基于联合权重超图划分的SNN负载均衡方法

SNN load balancing method based on united weight hypergraph partition

大规模脉冲神经网络并行模拟是探究大脑机能的重要手段。其难点在于合理地将负载映射到并行分布式平台上,提升模拟速度。为解决该问题,提出一种基于联合权重超图划分的SNN负载均衡方法,解决并行计算中进程间计算负载与通信负载的均衡问题,提高SNN模拟速度,并使用稀疏通信的方式替代集体通信,解决事件通信过程中的数据冗余问题,提升通信效率。实验结果表明,该方法使带有STDP突触20%规模的皮质层微电路模型的模拟时间,比标准循环分配算法缩短约64.5%,比普通超图分配算法缩短约57.4%,同时事件通信数据量减少了90%以上。

Large-scale spiking neural network(SNN)parallel simulation is an important means to explore brain function.The difficulty lies in properly mapping the load to the multi-process parallel computing platform and improving the simulation speed.In order to solve this problem,this paper proposed a SNN load balancing method based on united weight hypergraph partitioning,which could solve the problem of balancing computing load and communication load between processes in parallel computing,and improve the speed of SNN simulation.And it used sparse communication instead of collective communication to solve the problem of data redundancy in the process of event communication and improve communication efficiency.The experimental results show that this method makes the simulation time of the cortical microcircuit model with STDP synapse scale of 20%shortened by about 64.5%compared with the standard cyclic allocation algorithm,and about 57.4%compared with the ordinary supermap allocation algorithm.At the same time,the communication time is reduced by more than 90%.