Mitigating critical nodes in brain simulations via edge removal

Abstract

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.