The human brain is a complex biological neural network characterised by high degrees of connectivity among neurons. Any system designed to simulate large-scale spiking neuronal networks needs to support such connectivity and the associated communication traffic in the form of spike events. This paper investigates how best to generate multicast routes for SpiNNaker, a purpose-built, low-power, massively-parallel architecture. The discussed algorithms are an essential ingredient for the efficient operation of SpiNNaker since generating multicast routes is known to be an NP-complete problem. In fact, multicast communications have been extensively studied in the literature, but we found no existing algorithm adaptable to SpiNNaker. The proposed algorithms exploit the regularity of the two-dimensional triangular torus topology and the availability of selective multicast at hardware level. A comprehensive study of the parameters of the algorithms and their effectiveness is carried out in this paper considering different destination distributions ranging from worst-case to a real neural application. The results show that two novel proposed algorithms can reduce significantly the pressure exerted onto the interconnection infrastructure while remaining effective to be used in a production environment. 
SpiNNaker: Enhanced multicast routing