SHF: Small: Collaborative Research: Automated Numerical Solver EnviRonment (ANSER)

SHF：小型：协作研究：自动数值求解器环境 (ANSER)

• 批准号: 1717854
• 负责人: Elizabeth Jessup
• 金额: $ 22.5万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717854&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Automated

The computational science community is tackling ever larger and more complex applications. The solution of the underlying mathematics problems requires using high-end parallel computing resources effectively, and delivering performance without degrading productivity is critical for the success of scientific computing. Converting mathematics from algorithms to high-quality implementations, however, is a difficult process, whether an application is developed from scratch or by leveraging existing software libraries. Modern numerical packages provide numerous solutions with widely varying performance. Selecting among these possibilities requires expertise in numerical computation, mathematical software, compilers, and computer architecture, but even such broad knowledge does not guarantee the selection of the best-performing method for a particular problem. In response to these challenges, ANSER (Automated Numerical Solver EnviRonment) automates the selection and configuration of algorithms such as sparse linear solvers, eigensolvers, and graph methods in the context of large-scale scientific and engineering applications. The overall approach is generalizable to any situation involving multiple solutions whose performance varies with input problem properties. ANSER increases developer productivity and promotes effective use of modern parallel architectures to solve large-scale scientific and engineering problems. This work also impacts the training of the next-generation scientific workforce by involving graduate and undergraduate students in this model-guided development of high-performance software. ANSER, the Automated Numerical Solver EnviRonment, is an open-source web-based platform that supports the development of both scientific applications and high-performance libraries. It selects, configures and, in some cases, generates implementations of high-performance numerical algorithms. ANSER defines a methodology for automating the process of identifying problem features, creating performance models (based on combining analytical and machine learning approaches), and employing them in creating and configuring numerical software. ANSER initially targets widely used numerical packages for nonlinear partial differential equations and solution of eigenvalue problems, but it is designed to be extensible to other types of numerical methods, such as graph computations and n-body simulations. In addition to traditional dissemination methods (open-source software releases and publications), ANSER integrates semantic analysis of scientific computing literature to discover numerical methods similar to those provided by the target libraries and to identify and connect with our users. ANSER provides multiple interfaces to support different types of users, including students, computational scientists, and numerical library developers.

计算科学界正在解决更大，更复杂的应用程序。基础数学问题的解决方案需要有效地使用高端平行计算资源，并且在不降低生产力的情况下提供绩效对于科学计算的成功至关重要。但是，无论是从头开始开发应用程序还是利用现有软件库，将数学从算法转换为高质量实现都是一个困难的过程。现代数值软件包提供了许多解决方案，其性能差异很大。在这些可能性之间进行选择需要在数值计算，数学软件，编译器和计算机体系结构方面的专业知识，但是即使是如此广泛的知识也不能保证选择特定问题的最佳表现方法。为了应对这些挑战，ANSER（自动数值求解器环境）在大型科学和工程应用程序的背景下自动选择和配置算法，例如稀疏线性求解器，Eigensolvers和图形方法。总体方法可以推广到任何涉及多种解决方案的情况，其性能随输入问题属性而变化。 Anser提高了开发人员的生产率，并促进了有效利用现代平行体系结构来解决大规模的科学和工程问题。这项工作还影响了下一代科学劳动力的培训，这使毕业生和本科生参与了这一模型引导的高性能软件的开发。 Anser是自动化的数值求解器环境，是一个基于开源的网络平台，可支持科学应用程序和高性能库的开发。 它选择，配置并在某些情况下会生成高性能数值算法的实现。 ANSER定义了一种方法，用于自动化识别问题功能，创建性能模型（基于结合分析和机器学习方法），并使用它们来创建和配置数值软件的方法。 ANSER最初针对非线性偏微分方程和特征值问题解决方案的数值软件包的目标，但其设计为对其他类型的数值方法的扩展，例如图形计算和N体模拟。除了传统的传播方法（开源软件发布和出版物）外，Anser还将语义分析集成了科学计算文献的语义分析，以发现与目标库提供的数字方法相似的数值方法，并识别和与用户联系。 ANSER提供了多个接口，以支持不同类型的用户，包括学生，计算科学家和数字图书馆开发人员。

项目成果

A Performance and Recommendation System for Parallel Graph Processing Implementations: Work-In-Progress

• DOI: 10.1145/3302541.3313097
• 发表时间: 2019-03
• 期刊: Companion of the 2019 ACM/SPEC International Conference on Performance Engineering
• 作者: Samuel D. Pollard;Sudharshan Srinivasan;Boyana Norris

Comparative Performance Modeling of Parallel Preconditioned Krylov Methods

并行预处理 Krylov 方法的比较性能建模

• DOI: 10.1109/hpcc-smartcity-dss.2017.4
• 发表时间: 2017
• 期刊: 2017 IEEE 19th International Conference on High Performance Computing and Communications
• 作者: Sood, Kanika;Norris, Boyana;Jessup, Elizabeth
• 通讯作者: Jessup, Elizabeth