Hines M L, Carnevale N T
Department of Computer Science, Yale University, New Haven, CT, USA.
J Neurosci Methods. 2008 Apr 30;169(2):425-55. doi: 10.1016/j.jneumeth.2007.09.010. Epub 2007 Sep 16.
The increasing complexity of network models poses a growing computational burden. At the same time, computational neuroscientists are finding it easier to access parallel hardware, such as multiprocessor personal computers, workstation clusters, and massively parallel supercomputers. The practical question is how to move a working network model from a single processor to parallel hardware. Here we show how to make this transition for models implemented with NEURON, in such a way that the final result will run and produce numerically identical results on either serial or parallel hardware. This allows users to develop and debug models on readily available local resources, then run their code without modification on a parallel supercomputer.
网络模型日益增加的复杂性带来了越来越大的计算负担。与此同时，计算神经科学家发现更容易访问并行硬件，如多处理器个人计算机、工作站集群和大规模并行超级计算机。实际问题是如何将一个运行的网络模型从单处理器转移到并行硬件上。在这里，我们展示了如何对用NEURON实现的模型进行这种转换，使得最终结果在串行或并行硬件上运行时都能产生数值上相同的结果。这允许用户在现成的本地资源上开发和调试模型，然后无需修改代码就在并行超级计算机上运行他们的代码。
