Yavuz Esin, Turner James, Nowotny Thomas
Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ, UK.
Sci Rep. 2016 Jan 7;6:18854. doi: 10.1038/srep18854.
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.
对详细的脑回路模型进行大规模数值模拟，对于确定关于脑功能的假设并检验其一致性和合理性至关重要。然而，模拟现实模型面临的一个持续挑战是计算速度。在本文中，我们介绍了GeNN（GPU增强神经网络）框架，其旨在促进将图形加速器用于大规模神经元网络的计算模型，以应对这一挑战。GeNN是一个开源库，它通过一个灵活且可扩展的接口生成代码，以加速在NVIDIA GPU上执行网络模拟，该接口不需要用户具备深入的技术知识。我们展示的性能基准表明，对于一个由一百万个基于电导的霍奇金-赫胥黎神经元组成的网络，与CPU的单核相比可实现200倍的加速，但对于其他模型，加速倍数可能有所不同。GeNN适用于Linux、Mac OS X和Windows平台。源代码、用户手册、教程、维基、深入的示例项目以及所有其他相关信息均可在项目网站http://genn-team.github.io/genn/上找到。
