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

计算科学是“三大支柱”之一，补充了科学和工程中传统的理论和物理实验研究。并行有限元网格生成是这一支柱的关键组成部分，并且对于越来越多的工程和生命科学应用变得更加相关。该项目将建立一个轨道，以提供fi第一个Exascale时代的非结构化有限元（FE）保证了高质量的Delaunay网格生成。为什么FE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（如人体内的血液流动）的偏微分方程（PDE）只能通过数值逼近技术求解。这些技术需要通过将域镶嵌成更简单的几何形状（例如分别在二维和三维中的三角形和四面体）来近似域。该项目的重点是生命科学应用的3D和4D细分方法，例如脑血管疾病（CVD或中风）的血流模拟，这是美国主要的自然死亡原因之一。为了提供亿级网格生成，该项目将使用比当今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方面来实现：（1）将多种并行Delaunay网格生成方法集成到一个伸缩框架中。(2)开发特定于应用程序的c模型，描述此框架的固有并发性和数据访问模式。(3)针对大规模并行网格生成方法，开发领域专用的节能和组件级（内核和内存）功率缩放。该项目将在许多其他生命科学应用中产生更广泛的影响，例如与总统的大脑计划有关的应用。例如，该项目的网格生成技术可以定制为执行（到2020年）计算机模拟，以了解人类大脑的电路-这是帮助了解帕金森病和阿尔茨海默病等疾病的重要里程碑，预计这些疾病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，激发和留住”学生，并帮助他们确定STEM学习领域，将他们转变为负责任的大学毕业生。PI的MERIT新生研讨会（而不是传统的新生研讨会）在本科生研究经验（REU）活动（基于学生的兴趣）的背景下重新连接学生与高度可见的国家优先事项，如总统的大脑倡议。MERIT计划涵盖了各种主题，以达到尽可能多的学生与不同的兴趣和背景。目标是让计算科学家成为能够理解我们当今面临的医疗保健领域的道德、经济和研究挑战的企业家。

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.