  With the increasing development of neuromorphic platforms and their related
software tools as well as the increasing scale of spiking neural network (SNN)
models, there is a pressure for interoperable and scalable representations of
network state. In response to this, we discuss a parallel extension of a widely
used format for efficiently representing sparse matrices, the compressed sparse
row (CSR), in the context of supporting the simulation and serialization of
large-scale SNNs. Sparse matrices for graph adjacency structure provide a
natural fit for describing the connectivity of an SNN, and prior work in the
area of parallel graph partitioning has developed the distributed CSR (dCSR)
format for storing and ingesting large graphs. We contend that organizing
additional network information, such as neuron and synapse state, in alignment
with its adjacency as dCSR provides a straightforward partition-based
distribution of network state. For large-scale simulations, this means each
parallel process is only responsible for its own partition of state, which
becomes especially useful when the size of an SNN exceeds the memory resources
of a single compute node. For potentially long-running simulations, this also
enables network serialization to and from disk (e.g. for checkpoint/restart
fault-tolerant computing) to be performed largely independently between
parallel processes. We also provide a potential implementation, and put it
forward for adoption within the neural computing community.
