High Performance Algorithms for Electronic Materials

9525885 Chelikowsky这个计算材料科学补助金是由材料研究部门的材料理论计划和先进科学计算部门的新技术计划共同资助的。 一个主要研究者是计算材料科学家，而另一个是计算科学家。 这项研究的主要目标是开发计算机代码的先进平台上的材料建模，利用这两个研究人员的专业知识相结合。 该研究的目标是开发充分利用高性能计算机创新的计算方法，以开辟电子结构代码在复杂材料中的新应用。 研究的重点将是真实的空间方法通过高阶有限差分技术的电子结构问题。 有限差分方法结合从头算赝势可以为复杂系统提供一种简单、准确、有效的方法。 有限差分技术相对于更传统的平面波技术的一些优点包括： 有限差分技术比平面波编码更容易实现 没有精度损失，特别是对于并行实现; 这些技术不需要将超级单元用于局部系统。 没有 存在细胞间相互作用。 带电体系可直接处理，无需人工补偿本底。 真空的复制是自然的 并且与扩展基集相比最小化; 有限差分算法的结果在速度上的最小因子为4-5增益， 用于共享存储器架构的平面波技术。 对于大规模并行平台，预期会有显著更好的性能; 不需要快速傅立叶变换。 全球通信被最小化。 应用将集中在电子材料，如大团簇，液体和无定形固体。 算法开发将集中在设计并行预条件的特征值问题，以及这些技术在各种并行平台上的实施。 该计算材料科学基金由材料研究部的材料理论计划和高级科学计算部的新技术计划共同资助。 一个主要研究者是计算材料科学家，而另一个是计算科学家。 这项研究的主要目标是开发计算机代码的先进平台上的材料建模，利用这两个研究人员的专业知识相结合。 该研究的目标是开发充分利用高性能计算机创新的计算方法，以开辟电子结构代码在复杂材料中的新应用。 ***

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. ***