A Unified Framework for Large Scale Scientific Computing

大规模科学计算的统一框架

• 批准号: 0727600
• 负责人: Jun Zhang
• 金额: $ 19万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727600&HistoricalAwards=false
• 关键词: Unified; Framework; Large; Scale; Scientific

This research builds a unified computational framework for scalable and high efficiency solution of elliptic partial differential equations. The investigators develop a novel high-order multiscale multigrid computation methodology, which combines high accuracy computation and fast computing methods in a seamless way. This research work may impact many computational science and engineering and industry modeling and simulation applications. As U.S. high-tech industry moves from experiment-based design and development to computer-assisted design and development, higher performance numerical methods and faster computer simulation techniques will benefit U.S. industry by enabling design and development engineers to conduct quick verification to test their new ideas on computers, before committing to expensive experiments. These technologies are essential for the U.S. industry to maintain its leadership position in the competitive world market. Graduate students, including members from underrepresented groups, are trained to become the next generation researchers and educators with solid scientific computing skills. The technique simultaneously advances the numerical solution of partial differential equations in two fronts. One is to compute high accuracy solution by using high-order discretization methods, another is to compute the discrete solution in a minimum amount of computer time by using the fastest sparse linear system solvers. This unified framework advances the two fronts collectively by fusing the ideas and advantages of multiscale discretization and multigrid computations, to achieve the ultimate goal of computing accurate numerical solution at the minimum computer costs. It is the convergence of years of research work by many researchers in several different areas. This computational framework possesses high accuracy, high speed, high scalability, and delivers optimal efficiency for computing the numerical solution of elliptic partial differential equations.

这项研究为椭圆形偏微分方程的可扩展和高效率解决方案建立了一个统一的计算框架。研究人员开发了一种新型的高级多尺度多机计算方法，该方法结合了高精度计算和快速计算方法，以无缝的方式结合了快速计算方法。这项研究工作可能会影响许多计算科学，工程以及行业建模和模拟应用。随着美国高科技行业从基于实验的设计和开发转移到计算机辅助的设计和开发，更高的性能数值方法和更快的计算机仿真技术将通过使设计和开发工程师能够进行快速验证以在计算机上测试他们的新想法，并在承诺进行昂贵的实验。这些技术对于美国行业保持其在竞争性世界市场中的领导地位至关重要。研究生，包括来自代表性不足的团体的成员，受过培训，成为具有扎实科学计算技能的下一代研究人员和教育者。 该技术同时推进了两个方程中部分微分方程的数值解。一种是通过使用高级离散方法来计算高精度解决方案，另一个是通过使用最快的稀疏线性系统求解器以最小的计算机时间计算离散解决方案。这个统一的框架通过融合了多尺度离散化和多机计算的想法和优势，以实现以最低计算机成本计算准确的数值解决方案的最终目标，从而共同发展了这两个方面。这是许多不同领域的许多研究人员多年研究工作的融合。该计算框架具有高精度，高速，高扩展性，并提供了计算椭圆形偏微分方程的数值解决方案的最佳效率。