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.

该研究研究了有效模拟基于大型代理模型（ABM）的技术。 ABM越来越多地用于理解许多自然，建筑和社会系统中复杂的多尺度行为。尽管ABM具有捕获这些系统中复杂模型特征的必要结构，但这种结构使它们在计算上具有挑战性。当前用于台式计算和传统高性能计算扩展的技术无法有效处理这种计算复杂性。这严重限制了ABM的适用性。这项研究研究了旨在利用商品图形处理单元（GPU）可用的大量计算能力的新技术。它通过有效地将ABM模拟的超级计算民主化，从而大大扩展了基于代理的建模的可用性和适用性。此外，它可以在公共政策，救灾，药物治疗设计等，廉价的台式计算机上的“何种假设”方案进行虚拟测试。这项研究的主要挑战是对ABM计算的重新制定TOFIT GPU的数据并行模型。具体的研究主题包括代理数据的表示，代理运动的功能，复制，拆卸，通信，表示和操纵非空间代理网络，自适应行为，运行时用户互动，快速可视化，硬件和模型感知的自动代码优化，以及用于多GPU平台的多级并行性。 开发的技术应用于医学上的两个特定问题：结核病和全身性炎症反应综合征。 这些模型能够有效地模拟疾病病理学和新型治疗药物方案的核内测试。教育主题包括开发课程，通过开发ABM主题视频游戏的开发向K-12学生推广，研究生参与研究以及开发全面的传播网页。