CAREER: Towards Interactive Simulation of Giga-Scale Agent-Based Models on Graphics Processing Units

职业：在图形处理单元上进行基于千兆级代理的模型的交互式仿真

• 批准号: 1013278
• 负责人: Roshan D'souza
• 金额: $ 38.7万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-16 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1013278&HistoricalAwards=false
• 关键词: CAREER; Towards; Interactive; Simulation; Giga

This research investigates techniques for efficient simulation of large scale agent-based models (ABMs). ABMs are increasingly being used to understand complex multi-scale behaviors in many natural, built and social systems. Although ABMs have the necessary structure to capture complex model characteristics in these systems, this very structure makes them computationally challenging. Current techniques for desktop computing and extensions to traditional high performance computing are incapable of efficiently handling this computational complexity. This has severely limited the applicability of ABMs. This research investigates novel techniques designed to leverage the massive computing power available on commodity graphics processing units (GPUs). It greatly expands the availability and applicability of agent-based modeling by effectively democratizing super computing for ABM simulation. Furthermore, it enables virtual testing of "what-if" scenarios in public policy, contingency planning for disaster relief, drug therapy design etc., on inexpensive desktop computers at realistic levels of detail. The main challenge in this research is the re-formulation of ABM computation tofit the data-parallel model of GPUs. Specific research topics include representation of agent data, functions for agent motion, replication, decimation, communication, representation and manipulation of non-spatial agent networks, adaptive behaviors, run-time user interaction, fast visualization, hardware and model-aware automatic code optimization, and multi-level parallelism for multi-GPU platforms. The techniques developed are being applied to two specific problems in medicine: simulation of tuberculosis and systemic inflammatory response syndrome. These models enable efficient simulation of disease pathology and in-silico testing of novel therapeutic drug protocols. Educational topics include development of courses, outreach to K-12 students through development of ABM themed video games, undergraduate involvement in research, and the development of a comprehensive dissemination web page.

本研究探讨了大规模智能体模型（ABMs）的高效仿真技术。ABMs越来越多地被用于理解许多自然、建筑和社会系统中复杂的多尺度行为。尽管ABMs具有必要的结构来捕获这些系统中的复杂模型特征，但这种结构使得它们在计算上具有挑战性。当前的桌面计算技术和传统高性能计算的扩展无法有效地处理这种计算复杂性。这严重限制了ABMs的适用性。本研究调查了旨在利用商品图形处理单元（gpu）上可用的大量计算能力的新技术。它通过将超级计算有效地大众化，极大地扩展了基于智能体的建模方法的可用性和适用性。此外，它可以在廉价的台式电脑上以现实的细节水平对公共政策、救灾应急计划、药物治疗设计等方面的“假设”情景进行虚拟测试。本研究的主要挑战是重新制定ABM计算以适应gpu的数据并行模型。具体的研究课题包括智能体数据的表示、智能体运动的功能、复制、抽取、通信、非空间智能体网络的表示和操作、自适应行为、运行时用户交互、快速可视化、硬件和模型感知的自动代码优化以及多gpu平台的多级并行性。所开发的技术正在应用于医学上的两个具体问题：模拟结核病和全身炎症反应综合征。这些模型能够有效地模拟疾病病理和新型治疗药物方案的计算机测试。教育主题包括课程开发，通过开发ABM主题视频游戏向K-12学生推广，本科生参与研究，以及开发综合传播网页。