Modelling and simulation of complex problems has become an established ‘third pillar’ of science, complementary to theory and experimentation.
The multi-agent approach to modelling allows complex systems to be constructed in such as way as to add complexity from understanding at an individual level (i.e. a bottom-up approach). This approach is extremely powerful in a wide range of domains as diverse as computational biology to economics and physics. Whilst multi-agent modelling provides a natural and intuitive method to model systems the computational cost of performing large simulations is much greater than for top-down, system level alternatives.
In order for multi-agent modelling and simulation to be used as a tool for delivering excellent science, it is vital that simulation performance can scale, by targeting readily available computational resources effectively. Developed in UK since 2008, FLAME GPU provides this computational capacity by targeting readily available Graphics Processing Units capable of simulating many millions of interacting agents with performance which exceeds that of traditional CPU based simulators. FLAME GPU is an extended version of the FLAME (Flexible Large-scale Agent-based Modelling Environment) framework and is a mature and stable agent-based modelling simulation platform that enables modellers from various disciplines like economics, biology and social sciences to easily write agent-based models; for example, it is one of the modelling tools used for accelerating and scaling up of the Immune System Model developed for STriTuVaD. Importantly it abstracts the complexities of the GPU architecture away from modellers to ensure that modellers can concentrate on writing models without the
need to acquire specialist knowledge typically required to utilise GPU architectures.
This tutorial is aimed at the intermediate level. No knowledge of GPUs is required however basic knowledge multi agent modelling approaches is expected (i.e. formulating a problem as a set of individuals within a system) as well as understanding of XML document structure and basic programming ability.
By the end of the practical session, it is expected that the participants will understand how to write and execute a multi-agent model for FLAME GPU from scratch. Participants will leave with an appreciation of the key techniques, concepts, and algorithms which have been used.
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