Abstract
Multi-Agent Based Simulations (MABS) represents a relevant solution for the engineering and the study of complex systems in numerous domains (artificial life, biology, economy, etc.). However, MABS sometimes require a lot of computational resources, which is a major constraint that restricts the possibilities of study for the considered models (scalability, real-time interaction, etc.).Among the available technologies for HPC (High Performance Computing), the GPGPU (General-Purpose computing on Graphics Processing Units) proposes to use the massively parallel architectures of graphics cards as computing accelerator. However, while many areas benefit from GPGPU performances (meteorology, molecular dynamics, finance, etc.). Multi-Agent Systems (MAS) and especially MABS hardly enjoy the benefits of this technology: GPGPU is very little used and only few works are interested in it. In fact, the GPGPU comes along with a very specific development context which requires a deep and not trivial transformation process for multi-agents models. So, despite the existence of works that demonstrate the interest of GPGPU, this difficulty explains the low popularity of GPGPU in the MAS community.In this thesis, we show that among the works which aim to ease the use of GPGPU in an agent context, most of them do it through a transparent use of this technology. However, this approach requires to abstract some parts of the models, what greatly limits the scope of the proposed solutions. To handle this issue, and in contrast to existing solutions, we propose to use a nhybrid approach (the execution of the simulation is shared between both the processor and graphics card) that focuses on accessibility and reusability through a modeling process that allows to use directly GPU programming while simplifying its use. More specifically, this approach is based on a design principle, called GPU delegation of agent perceptions, consists in making a clear separation between the agent behaviors, managed by the processor, and environmental dynamics, handled by the graphics card. So, one major idea underlying this principle is to identify agent computations which can be transformed in new structures (e.g. in the environment) in order to distribute the complexity of the code and modulate its implementation. The study of this principle and the different experiments conducted show the advantages of this approach from both a conceptual and performances point of view. Therefore, we propose to generalize this approach and define a comprehensive methodology relying on GPU delegation specifically adapted to the use of massively parallel architectures for MABS.