Programming Data Structures for Large-Scale Desktop Simulations of Complex Systems

Abstract

Studying complex systems requires running largescale simulations over many iterations in time. It is therefore important to provide efficient implementations. The present study borrows philosophical concepts from Gilbert Simondon to identify data structures and algorithms that have the biggest impact on running time and memory usage. These are the entity e-tuple E and the intertwined update function φ. Focusing on implementing data structures in C#, E is implemented as a list of objects according to current software engineering practice and as an array of pointers according to theoretical considerations. Cellular automata simulation with 10 9 entities over one iteration reveal that object-list with dynamic typing and multi-state readiness has a drastic effect on running time and memory usage, especially dynamic as it has a big impact on the evolution time. Pointer-arrays are possible to implement in C# and are more running time and memory efficient as compared to the object-list implementation, however, they are cumbersome to implement. In conclusion, avoiding dynamic typing in object-list based implementations or using pointerarrays gives evolution times that are acceptable in practice, even on desktop computers.