Agent based modelling (ABM) offers a powerful abstraction for scientific study in a broad range of domains. The use of agent based simulators encourages good software engineering design such as separation of concerns, that is, the uncoupling of the model description from its implementation detail. A major limitation in current approaches to ABM simulation is that of the trade off between simulator flexibility and performance. It is common that highly optimised simulations, such as those which target graphics processing units (GPU) hardware, are implemented as standalone software. This work presents a software framework (FLAME GPU 2) which balances flexibility with performance for general purpose ABM. Methods for ensuring high computational efficacy are demonstrated by, minimising data movement, and ensuring high device utilisation by exploiting opportunities for concurrent code execution within a model and through the use of ensembles of simulations. A novel hierarchical sub‐modelling approach is also presented which can be used to model certain types of recursive behaviours. This feature is shown to be essential in providing a mechanism to resolve competition for resources between agents within a parallel environment which would otherwise introduce race conditions. To understand the performance characteristics of the software, a benchmark model with millions of agents is used to explore the use of simulation ensembles and to parametrically investigate concurrent code execution within a model. Performance speedups are demonstrated of 3.5 and 10 respectively over a baseline GPU implementation. Our hierarchical sub‐modelling approach is used to demonstrate the implementation of a recursive algorithm to resolve competition of agent movement which occurs as a result of agent desire to simultaneously occupy discrete areas high in a ‘resource’. The algorithm is used to implement a classical socio‐economics model, Sugarscape, with populations of up to 16M agents.

Background: In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. Results: This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. Conclusions: Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.

Agent-based modelling (ABM) has revolutionised the simulation of complex systems, finding applications in diverse fields such as economic markets and traffic management. By modelling individuals as autonomous agents within a dynamic environment, ABM enables the exploration of system behaviours and the evaluation of interventions at various spatiotemporal resolutions. However, the computational intensity of ABM, particularly in large-scale simulations, remains a significant hurdle. This paper presents a novel approach to addressing these challenges through the development of a GPU-accelerated transport model, specifically applied to a road network. Utilising the FLAME-GPU framework, the proposed model demonstrates enhanced scalability and efficiency compared with traditional CPU-based simulations, such as Simulation of Urban MObility (SUMO). Through rigorous comparative analysis, this study highlights significant improvements in simulation speed and the capacity to manage larger vehicle populations. The research underscores the transformative potential of GPU acceleration in mitigating computational constraints within ABM, offering a practical framework for simulating transport systems with greater precision and depth. Extensive experimentation validates the model’s ability to realistically simulate the vehicle population of the Isle of Wight, achieving a balance between computational efficiency and the accurate representation of complex traffic dynamics.