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: 1717883
• 负责人: Boyana Norris
• 金额: $ 22.5万
• 依托单位: University of Oregon Eugene
• 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 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717883&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.

计算科学界正在处理越来越大和越来越复杂的应用程序。底层数学问题的解决需要有效地使用高端并行计算资源，而在不降低生产力的情况下提供性能对于科学计算的成功至关重要。然而，将数学从算法转换为高质量的实现是一个困难的过程，无论是从头开始开发应用程序还是利用现有的软件库。现代数值软件包提供了许多具有广泛不同性能的解决方案。在这些可能性中进行选择需要数值计算、数学软件、编译器和计算机体系结构方面的专业知识，但即使这样广泛的知识也不能保证为特定问题选择最佳性能的方法。为了应对这些挑战，ANSER（自动数值求解器环境）在大规模科学和工程应用的背景下自动选择和配置算法，如稀疏线性求解器，特征值求解器和图形方法。整体方法是可推广到任何情况下，涉及多个解决方案，其性能随输入问题的属性。 ANSER提高了开发人员的生产力，并促进了现代并行架构的有效使用，以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，让研究生和本科生参与这种模型指导的高性能软件开发。 ANSER，自动数值求解器环境，是一个开源的基于Web的平台，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，生成高性能数值算法的实现。 ANSER定义了一种方法，用于自动识别问题特征，创建性能模型（基于分析和机器学习方法的结合），并将其用于创建和配置数值软件。ANSER最初的目标是广泛使用的非线性偏微分方程和特征值问题的解决方案的数值包，但它的设计是可扩展到其他类型的数值方法，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），ANSER还集成了科学计算文献的语义分析，以发现与目标库提供的方法相似的数值方法，并识别和连接我们的用户。ANSER提供了多种接口来支持不同类型的用户，包括学生、计算科学家和数值库开发人员。

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