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

TL;DR

This paper explores efficient data structures for large-scale complex system simulations, comparing object-lists and pointer-arrays in C# to optimize runtime and memory, guided by philosophical insights.

Contribution

It introduces a novel approach using Gilbert Simondon's concepts to analyze and implement data structures for large-scale simulations, highlighting the benefits of pointer-arrays over object-lists.

Findings

Pointer-arrays outperform object-lists in efficiency.

Avoiding dynamic typing improves performance significantly.

Implementing pointer-arrays in C# is feasible and beneficial.

Abstract

The investigation of complex systems requires running large-scale simulations over many temporal iterations. 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 $\mathcal{E}$ and the intertwined update function $\phi$. Focusing on implementing data structures in C#, $\mathcal{E}$ is implemented as a list of objects according to current software engineering practice and as an array of pointers according to theoretical considerations. Cellular automaton simulations with $10^9$ entities over one iteration reveal that the object-list with dynamic typing and multi-state readiness has a drastic effect on running time and memory usage, especially dynamic typing 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 pointer-arrays gives evolution times that are acceptable in practice, even on desktop computers.