ITR-(ASE+NHS)-(sim+dmc): Non-Equilibrium Surface Growth and the Scalability of Parallel Discrete-Event Simulations for Large Asynchronous Systems

ITR-(ASE NHS)-(sim dmc)：大型异步系统的非平衡表面生长和并行离散事件仿真的可扩展性

• 批准号: 0426488
• 负责人: Gyorgy Korniss
• 金额: $ 55万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0426488&HistoricalAwards=false
• 关键词: ITR; ASE+NHS; sim+dmc; Non; Equilibrium

This award was made on a proposal submitted to the Division of Materials Research under the Information Technology Research solicitation NSF-04-012. It supports collaborative computational and theoretical research in the area of statistical physics. The PIs propose to develop algorithms for parallel discrete-event simulations, study their scalability and performance, and implement them on chosen applications involving materials related phenomena and statistical physics. The proposed research contributes to Advances in Science and Enginering (ASE) and to National and Homeland Security (NHS) as the ITR National Priorities. The proposed research focuses on innovations in computational modeling and simulations (sim) using innovative approaches to develop efficient communication and synchronization protocols in data intensive simulations (dmc) as Technical Focus Areas.The PIs will focus on parallel discrete event simulation algorithms which can be applied to study large-scale systems, including cell phone communication networks, models of spread of diseases (e.g., smallpox), the electric power grid, dynamic phenomena in materials systems, and ecological invasion in spatially extended environments. To understand the scalability and performance of large-scale massively parallel discrete-event simulations, the simulation itself is viewed as a complex interacting system consisting of tens of thousands of processors and an underlying network, facilitating communications and synchronizations between the processors. Using powerful tools and frameworks from statistical physics and particularly non-equilibrium surface growth, such as coarse-graining and finite-size scaling, the PIs identify the relevant node-to-node processes on the network. The universal features of the resulting non-equilibrium and stochastic model describe the progress of the individual processors in the parallel simulation (the parallel simulation "landscape"). Based on the "morphological" properties of this landscape, the PIs propose to design and develop algorithms that simultaneously optimize simulation speed and data management. The PIs plan to apply developed algorithms to various problems, including the study of dynamic phenomena in selected materials, and ecological invasion in multi-species models with preemptive competition. Both of these exhibit long-living metastable states with subtle finite-size effects. Implementation of massively parallel simulations is crucial to extract and to understand the underlying temporal and spatial patterns in these systems.This award also supports the education and training of students at the graduate and undergraduate levels in simulation and modeling with applications in science and engineering. The PIs endeavor to involve members of under-represented groups in research and education supported by this award. The research includes a collaborative component with researchers at the Los Alamos National Laboratory (LANL), where scalable simulation of individual-based models (e.g., for modeling a bioterrorist attack using smallpox in human-contact networks) and critical infrastructures (e.g., vulnerability detection for the electric power-grid) is of particular interest to the Laboratory. Graduate students will also participate in visits to LANL through the Laboratory's summer student program, further contributing to the education and training value of the proposed research. Research Experience for Undergraduates (REU) at Rensselaer will support, in part, undergraduate participation in the proposed research. The PIs will continue to participate in outreach activities, primarily through classroom interaction with science oriented high-school seniors.%%%This award was made on a proposal submitted to the Division of Materials Research under the Information Technology Research solicitation NSF-04-012. It supports collaborative computational and theoretical research in the area of statistical physics. The PIs propose to use powerful conceptual tools from statistical physics to develop algorithms to enable computer simulation of complex systems, including various materials related phenomena. The PIs main focus will be on simulations characterized by changes in the system that occur as discrete spatially localized events as time advances. This encompasses a diverse range of systems from, for example, battlefield simulation to the changes in the orientation of magnetic moments as a magnetic material is heated. The PIs will implement these algorithms and apply them to materials related phenomena and statistical physics. The proposed research contributes to Advances in Science and Enginering (ASE) and to National and Homeland Security (NHS) as the ITR National Priorities. The proposed research focuses on innovations in computational modeling and simulations (sim) using innovative approaches to develop efficient communication and synchronization protocols in data intensive simulations (dmc) as Technical Focus Areas.This award also supports the education and training of students at the graduate and undergraduate levels in simulation and modeling with applications in science and engineering. The PIs endeavor to involve members of under-represented groups in research and education supported by this award. The research includes a collaborative component with researchers at the Los Alamos National Laboratory (LANL), where scalable simulation of individual-based models (e.g., for modeling a bioterrorist attack using smallpox in human-contact networks) and critical infrastructures (e.g., vulnerability detection for the electric power-grid) is of particular interest to the Laboratory. Graduate students will also participate in visits to LANL through the Laboratory's summer student program, further contributing to the education and training value of the proposed research. Research Experience for Undergraduates (REU) at Rensselaer will support, in part, undergraduate participation in the proposed research. The PIs will continue to participate in outreach activities, primarily through classroom interaction with science oriented high-school seniors.***

