Amazon EC2上基于代理的建模即服务:机遇与挑战

M. Kiran, Kabiru M. Maiyama, Haroon Mir, Bashir Mohammed, Ashraf Al-Ou'n
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引用次数: 3

摘要

为了成功地完成仿真任务,建模和仿真在很大程度上受到计算能力和资源可用性的影响。在本文中,我们研究了在云基础设施上部署FLAME框架,这是唯一一个在不同并行硬件架构上自动生成可并行代码的超级计算框架。该框架侧重于基于代理的建模(ABM)技术,该技术在高性能计算领域提出了各种挑战,以及这些挑战如何在云环境中反映出来。在计算上,这些模拟用互联软件编程是极其复杂的,使用大量的计算能力和架构挑战。高性能计算网格已经为其中的一些问题提供了解决方案,但仍然不足以解决建模者面临的大多数问题。本文讨论了执行这些模拟的计算问题和开放的挑战。呈现abm即服务,并提供一个可能的框架,说明如何使用平台作为服务后端来实现它。讨论了诸如内存、处理和时间等计算问题,突出了为非计算科学家提供这些服务的问题。
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Agent-Based Modelling as a Service on Amazon EC2: Opportunities and Challenges
Modelling and Simulation is heavily influenced by availability of computational power and resources, to successfully complete simulation tasks. In this paper, we investigate deploying the FLAME framework, the only supercomputing framework that automatically produces parallelisable code on different parallel hardware architectures, on cloud infrastructures. The framework focuses on agent-based modelling (ABM) technique which has presented various challenges in the high-performance computing fields and how these reflect in Cloud environments. Computationally these simulations are extremely complex to program with interconnected software, using massive amount of computational power and architectural challenges. High-performance computing grids have provided solutions to some of these issues, but are still not capable enough to solve most of the issues faced by the modelers. This paper discusses the computational problems of executing these simulations and open challenges. Presenting ABM-as-a-service with a possible framework on how this can be implemented with a platform as a service backend. Computational problems such as memory, processing and time are discussed highlighting the issues for enabling these services for non-computing scientists.
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