High Performance Algorithms for Electronic Materials

电子材料的高性能算法

• 批准号: 9525885
• 负责人: James Chelikowsky
• 金额: $ 59.3万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-12-01 至 1999-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9525885&HistoricalAwards=false
• 关键词: Performance; Algorithms; Electronic; Materials

9525885 Chelikowsky This computational materials science grant is co-funded by the Materials Theory Program in the Division of Materials Research and the New Technologies Program in the Advanced Scientific Computing Division. One principal investigator is a computational materials scientist while the other is a computational scientist. A primary objective of this research is to develop computer codes for materials modeling on advanced platforms which utilize the combined expertise of these two researchers. The goal of the research is to develop computational methods which fully utilize innovations in high performance computers in order to open new applications of electronic structure codes to complex materials. The focus of the research will be on real space methods through higher order finite difference techniques to electronic structure problems. Finite difference techniques coupled with ab initio pseudopotentials can provide a simple, yet accurate and efficacious, approach to complex systems. A few of the advantages of finite difference techniques over more traditional plane wave techniques include: finite difference techniques are far easier to implement than plane wave codes with no loss of accuracy, especially for parallel implementations; these techniques to not require the use of supercells for localized systems. No cell-cell interactions are present. Charged systems can be handled directly without artificial compensating backgrounds. Replication of vacuum is natural and minimized compared to extended basis sets; finite difference algorithms result in a minimum factor of 4-5 gain in speed over plane wave techniques for shared memory architectures. Significantly better performance is expected for massively parallel platforms; no fast-Fourier transforms are required. Global communications are minimized. Applications will center on electronic materials such as large clusters, liquids and amorphous s olids. Algorithm development will concentrate on designing parallel preconditioners for the eigenvalue problem, as well as the implementation of these techniques on various paralllel platforms. %%% This computational materials science grant is co-funded by the Materials Theory Program in the Division of Materials Research and the New Technologies Program in the Advanced Scientific Computing Division. One principal investigator is a computational materials scientist while the other is a computational scientist. A primary objective of this research is to develop computer codes for materials modeling on advanced platforms which utilize the combined expertise of these two researchers. The goal of the research is to develop computational methods which fully utilize innovations in high performance computers in order to open new applications of electronic structure codes to complex materials. ***

9525885 Chelikowsky此计算材料科学赠款由材料理论计划在材料研究部和高级科学计算部的新技术计划中共同资助。 一位主要研究者是计算材料科学家，而另一个是计算科学家。 这项研究的主要目的是在高级平台上开发用于材料建模的计算机代码，以利用这两位研究人员的组合专业知识。 该研究的目的是开发计算方法，这些方法可以充分利用高性能计算机中的创新，以便将电子结构代码的新应用程序开放到复杂的材料。 该研究的重点将通过高阶有限差异技术到电子结构问题来实现真实空间方法。 有限的差异技术和伪算量可以为复杂系统提供一种简单，准确而有效的方法。 有限差异技术比更传统的平面波技术的一些优点包括：有限的差异技术比没有准确性损失的平面波代码更容易实现，尤其是对于并行实现； 这些技术不需要将超级电池用于本地化系统。 不存在细胞电池相互作用。 无需人工补偿背景即可直接处理带电的系统。 与扩展基集相比，真空的复制是自然的和最小化的。 有限差算法导致平面波技术的最低速度增益为共享内存体系结构的速度4-5倍。 对于大规模并行平台，预期的性能要好得多。 无需快速转换。 全球通信被最小化。 应用将以电子材料为中心，例如大型簇，液体和无定形的OLID。 算法开发将集中于为特征值问题设计并行的预处理，以及在各种Paralllel平台上实施这些技术。 %% %%此计算材料科学赠款由材料理论计划在材料研究部和高级科学计算部的新技术计划中共同资助。 一位主要研究者是计算材料科学家，而另一个是计算科学家。 这项研究的主要目的是在高级平台上开发用于材料建模的计算机代码，以利用这两位研究人员的组合专业知识。 该研究的目的是开发计算方法，这些方法可以充分利用高性能计算机中的创新，以便将电子结构代码的新应用程序开放到复杂的材料。 ***