发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大规模SNN仿真中的同步开销与通信瓶颈，提出乐观异步通信模型OACM，结合混合仿真、单向通信与自适应路由，在174节点上较CORTEX加速1.4倍、较NEST加速22.6倍。
AI 中文摘要
脉冲神经网络（SNN）仿真作为理解大脑动力学和推进神经形态计算的关键工具，在大规模分布式实现中面临显著的可扩展性挑战。主要瓶颈源于时间驱动模拟器中频繁的同步开销，以及乐观PDES方法中大量的二次回滚，加之低效的通信模式未能充分利用网络带宽。在本文中，我们提出OACM，一种乐观异步通信模型，通过三项关键创新解决这些挑战。首先，我们设计了乐观混合SNN仿真，结合了时间驱动方法的实现简单性与通过同步窗口内策略性回滚机制降低同步频率的优势。其次，我们利用UNR库实现异步单向通信，消除了握手延迟，实现了计算与通信的完全重叠。第三，我们在2D-HyperX虚拟拓扑内开发了自适应消息聚合和路由策略，以优化小消息流量下的带宽利用。在高性能计算集群上的实验评估表明，在模拟多区域狨猴大脑模型、规模高达174个计算节点时，OACM相比原始CORTEX模拟器实现了最高1.4倍的加速，相比NEST实现了超过22.6倍的加速。
英文摘要
Spiking Neural Network (SNN) simulation serves as a crucial tool for understanding brain dynamics and advancing neuromorphic computing, but faces significant scalability challenges in large-scale distributed implementations. The primary bottlenecks arise from frequent synchronization overhead in time-driven simulators and extensive secondary rollbacks in optimistic PDES approaches, compounded by inefficient communication patterns that underutilize network bandwidth. In this paper, we propose OACM, an Optimistic Asynchronous Communication Model that addresses these challenges through three key innovations. First, we design Optimistic Hybrid SNN Simulation that combines the implementation simplicity of time-driven approaches with reduced synchronization frequency through strategic rollback mechanisms within synchronization windows. Second, we implement asynchronous one-sided communication using the UNR library, eliminating handshake latency and achieving complete computation-communication overlap. Third, we develop adaptive message aggregation and routing strategies within a 2D-HyperX virtual topology to optimize bandwidth utilization for small message traffic. Experimental evaluation on a high-performance computing cluster demonstrates that OACM achieves up to 1.4x speedup over the original CORTEX simulator and over 22.6x speedup compared to NEST when simulating a multi-area marmoset brain model at scales of up to 174 compute nodes.
