EAGER: Collaborative Research: Lighthouse: A User-Centered Web System for High-Performance Software Development

EAGER：协作研究：Lighthouse：用于高性能软件开发的以用户为中心的 Web 系统

• 批准号: 1550163
• 负责人: Elizabeth Jessup
• 金额: $ 15万
• 依托单位: University of Colorado at Boulder
• 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: 2015-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550163&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Lighthouse; User

The role of computing in science and engineering has been growing steadily in recent years, leading to ever larger and more complex problems. Their solution requires high-performance resources, which has resulted in many efforts to produce new mathematical approaches, programming models and languages, software libraries, and runtime environments for advanced parallel computers. This vast and growing number of solution methods presents a challenge to scientists who do not want to invest the time needed to locate and learn new computational tools. The PIs are developing Lighthouse, a taxonomy-based system, that equips practitioners with easy discovery and use of high-performance software solutions to a variety of common problems arising in scientific and engineering applications. Lighthouse also integrates information about the performance of multiple implementations of numerical algorithms. It thus allows users to readily find and start using the best method for addressing a particular challenge with the computers available to them. Less time spent in learning numerical packages and faster scientific simulations directly increases scientific productivity.Lighthouse is a web-based framework that offers a solution to two main research challenges. First, it aids in the creation of complex high-performance applications that leverage the latest advances in applied mathematics and computer science research. Second, it facilitates effective intra- and cross-domain communication for computer scientists, applied mathematicians, computational scientists, and students as they tackle scientific and engineering computing problems. Lighthouse incorporates an extensible taxonomy that enables different types of searches for numerical solutions depending on a user?s level of expertise. It then provides a mechanism for generating code templates based on the search results. Lighthouse thereby reduces the substantial learning curves associated with using state-of-the art high-performance software libraries. The Lighthouse system integrates taxonomy information for an expanding number of high-performance numerical libraries that presently comprises the linear algebra packages LAPACK, PETSc, and SLEPc. Lighthouse employs machine learning methods to automatically classify routines based on their performance attributes. In this way, it helps to provide more accurate search results that take into account not only functionality but also problem scale, performance, and available computational resource requirements. Finally, Lighthouse improves communication within and between communities by providing an accessible web-based interface to taxonomies of numerical packages that includes documentation, references, and performance information.

近年来，计算在科学和工程中的作用一直在稳步增长，导致更大，更复杂的问题。 他们的解决方案需要高性能的资源，这导致了许多努力为高级并行计算机生成新的数学方法，编程模型和语言，软件库和运行时环境。这种大量且越来越多的解决方案方法给不想投入定位和学习新计算工具所需时间的科学家带来了挑战。 PI正在开发基于分类的系统灯塔，该系统使从业者易于发现并使用高性能软件解决方案，以解决科学和工程应用中引起的各种常见问题。 Lighthouse还集成了有关数值算法多种实现的性能的信息。 因此，它使用户可以轻松地找到并开始使用最佳方法来解决特定挑战的计算机。在学习数值软件包和更快的科学模拟上花费的时间更少，直接提高了科学生产力。Lighthouse是一个基于网络的框架，可为两个主要研究挑战提供解决方案。 首先，它有助于创建复杂的高性能应用程序，以利用应用数学和计算机科学研究的最新进展。 其次，它促进了计算机科学家，应用数学家，计算科学家和学生解决科学和工程计算问题的有效内和跨域通信。 Lighthouse结合了可扩展的分类法，该分类法可以根据用户的专业水平来实现不同类型的数值解决方案搜索。 然后，它提供了一种基于搜索结果生成代码模板的机制。 灯塔因此减少了与使用最先进的高性能软件库相关的大量学习曲线。 Lighthouse系统集成了分类信息，以扩大数量的高性能数值库，该库目前包括线性代数套件Lapack，PETSC和SLEPC。 Lighthouse采用机器学习方法根据其性能属性自动对例程进行分类。 通过这种方式，它有助于提供更准确的搜索结果，不仅要考虑功能，还考虑了问题规模，性能和可用的计算资源要求。最后，灯塔通过为包括文档，参考和性能信息的数值软件包分类法提供了可访问的基于Web的界面，从而改善了社区内部和社区之间的通信。