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).
翻译:本文介绍并评价了为有效使用GPU而优化的可自由提供的蜂窝非线性网络模拟器,以实现快速建模和模拟。 它在非线性复杂动态系统中的若干应用中具有相关性,例如缓慢增长现象以及边缘探测等各种图像处理应用中具有相关性。 模拟器设计成一个以Python为作者的Jupyter笔记本笔记本,并进行功能测试和优化,以便在可自由获取的云平台Google协作器上运行。 虽然模拟器的实际形式旨在模拟FitzHugh Nagumo React-Difulation细胞非线性网络,但可以很容易地将其适用于任何其他类型的有限差异时间-度模型。 考虑了四种执行版本,即使用PyCUDA, NUMBA 分别为 CUPY 库(全部三个支持 GPU 计算),以及在没有 GPU 时使用基于 NUMPY 的运行。 四个执行器的特性和性能 。 正在分析四个执行器的特性和性能性细胞网络的实施结论,即PyCUDDDA不至Meal 网络运行14秒。