Parallel Execution Strategies for Cellular Automata on Shared Memory Architectures

Cellular Automata (CA) are extensively utilized for modeling and simulating complex systems due to their simplicity, flexibility, and ability to represent diverse phenomena. However, the computational intensity of CA models, particularly in large-scale applications, demands the development of efficient parallel execution techniques. This is especially critical when targeting shared memory architectures, where resource management and thread coordination play pivotal roles in achieving optimal performance. In this study, we examine a range of parallel execution strategies specifically designed to enhance the efficiency and scalability of CA models. The experimental evaluation highlights good scalability in execution time and resource utilization, making these strategies highly suitable for extensive simulations across various domains. Additionally, the results emphasize that imbalance conditions - often arising during CA computations - can considerably influence the effectiveness of choosing a given strategy.