该奖项是根据信息技术研究招标NSF-04-012下提交给材料研究部的提案而授予的。它支持统计物理领域的协作计算和理论研究。pi建议开发并行离散事件模拟的算法，研究其可扩展性和性能，并在涉及材料相关现象和统计物理的选定应用中实现它们。拟议的研究有助于科学与工程进展（ASE）和国家和国土安全（NHS）作为ITR国家优先事项。提出的研究重点是计算建模和仿真（sim）的创新，使用创新方法在数据密集型仿真（dmc）中开发有效的通信和同步协议作为技术重点领域。pi将专注于并行离散事件模拟算法，该算法可应用于研究大规模系统，包括手机通信网络、疾病传播模型（例如天花）、电网、材料系统中的动态现象以及空间扩展环境中的生态入侵。为了理解大规模并行离散事件模拟的可扩展性和性能，模拟本身被视为一个复杂的交互系统，由数万个处理器和一个底层网络组成，促进处理器之间的通信和同步。使用来自统计物理学的强大工具和框架，特别是非平衡表面生长，如粗粒度和有限尺寸缩放，pi识别网络上相关的节点到节点过程。由此产生的非平衡和随机模型的普遍特征描述了并行模拟（并行模拟“景观”）中单个处理器的进展。基于该景观的“形态学”特性，pi建议设计和开发同时优化模拟速度和数据管理的算法。pi计划将开发的算法应用于各种问题，包括研究选定材料的动态现象，以及具有抢先竞争的多物种模型中的生态入侵。两者都表现出持久的亚稳态，具有微妙的有限尺寸效应。大规模并行模拟的实现对于提取和理解这些系统中潜在的时间和空间模式至关重要。该奖项还支持研究生和本科生在科学和工程应用模拟和建模方面的教育和培训。ppi努力让代表性不足的群体成员参与本奖项支持的研究和教育。该研究包括与洛斯阿拉莫斯国家实验室（Los Alamos National Laboratory， LANL）研究人员的合作组成部分，实验室对基于个人的模型（例如，在人类接触网络中使用天花进行生物恐怖袭击的建模）和关键基础设施（例如，电网的脆弱性检测）的可扩展模拟特别感兴趣。研究生也将通过实验室的暑期学生计划参加访问LANL，进一步促进拟议研究的教育和培训价值。伦斯勒的本科生研究经验（REU）将在一定程度上支持本科生参与拟议的研究。pi将继续参与外展活动，主要是通过与注重科学的高中高年级学生的课堂互动。该奖项是根据信息技术研究招标NSF-04-012向材料研究部提交的一份提案而授予的。它支持统计物理领域的协作计算和理论研究。pi建议使用统计物理学中强大的概念工具来开发算法，以实现复杂系统的计算机模拟，包括各种材料相关现象。pi的主要重点将放在模拟上，其特征是随着时间的推移，系统中的变化作为离散的空间局部事件发生。这包含了各种各样的系统，例如，战场模拟到磁性材料加热时磁矩方向的变化。pi将实现这些算法并将其应用于材料相关现象和统计物理。拟议的研究有助于科学与工程进展（ASE）和国家和国土安全（NHS）作为ITR国家优先事项。提出的研究重点是计算建模和仿真（sim）的创新，使用创新方法在数据密集型仿真（dmc）中开发有效的通信和同步协议作为技术重点领域。该奖项还支持研究生和本科生在科学和工程应用模拟和建模方面的教育和培训。ppi努力让代表性不足的群体成员参与本奖项支持的研究和教育。该研究包括与洛斯阿拉莫斯国家实验室（Los Alamos National Laboratory， LANL）研究人员的合作组成部分，实验室对基于个人的模型（例如，在人类接触网络中使用天花进行生物恐怖袭击的建模）和关键基础设施（例如，电网的脆弱性检测）的可扩展模拟特别感兴趣。研究生也将通过实验室的暑期学生计划参加访问LANL，进一步促进拟议研究的教育和培训价值。伦斯勒的本科生研究经验（REU）将在一定程度上支持本科生参与拟议的研究。pi将继续参与外展活动，主要是通过与注重科学的高中高年级学生的课堂互动