  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 spiking
behaviors and synaptic connectivity that do not change when the number of
hardware processing nodes is varied, simplifying the quantitative study of
scalability on commodity and custom architectures. Here, we present the strong
and weak scaling and the profiling of the computational/communication
components of the DPSNN-STDP benchmark (Distributed Simulation of Polychronous
Spiking Neural Network with synaptic Spike-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 6.6 Giga synapses down to 200 K synapses. The code demonstrated to
be fast and scalable: 10 wall clock seconds were required to simulate one
second of activity and plasticity (per Hertz of average firing rate) of a
network composed by 3.2 G synapses running on 128 hardware cores clocked @ 2.4
GHz. 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.
