Spiking neural network simulation speeds up brain modeling over 20 times
OACM: Optimistic Asynchronous Communication Model for Large-Scale SNN Simulation
Distributed, Parallel, and Cluster Computing
Summary
Simulating how brain cells communicate is important but can be very slow and hard to do on large computers. The authors propose a new way called OACM that mixes different simulation styles to reduce waiting times and uses smarter communication techniques to fully use network capacity. Their method runs simulations much faster, especially for large models like a marmoset brain, using many computers working together efficiently. This could help scientists run bigger brain experiments on supercomputers more quickly.
What this means in practice
- •For neuromorphic hardware engineers: Improve large-scale brain simulations for designing and testing neuromorphic chips by using OACM to reduce simulation time and communication bottlenecks.
- •For high-performance computing teams: Optimize distributed simulations of complex neural networks by adopting OACM’s asynchronous messaging and rollback approach to better use computing and network resources.
Authors
Guangnan Feng, Yangle Zeng, Honghui Liang, Zhiguang Chen, Yutong Lu
Abstract
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.