The Kauffman N-K, or random boolean network, model is an important tool for exploring
the properties of large scale complex systems. There are computational challenges in simulating
large networks with high connectivities. We describe some high-performance data structures and
algorithms for implementing large-scale simulations of the random boolean network model using
various storage types provided by the D programming language. We discuss the memory complexity
of an optimised simulation code and present some measured properties of large networks.
Hawick, K.A., James, H.A., Scogings, C.J. (2007), Simulating large random Boolean networks, Research Letters in the Information and Mathematical Sciences, 11, 33-43