Scalable agent-based modelling with cloud HPC resources for social simulations

Abstract

New concepts like agent-based modelling are providing social scientists with new tools, more suited to their background than other simulation techniques. The success of this new trend will be strongly related to the existence of simulation tools capable of fulfilling the needs of these disciplines. Given the computational requirement of realistic agent-based models, high-performance computing infrastructure is often necessary to perform the calculations. At present, such resources are unlikely to be available to humanities researchers. Having developed Pandora, an open-source framework designed to create and execute large-scale social simulations in high-performance computing environments, this work presents an evaluation of the impact of cloud computing within this context. We find that the constraints of the cloud environment do not have a significant impact on the generic pattern of execution, providing a cost-effective solution for social scientists.

Towards a conceptual framework to support adaptative agent-based systems partitioning

“…However, it performs better when agents are grouped based on their positions and future goals. In [10], the paper describes the Pandora framework dedicated to implement scalable agentbased models and execute them on Cloud HPC resources. The authors tackle the distribution and parallelism issue of agent-based system as well as the benefits of using HPC cloud resources to perform more scalable simulations.…”

“…Also, in [7][8], the authors presents respectively a cluster-based approach and a proximity load balancing method to partition individual-oriented fish school simulations. In [10], the authors present the Pandora framework to distribute large-scale social simulations on the High Performance Computing (HPC) infrastructure. Based on the existing literature, most of the partitioning methods are used under specific requirements of a given application domain.…”

Automating NEURON Simulation Deployment in Cloud Resources

“…Many scientific communities have embraced or investigated the use of cloud resources for their research, including particle physics (Sadooghi et al 2015), astrophysics (Smith 2011), high-energy physics (Segal et al 2010; Taylor et al 2015), computational chemistry (Thackston and Fortenberry 2015b), chemical modeling for high–throughput drug discovery (Moghadam et al 2015), bioinformatics (Hanson et al 2014), medical imaging (Kagadis et al 2013), geophysics (Mudge et al 2011), social sciences (Wittek and Rubio-Campillo 2012), geochemistry (Huang et al 2014), genomic analysis (Ban et al 2015), and various projects at the Department of Energy (Yelick et al 2011). …”

Reducing Memory Requirements for Diverse Animated Crowds

“…It is desirable that crowd simulations maintain a steady refresh rate of 33 ms in which case optimization of each simulation stage is required. In order to maintain this refresh rate different approaches use parallelization techniques on multi core processors [Guy et al 2009;Richmond et al 2009] or clusters [Wittek and Rubio-Campillo 2012] while other approaches are focused on optimizing rendering [Dobbyn et al 2005;Millan and Rudomin 2006;Kavan et al 2008;Beacco et al 2012] or on reducing the overhead level of detail (LOD) selection produces [Hernández and Rudomin 2011].…”