The communication of spike events constitutes a major bottleneck in simulations of brain-scale networks with realistic connectivity, as for example, the multi-area model of macaque visual cortex (Schmidt, 2018). The model consists of 32 cortical microcircuits, each representing a downscaled area of the visual cortex at the resolution of single neurons and synapses with many connections both within and between areas. While synaptic transmission delays between areas are in the range of milliseconds, they can be as short as 0.1 ms within areas. This requires frequent spike communication between compute nodes to maintain causality in the network dynamics (Morrison, 2008), where all compute nodes need to participate in the communication when using a conventional distribution scheme, which evenly divides work among compute nodes by distributing neurons uniformly across nodes regardless of network topology.We target this challenge and propose a structure-aware distribution scheme along with a novel spike-communication framework. The structure-aware scheme places neurons on the hardware in a way that mimics the network’s topology, collocating neurons of the same area on a group of compute nodes. Paired with a communication framework that distinguishes local short delay intra-area communication and global long delay inter-area communication, the structure-aware approach minimizes the costly global communication and thereby reduces simulation time. Our prototype implementation is fully tested and was developed within the neuronal simulator NEST (Gewaltig, 2007).We show that the new strategy significantly reduces communication time in weak-scaling experiments, and the effect increases with an increasing number of compute nodes.