Distributed simulation of polychronous and plastic spiking neural networks: strong and weak scaling of a representative mini-application benchmark executed on a small-scale commodity cluster

We introduce a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. It can be used to measure performances of existing computing platforms and to drive the development of future parallel/distributed computing systems dedicated to the simulation of plastic spiking networks. The mini-application is designed to generate identical spiking behaviors and network topologies over a varying number of processing nodes, simplifying the quantitative study of scalability on commodity and custom architectures. Here, we present a first set of strong and weak scaling measures of DPSNN-STDP benchmark (Distributed Simulation of Polychronous Spiking Neural Network with synaptic Spiking Timing Dependent Plasticity). In this first test, we used the benchmark to exercise a small scale cluster of commodity processors (varying the number of used physical cores from 1 to 128). The cluster was interconnected through a commodity network. Bidimensional grids of columns composed of Izhikevich neurons projected synapses locally and toward first, second and third neighboring columns. The size of the simulated network varied from 1.6 Giga synapses down to 200 K synapses. The mini-application has been designed to be easily interfaced with standard and custom software and hardware communication interfaces. It has been designed from its foundation to be natively distributed and parallel, and should not pose major obstacles against distribution and parallelization on several platforms. During 2014, we will further enhance it to enable the description of larger networks, more complex connectomes, and prepare it for distribution to a larger community. The DPSNN-STDP mini-application benchmark is developed in the framework of the EURETILE FET FP7 European project