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

计算科学界正在处理更大、更复杂的应用程序。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，无论应用程序是从头开始开发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，但即使如此广泛的知识也不能保证为特定问题选择出性能最好的方法。为了应对这些挑战，ANSER（自动化数值求解环境）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、特征求解器和图方法。总体方法可推广到涉及多个解决方案的任何情况，这些解决方案的性能随输入问题的属性而变化。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。通过将研究生和本科生纳入高性能软件的模型指导开发中，这项工作还影响了下一代科学劳动力的培训。ANSER，即自动数值求解环境，是一个基于web的开源平台，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，用于自动化识别问题特征的过程，创建性能模型（基于分析和机器学习方法的结合），并将它们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求解的数值包，但它被设计成可扩展到其他类型的数值方法，如图计算和n体模拟。除了传统的传播方法（开源软件发布和出版物），ANSER集成了科学计算文献的语义分析，以发现与目标库提供的类似的数值方法，并识别和连接我们的用户。ANSER提供了多个接口来支持不同类型的用户，包括学生、计算科学家和数字库开发人员。

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.