计算机工程2025，Vol.51Issue(5)：219-228,10.DOI:10.19678/j.issn.1000-3428.0069271
基于通信和拓扑感知的SNN分区与映射算法
Communication and Topology-Aware Partitioning and Mapping Algorithm for SNN
摘要
Abstract
Spiking Neural Network(SNN)has become increasingly important for studying and simulating the functions of various brain regions and their interconnections.Parallel-distributed computing has become an inevitable choice for SNN simulations of larger-scale brain regions.However,as the scale of computation increases,SNN simulation performance is affected primarily by load imbalances among computing nodes and communication issues.For distributed computing platforms,existing partitioning algorithms cannot find a globally optimal partition or effectively map workloads to computing cores.Therefore,this study proposes a communication and topology-aware partitioning and mapping algorithm that includes two core steps:partitioning and topology-aware mapping.Introducing a partitioning method that is aware of SNN connections improves the computational efficiency and reduces communication latency.In the topology-aware mapping method,the communication topology graph and underlying network information are utilized to efficiently allocate workloads to computing nodes and minimize the communication costs across different computing cores.Experimental results show that,when simulating SNN benchmark datasets with 96 processes on the parallel computing platform of the National Supercomputing Center in Jinan,the proposed method achieves better load balancing and communication performance than existing state-of-the-art partitioning frameworks.The synchronization and communication times are reduced by 40％and 7.1％,respectively,and the total simulation time is shortened by 30％.关键词
脉冲神经网络/分布式计算/负载均衡/超图分区/拓扑感知映射Key words
Spiking Neural Network(SNN)/distributed computing/load balancing/hypergraph partitioning/topology-aware mapping分类
信息技术与安全科学引用本文复制引用
黄尧,柴志雷..基于通信和拓扑感知的SNN分区与映射算法[J].计算机工程,2025,51(5):219-228,10.基金项目
国家自然科学基金(61972180). （61972180）
