyter notebook written in Python and functionally tested and optimized to run on the freely available cloud platform Google Collaboratory. Although the simulator, in its actual form, is designed to model the FitzHugh Nagumo Reaction-Diffusion cellular nonlinear network, it can be easily adapted for any other type of finite-difference time-domain model. Four implementation versions are considered, namely using the PyCUDA, NUMBA respectively CUPY libraries (all three supporting GPU computations) as well as a NUMPY-based implementation to be used when GPU is not available. The specificities and performances for each of the four implementations are analyzed concluding that the PyCUDA implementation ensures a very good performance being capable to run up to 14000 Mega cells per seconds (each cell referring to the basic nonlinear dynamic system composing the cellular nonlinear network).

中文翻译：

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用于蜂窝非线性网络和其他时域有限差分系统的快速建模和仿真的Python框架

本文介绍并评估了可免费使用的蜂窝非线性网络模拟器，该模拟器针对GPU的有效使用进行了优化，以实现快速建模和仿真。它的相关性已在非线性复杂动力系统中的多种应用中得到证明，例如缓慢增长现象，以及在各种图像处理应用中（例如边缘检测）。该模拟器设计为用Python编写的Jupyter笔记本，并经过了功能测试和优化，可在可免费使用的云平台Google合作实验室上运行。尽管仿真器以其实际形式被设计为对FitzHugh Nagumo反应扩散细胞非线性网络进行建模，但它可以轻松地适用于任何其他类型的有限差分时域模型。考虑了四个实施版本，即使用PyCUDA，NUMBA分别为CUPY库（全部三个都支持GPU计算），以及在GPU不可用时使用的基于NUMPY的实现。分析了这四个实施方案中每种方案的特性和性能，得出结论，PyCUDA实施方案确保了非常好的性能，能够每秒运行多达14000个兆单元（每个单元均指构成蜂窝非线性网络的基本非线性动态系统）。