  Spiking neural networks (SNNs) are a promising candidate for
biologically-inspired and energy efficient computation. However, their
simulation is notoriously time consuming, and may be seen as a bottleneck in
developing competitive training methods with potential deployment on
neuromorphic hardware platforms. To address this issue, we provide an
implementation of mini-batch processing applied to clock-based SNN simulation,
leading to drastically increased data throughput. To our knowledge, this is the
first general-purpose implementation of mini-batch processing in a spiking
neural networks simulator, which works with arbitrary neuron and synapse
models. We demonstrate nearly constant-time scaling with batch size on a
simulation setup (up to GPU memory limits), and showcase the effectiveness of
large batch sizes in two SNN application domains, resulting in $\approx$880X
and $\approx$24X reductions in wall-clock time respectively. Different
parameter reduction techniques are shown to produce different learning outcomes
in a simulation of networks trained with spike-timing-dependent plasticity.
Machine learning practitioners and biological modelers alike may benefit from
the drastically reduced simulation time and increased iteration speed this
method enables. Code to reproduce the benchmarks and experimental findings in
this paper can be found at https://github.com/djsaunde/snn-minibatch.
