N- Neuron Simulation Using Multiprocessor Cluster

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Addis Ababa University


Clusters built from consumer level multiprocessor computer nodes give a room for a better performance of compute intensive applications in a relatively cheaper cost as compared to dedicated High performance computing facilities. Simulation of biophysical activities of the organic brain using biologically realistic models is one of the areas of compute intensive applications that could be used on this computational platform. In this work, large scale simulation of spiking neural network(SNN) on a cluster of 8 physical cores enabled with hyperthreading (16 logical cores) is presented. The neural network is composed of the biologically plausible and computationally efficient Izhikevich single neuron model. To improve the performance of the simulation and effectively exploit the computational capacity of the cluster, we have used two parallel programming techniques: distributed parallel programming using Message Passing Interface (MPI) library and distributed shard (hybrid) parallel programming using MPI in tandem with Open Multi-Processing (OpenMP) library. Moreover, to harness the combined memory and computation power of the cluster the neurons were distributed across the nodes using static load balancing mechanism. Hence, we were able to simulate up to 160,000 neurons and 3.2M synapses connection per neuron. Performance evaluation for different configuration of the SNN with a purely MPI and Hybrid Parallelization method was presented. Our performance result show that for 160K neurons with 200 synapses connections, using purely MPI parallelization with 16 MPI processes the sequential simulation has improved by 43.12% and using the hybrid parallel programming the sequential simulation has improved by 69.58%. Hence, Comparing the performance results the hybrid parallelization approach demonstrated to be a good programming solution for simulation of SNNs on a cluster of consumer level multiprocessors



Spiking Neural Network, MPI, OpenMP, Hybrid Parallelization