A survey on parallel and distributed Multi-Agent Systems

International audienceSimulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are, thus, often used to model and simulate complex systems. Whatever modeling type used, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis, mainly based on platform properties, then a performance comparison using the same agent model implemented on each platform

Towards a goal-oriented agent-based simulation framework for high-performance computing

Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

Load-Balancing for Large Scale Situated Agent-based Simulations

AbstractIn large scale agent-based simulations, memory and computational power requirements can increase dramatically because of high numbers of agents and interactions. To be able to simulate millions of agents, distributing the simulator on a computer network is promising, but raises some issues like: agents allocation and load-balancing between machines. In this paper, we study the best ways to automatically balance the loads between machines in large scale situations. We study the performance of two different applications with two different distribution approaches, and we show in our experimental results that some applications can automatically adapt the loads between machines and get alone a high performance in large scale simulations with one distribution approach than the other

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Agent-Based Modelling: Parallel and Distributed Simulation of Product Development Team

The successful collaboration of team members plays a crucial role in product development. Research of different teamwork factors may help understand the process, and computer simulation can be a suitable method of achieving this goal. This work aims to give an overview of agent-based modelling of product development teams and survey existing models that tackle this problem. Some tools and frameworks for agent-based modelling will be described with an emphasis on parallel and distributed architectures which are used to improve performance and enable very big and complex simulations

Simulating heterogeneous behaviours in complex systems on GPUs

Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU 1 [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x

Simulación paralela basada en agentes de sociedades pre-colombinas : Relaciones entre clanes

La utilización de paralelismo para resolver problemas de simulación es algo habitual. En este proyecto se ha desarrollado un modelo en paralelo que permita simular, a partir de agentes, la sociedad precolombina en la Patagonia. El modelo no partía de cero y en este proyecto se le ha añadido funcionalidades que simulen la vida del ser humano y la relación entre los clanes. Para finalizar se ha comprobado las mejoras en el rendimiento del modelo paralelo.La utilització del paral·lelisme per resoldre problemes de simulació és quelcom habitual. En aquest projecte s'ha desenvolupat un model en paral·lel que permet simular, fent servir agents, la societat precolombina de la Patagònia. El model no començava de zero i en aquest projecte s'han afegit funcionalitats que simulin la vida del ésser humà i la relacion entre clans. Per finalitzar s'ha comprovat les millores en el rendiment del model paral·lel.The use of parallelism to solve problems of simulation is something common. In this project i have developed a model based in parallel that let simulate, using agents, the Pre-Columbian society of Patagonia. The model was starting and in this project have been added functionalities to simulate human life and the relationship between clans. Finally, it has verified the performance enhancements provided by the parallel model

Simulación de comportamientos de sistemas distribuidos para obtener robustez y Auto-recuperación de fallas

219 - 241 p. :ilustraciones, libro electrónicoEste capítulo muestra algunos aspectos fundamentales para definir una simulación de comportamientos de sistemas distribuidos para lograr que un sistema sea robusto y se recupere a sí mismo de fallas. Se determina como tarea de interés la colección y la sincronización de datos en nodos conectados en una red y se mencionan algunos subproblemas de interés relacionados con la definición de las fallas y la obtención de las propiedades de robustez y recuperación de fallas. Se utilizaron sistemas multi-agente para diseñar la simulación porque permiten modelar componentes autónomos mediante el uso de un programa de agente.Capítulo 9ISBN: 978958580477

Development of a distributed agent based simulation benchmark using D-MASON

Agent-based models(ABM) are computational models that allow the simulation of systems focusing on the behavior and interactions of autonomous individuals (agents) within an environment, with the objective of recreating a complex system. As ABM simulations complexity grows, the computational resources needed to fulfill the simulations grow as well. Considering the fact that agents are independent entities and can be run in parallel, running ABMS on distributed systems brings a solution to the problem. This paper is focused on the implementation of a benchmark with DMASON ABM simulations framework, to evaluate its performance and compare it with other distributed ABMS frameworks.Els models basats en agents (ABM) són models computacionals que permeten la simulació de sistemes centrant-se en el comportament i les interaccions dels agents a dins d'un entorn, amb l'objectiu de recrear sistemes complexos. Quan la complexitat de les simulacions de ABM creix, els recursos computacionals necessaris per a dur a terme les simulacions també creixen. Considerant el fet de que els agents son entitats autònomes i poden executar-se en paral·lel, correr ABMS a sistemes distribuïts soluciona aquest problema. Aquest paper es centra en l'implementació d'un benchmark amb D-MASON, una plataforma per a simulacions d'ABM, per tal d'avaluar el seu rendiment i comparar-lo amb el d'altres plataformes similars.Los modelos basados en agentes (ABM) son modelos computacionales que permiten la simulación de sistemas centrándose en el comportamiento y las interacciones de agentes dentro de un entorno, con el objetivo de recrear sistemas complejos. Cuando la complejidad de las simulaciones de ABM crece, los recursos computacionales necesarios para llevar a cabo las simulaciones crece también. Considerando el hecho de que los agentes son entidades autónomas y pueden correr en paralelo, ejecutar ABMS en sistemas distribuidos soluciona este problema. Este paper se centra en la implementación de un benchmark con D-MASON, una plataforma para la simulación de ABM, con el objetivo de evaluar su rendimiento y compararlo con el de otras plataformas similares