在脉冲神经网络的大规模脑仿真中利用网络拓扑
Exploiting network topology in brain-scale simulations of spiking neural networks
AI总结：
针对分布式大规模脉冲神经网络仿真中通信瓶颈问题，提出基于脑区结构感知的局部-全局混合通信架构，显著提升性能并降低能耗。
摘要：
传统超级计算机的仿真代码为神经形态计算系统提供了参考。当前分布式大规模脉冲神经元网络仿真的瓶颈是计算节点间的通信。通信速度似乎受限于节点间的互连和协调数据传输的软件库。然而，性能分析显示，计算节点在通信调用之间所需时间的变异性很大。瓶颈实际上是等待最慢节点的时间。一个统计模型基于通信调用之间计算时间的分布解释了总仿真时间。一个根本的解决方法是避免通信调用，因为这需要更少的同步并减少跨计算节点计算时间的变异性。哺乳动物大脑的区域组织本身就适合这种优化策略。区域内神经元之间的连接延迟短，但跨区域的长程连接延迟长一个数量级。这提示了一种结构感知的区域到计算节点的映射，允许将节点模拟特定区域的更频繁通信与较不频繁的全局通信进行分区。我们在一个实际例子中展示了显著的性能提升。本工作提出了一种用于大规模神经元网络仿真的局部-全局混合通信架构，作为将大脑结构映射到超级计算机结构的第一步。它挑战了长期以来认为仿真瓶颈是标准通信库集体调用中固有的同步的信念。我们为在传统计算系统上高效节能地模拟神经元网络提供了指导，并提高了神经形态系统的标准。
英文摘要：
Simulation code for conventional supercomputers serves as a reference for neuromorphic computing systems. The present bottleneck of distributed large-scale spiking neuronal network simulations is the communication between compute nodes. Communication speed seems limited by the interconnect between the nodes and the software library orchestrating the data transfer. Profiling reveals, however, that the variability of the time required by the compute nodes between communication calls is large. The bottleneck is in fact the waiting time for the slowest node. A statistical model explains total simulation time on the basis of the distribution of computation times between communication calls. A fundamental cure is to avoid communication calls because this requires fewer synchronizations and reduces the variability of computation times across compute nodes. The organization of the mammalian brain into areas lends itself to such an optimization strategy. Connections between neurons within an area have short delays, but the delays of the long-range connections across areas are an order of magnitude longer. This suggests a structure-aware mapping of areas to compute nodes allowing for a partition into more frequent communication between nodes simulating a particular area and less frequent global communication. We demonstrate a substantial performance gain on a real-world example. This work proposes a local-global hybrid communication architecture for large-scale neuronal network simulations as a first step in mapping the structure of the brain to the structure of a supercomputer. It challenges the long-standing belief that the bottleneck of simulation is synchronization inherent in the collective calls of standard communication libraries. We provide guidelines for the energy efficient simulation of neuronal networks on conventional computing systems and raise the bar for neuromorphic systems.
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