A communication-aware framework for parallel spatially explicit agent-based models
A communication-aware framework for parallel spatially explicit agent-based models
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DOI：
10.1080/13658816.2013.771740
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发表时间：
2013-11
影响因子：
5.7
通讯作者：
Eric Shook;Shaowen Wang;Wenwu Tang
中科院分区：
文献类型：
--
作者：
Eric Shook;Shaowen Wang;Wenwu Tang
基于空间显式主体的并行模型（SE-ABM）利用高性能和并行计算来模拟复杂地理系统的空间动态。并行SE-ABM与CyberGIS的集成可以促进对大量计算资源和地理信息系统的直接访问，以支持模拟前后的分析和可视化。然而，为了从CyberGIS集成中获益，并行SE-ABM必须克服在并行计算环境中协调多个处理器核心的通信管理挑战。本文通过描述一个管理处理器间通信的通用框架来检查和解决这一挑战，以使并行SE-ABM能够扩展到高性能并行计算机。该框架综合了四个相互关联的组件：代理分组、线性域分解、感知通信的负载平衡策略和实体代理。基于模板代理模型的一系列计算实验结果表明，并行计算效率随着处理器间通信的增加而降低，特别是当将固定大小的模型扩展到数千个处理器内核时。因此，有效的沟通管理是至关重要的。该通信框架可以有效地扩展到2048个内核，证明了它能够有效地扩展到数千个处理器内核，以支持数十亿代理的模拟。在模拟场景中，通信感知负载平衡器减少了总体模拟时间和通信百分比，从而提高了总体计算效率。通过检查和解决处理器间通信挑战，本研究使并行SE-ABM能够有效地利用高性能计算资源，从而减少与CyberGIS协同集成的障碍。
Parallel spatially explicit agent-based models (SE-ABM) exploit high-performance and parallel computing to simulate spatial dynamics of complex geographic systems. The integration of parallel SE-ABM with CyberGIS could facilitate straightforward access to massive computational resources and geographic information systems to support pre- and post-simulation analysis and visualization. However, to benefit from CyberGIS integration, parallel SE-ABM must overcome the challenge of communication management for orchestrating many processor cores in parallel computing environments. This paper examines and addresses this challenge by describing a generic framework for the management of inter-processor communication to enable parallel SE-ABM to scale to high-performance parallel computers. The framework synthesizes four interrelated components: agent grouping, rectilinear domain decomposition, a communication-aware load-balancing strategy, and entity proxies. The results of a series of computational experiments based on a template agent-based model demonstrate that parallel computational efficiency diminishes as inter-processor communication increases, particularly when scaling a fixed-size model to thousands of processor cores. Therefore, effective communication management is crucial. The communication framework is shown to efficiently scale up to 2048 cores, demonstrating its ability to effectively scale to thousands of processor cores to support the simulation of billions of agents. In a simulated scenario, the communication-aware load-balancer reduced both overall simulation time and communication percentage improving overall computational efficiency. By examining and addressing inter-processor communication challenges, this research enables parallel SE-ABM to efficiently use high-performance computing resources, which reduces the barriers for synergistic integration with CyberGIS.