Collaborative Research: SI2-SSI: ELSI-Infrastructure for Scalable Electronic Structure Theory

合作研究：SI2-SSI：ELSI-可扩展电子结构理论基础设施

• 批准号: 1450372
• 负责人: Lin Lin
• 金额: $ 50.4万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450372&HistoricalAwards=false
• 关键词: Collaborative; Research; SI2; SSI; ELSI

Predictive, so-called ab initio electronic structure calculations, particularly those based on the Kohn-Sham density functional theory (DFT) are now a widely used scientific workhorse with applications in virtually all sciences, and increasingly in engineering and industry. In materials science, they enable the computational ("in silico") design of new materials with improved properties. In biological or pharmacological research, they provide molecular-level insights into the function of macromolecules or drugs. In the search for new energy solutions, they give molecular-level insights into new solar cell designs, catalytic processes, and many others. A key bottleneck in many applications and calculations is the "cubic scaling wall" of the so-called Kohn-Sham eigenvalue problem with system size (i.e., the effort increases by a factor of 1,000 if the model size increases by a factor of 10). This project will establish an open source software infrastructure "ELSI" that offers a common, practical interface to initially three complementary solution strategies to alleviate or overcome the difficulty associated with solving the Kohn-Sham eigenvalue problem. ELSI will enable a broad range of end user communities, centered around different codes with, often, unique features that tie a specialized group of scientists to that particular solution, to easily incorporate state-of-the-art solution strategies for a key problem they all share. By providing these effective, accessible solution strategies, we will open up major areas for electronic structure theory where DFT based predictive methodologies are not applicable today. This will in turn open doors for new development in materials science, chemistry, and all related areas. Commitments to support ELSI exist from some of the most important electronic structure developer communities, as well as from industry and government leaders in high-performance computing. Thus, we will create a strong U.S. based infrastructure that leverages the large user and developer base from a globally active community developing DFT methods for materials research.ELSI will support and enhance three state-of-the-art approaches, each best suited for a specific problem range: (i) The ELPA (EigensoLvers for Petascale Applications) library, a leading library for efficient, massively parallel solution of eigenvalue problems (for small- and mid-sized problems up to several 1,000s of atoms), (ii) the OMM (Orbital Minimization Method) in a recent re-implementation, which circumvents the eigenvalue problem by focusing on a reduced, auxiliary problem (for systems in the several 1,000s of atoms range), and (iii) the PEXSI (Pole EXpansion and Selective Inversion) library, a proven reduced scaling (at most quadratic scaling) solution for general systems (for problems with 1,000s of atoms and beyond). By establishing standardized interfaces in a style already familiar to many electronic structure developers, ELSI will enable production electronic structure codes that use it to significantly reduce the "scaling wall" of the eigenvalue problem. First, ELSI will help them make efficient use of the most powerful computational platforms available. The target platforms are current massively parallel computers and multicore architectures, GPU based systems and future manycore processors. Second, the project will make targeted methodological improvements to ELPA, OMM, and PEXSI, e.g., a more effective use of matrix sparsity towards very large systems. The focus on similar computational architectures and similar methodological enhancements will lead to significant cross-fertilization and synergy between these approaches.

预测性的，所谓的从头计算电子结构计算，特别是那些基于科恩-沙姆密度泛函理论（DFT）的计算，现在是一个广泛使用的科学主力，几乎在所有科学中应用，并越来越多地在工程和工业中应用。在材料科学中，它们使具有改进性能的新材料的计算（“在硅片上”）设计成为可能。在生物学或药理学研究中，它们为大分子或药物的功能提供分子水平的见解。在寻找新能源解决方案的过程中，他们为新的太阳能电池设计、催化过程等提供了分子水平的见解。许多应用和计算中的关键瓶颈是所谓的Kohn-Sham特征值问题与系统大小的“立方缩放壁”（即，如果模型大小增加10倍，则工作量增加1，000倍）。该项目将建立一个开源软件基础设施“ELSI”，为最初的三个互补解决方案策略提供一个通用的实用接口，以减轻或克服与解决Kohn-Sham特征值问题相关的困难。ELSI将使广泛的最终用户社区，围绕不同的代码，通常，独特的功能，将一个专门的科学家小组，以特定的解决方案，很容易地将国家的最先进的解决方案战略，他们都共享的关键问题。通过提供这些有效的，可访问的解决方案策略，我们将打开电子结构理论的主要领域，其中基于DFT的预测方法是不适用的今天。这将为材料科学、化学和所有相关领域的新发展打开大门。一些最重要的电子结构开发人员社区以及高性能计算领域的行业和政府领导者都承诺支持ELSI。因此，我们将创建一个强大的美国基础设施，利用来自全球活跃社区的庞大用户和开发人员基础，开发用于材料研究的DFT方法。ELSI将支持和增强三种最先进的方法，每种方法最适合特定的问题范围：（一）ELPA（EigensoLvers for Petascale Applications）库，一个领先的高效库，特征值问题的大规模并行求解（对于高达数千个原子的中小型问题），（ii）OMM（轨道最小化方法）在最近的重新实施，它绕过了本征值问题的重点是减少，辅助问题（对于原子数在1,000的范围内的系统），以及（iii）PEXSI（极点扩展和选择性反演）库，一个经过验证的一般系统（对于原子数在1,000及以上的问题）的缩减缩放（最多二次缩放）解决方案。通过以许多电子结构开发人员已经熟悉的方式建立标准化接口，ELSI将使使用它的生产电子结构代码能够显着减少本征值问题的“缩放墙”。首先，ELSI将帮助他们有效利用最强大的计算平台。目标平台是当前的大规模并行计算机和多核架构，基于GPU的系统和未来的众核处理器。第二，该项目将对ELPA、OMM和PEXSI进行有针对性的方法改进，例如，更有效地使用矩阵稀疏性对非常大的系统。对类似的计算架构和类似的方法增强的关注将导致这些方法之间的显著交叉和协同作用。