  Distributed systems are becoming more common place, as computers typically
contain multiple computation processors. The SpiNNaker architecture is such a
distributed architecture, containing millions of cores connected with a unique
communication network, making it one of the largest neuromorphic computing
platforms in the world. Utilising these processors efficiently usually requires
expert knowledge of the architecture to generate executable code. This work
introduces a set of tools (SpiNNTools) that can map computational work
described as a graph in to executable code that runs on this novel machine. The
SpiNNaker architecture is highly scalable which in turn produces unique
challenges in loading data, executing the mapped problem and the retrieval of
data. In this paper we describe these challenges in detail and the solutions
implemented.
