N- Neuron Simulation Using Multiprocessor Cluster
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Date
2017-10
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Addis Ababa University
Abstract
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
Description
Keywords
Spiking Neural Network, MPI, OpenMP, Hybrid Parallelization