ATM: Area-based Partition and Topology-aware Mapping for Large-scale SNN Simulation
Yangle Zeng, Guangnan Feng, Zhiguang Chen, Yutong Lu, Nong Xiao · 2024
Spiking Neural Network (SNN) is an effective tool for the simulation of neuronal dynamics as well as the understanding of brain structure and functions. However, scaling up SNN for large-scale simulations poses significant computational demands that necessitate the supercomputers. The advent of distributed simulation introduces the requirement of SNN partition and process mapping, which becomes a critical challenge in the context of large-scale distributed SNN simulations. In this paper, we propose an Area-based partition and Topology-aware process Mapping (ATM) strategy to balance the computation workload while coping with the heterogeneity of communication interconnect. We first model the computation workload and communication volume of the SNN simulation according to its biological features. Based on this model, we design an area-based SNN partition strategy to balance the computation workload. Subsequently, we introduce a topology-aware strategy for process mapping, Bottleneck Fulfilling (BF), tailored specifically for collective communication paradigms. Experiments are conducted on an HPC cluster with a multi-area model of the marmoset brain. The results demonstrate that the proposed approach achieves up to 2.2x speedup compared with the baseline on 290 compute nodes.