Scalable Construction of Spiking Neural Networks using up to thousands of GPUs
For computational neuroscientists, this work addresses the scalability bottleneck of simulating large spiking neural networks on exascale supercomputers.
The paper presents a scalable method for constructing spiking neural networks on multi-GPU clusters using MPI, enabling efficient simulation of large-scale cortical models. Scaling performance is demonstrated on up to thousands of GPUs with two communication strategies.
Diverse scientific and engineering research areas deal with discrete, time-stamped changes in large systems of interacting delay differential equations. Simulating such complex systems at scale on high-performance computing clusters demands efficient management of communication and memory. Inspired by the human cerebral cortex -- a sparsely connected network of $\mathcal{O}(10^{10})$ neurons, each forming $\mathcal{O}(10^{3})$--$\mathcal{O}(10^{4})$ synapses and communicating via short electrical pulses called spikes -- we study the simulation of large-scale spiking neural networks for computational neuroscience research. This work presents a novel network construction method for multi-GPU clusters and upcoming exascale supercomputers using the Message Passing Interface (MPI), where each process builds its local connectivity and prepares the data structures for efficient spike exchange across the cluster during state propagation. We demonstrate scaling performance of two cortical models using point-to-point and collective communication, respectively.