XPS:FULL:DSD: A novel framework for developing highly scalable and energy efficient guaranteed quality mesh generation for 3D and 4D finite element analysis

XPS:FULL:DSD：一种新颖的框架，用于开发高度可扩展且节能的保证质量网格生成，用于 3D 和 4D 有限元分析

• 批准号: 1439079
• 负责人: Nikos Chrisochoides
• 金额: $ 85万
• 依托单位: Old Dominion University Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1439079&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; novel; framework

Computational science is one of the "three pillars," complementing traditional theoretical and physical experimental studies in science and engineering. Parallel finite element mesh generation is a critical building block for this pillar and is becoming even more relevant for a growing number of engineering and life science applications. This project will set up a trajectory to deliver the first exascale-era unstructured finite element (FE) guaranteed quality Delaunay mesh generation.Why is FE mesh generation an important computational science tool? Many partial differential equations (PDEs) that are used to model complex multi-scale phenomena such as blood flow in the human body can only be solved by numerical approximation techniques. These techniques require the approximation of the domain by tessellating it into simpler geometric shapes such as triangles and tetrahedra in two and three dimensions, respectively. This project's focus is in 3D and 4D tessellation methods for life science applications such as blood flow simulations for Cerebro-Vascular Disease (CVD, or stroke), one of the leading natural causes of death in the US. In order to deliver exascale-era mesh generation, this project is set to achieve billion-way concurrency using substantially less electric power than today's state-of-the-art methods. This goal will be achieved by focusing on the following three objectives: (1) Integration of multiple parallel Delaunay mesh generation methods into a telescopic framework. (2) Development of application-specific models that describe the inherent concurrency and data access patterns of this framework. (3) Development of domain-specific energy-efficient and component-level (core and memory) power scaling for massively parallel mesh generation methods. This project will have broader impact in many other life science applications such as those related to the President's BRAIN Initiative. For example, the mesh generation techniques from this project can be customized to perform (by 2020) computer simulations to understand the circuitry of the human brain - an important milestone to help understand diseases like Parkinson's and Alzheimer's, which are expected to increase with the aging of the U.S. population. Finally, this project will contribute via the PI's MERIT outreach program for STEM education in K-12 and college students. The PI's goal is to "mentor, excite, and retain" students and help them to identify STEM areas of study that will transform them into responsible college graduates. The PI's MERIT Freshman Seminars (as opposed to traditional Freshman Seminars) re-connect students in the context of Research Experiences for Undergraduates (REU) activities (based on students interests) with highly visible national priorities such as the President's BRAIN Initiative. The MERIT program covers a variety of topics to reach as many students as possible with diverse interests and background. The goal is to prepare computational scientists to be entrepreneurs capable of understanding the ethical, economic, and research challenges in health care we face today.

计算科学是“三大支柱”之一，是对科学和工程领域传统理论和物理实验研究的补充。并行有限元网格生成是这一支柱的关键组成部分，并且在越来越多的工程和生命科学应用中变得越来越重要。该项目将建立一个轨迹来交付&amp；#64257；第一个百亿亿次非结构有限元（FE）保证质量的Delaunay网格生成。为什么有限元网格生成是一个重要的计算科学工具？许多用于模拟复杂多尺度现象（如人体血流）的偏微分方程（PDEs）只能通过数值近似技术来求解。这些技术需要通过将域细分成更简单的几何形状，如三角形和四面体，分别在二维和三维中逼近域。该项目的重点是生命科学应用的3D和4D镶嵌方法，如脑血管疾病（CVD或中风）的血流模拟，这是美国主要的自然死亡原因之一。为了实现百亿亿次的网格生成，该项目将使用比目前最先进的方法少得多的电力来实现十亿路并发。这一目标将通过以下三个目标来实现：(1)将多个并行Delaunay网格生成方法集成到一个伸缩框架中。(2)开发应用规格&amp；#64257；描述该框架固有的并发性和数据访问模式的C模型。(3)开发domain-spec &amp；#64257；C节能和组件级（核心和内存）功率缩放大规模并行网格生成方法。该项目将对许多其他生命科学应用产生更广泛的影响，例如与总统大脑计划相关的应用。例如，这个项目的网格生成技术可以定制，以执行（到2020年）计算机模拟，以了解人类大脑的电路——这是一个重要的里程碑，有助于了解帕金森症和阿尔茨海默氏症等疾病，这些疾病预计将随着美国人口的老龄化而增加。最后，该项目将通过PI的MERIT外展计划为K-12和大学生的STEM教育做出贡献。PI的目标是“指导、激励和留住”学生，并帮助他们确定STEM研究领域，从而将他们转变为负责任的大学毕业生。PI的MERIT新生研讨会（与传统的新生研讨会相反）在本科生研究经验（REU）活动（基于学生的兴趣）的背景下，将学生与高度可见的国家优先事项（如总统的大脑倡议）重新联系起来。MERIT计划涵盖了各种主题，以接触尽可能多的具有不同兴趣和背景的学生。我们的目标是将计算科学家培养成能够理解我们今天在医疗保健领域面临的伦理、经济和研究挑战的企业家。