Collaborative Research: SI2-SSI: Sustaining Innovation in the Linear Algebra Software Stack for Computational Chemistry and Other Sciences

合作研究：SI2-SSI：计算化学和其他科学的线性代数软件堆栈的持续创新

• 批准号: 1550493
• 负责人: Robert van de Geijn
• 金额: $ 75.1万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550493&HistoricalAwards=false
• 关键词: Collaborative; Research; SI2; SSI; Sustaining

Scientific discovery now often involves computer simulation in addition to, or instead of, laboratory experimentation. This can accelerate, improve, and/or expand scientific insight, often at a great reduction in cost. Many such computer simulations spend much or most of their time solving linear algebra (matrix) problems. For these simulations, linear algebra problems constitute the most basic building blocks of the computation. As a result, software libraries (bundles of specialized code) that efficiently solve linear algebra problems fundamentally support sustained innovation in science. The project aims to create a next generation of software libraries for this domain and will make these libraries available to the scientific community as open source software that can be easily ported to current and future computer architectures. This will directly and indirectly impact discovery in academia, at the national labs, and in industry. The project will also impact affordable education through open course ware that is expected to reach a broad audience. The involvement of undergraduate and graduate students will strengthen the pool of qualified individuals trained to support scientific computing. The project involves research staff and students who are members of traditionally underrepresented groups.The BLAS (Basic Linear Algebra Subprograms) are well-known routines that provide standard building blocks for performing basic vector and matrix operations. The Level 1 BLAS perform scalar, vector and vector-vector operations, the Level 2 BLAS perform matrix-vector operations, and the Level 3 BLAS perform matrix-matrix operations. Because the BLAS are efficient, portable, and widely available, they are commonly used in the development of high quality linear algebra software, such as the well-known Linear Algebra PACKage (LAPACK), as an example. However, the BLAS libraries that exist today have not evolved to new computing architectures, and hence do not perform as well as they could. The technical goal and scope of this project, therefore, is to develop a new high-performance dense linear algebra library with broad functionality that can be easily ported to current and future multi-core and many-core processors. The project builds on the BLAS-like Library Instantiation Software (BLIS) effort that has exposed low-level primitives that facilitate the high-performance implementation of BLAS. By implementing the higher-level dense linear algebra functionality in terms of these low-level primitives, portable high performance will be achieved for higher-level functionality needed by many scientific computing applications. Contributions will include the to-be developed techniques for implementing such software, the resulting open source software, and pedagogical artifacts that will include open course ware.

科学发现现在经常涉及计算机模拟，而不是实验室实验。这可以加速，改善和/或扩大科学洞察力，通常会大大降低成本。许多这样的计算机模拟花费大量或大部分时间来解决线性代数（矩阵）问题。对于这些模拟，线性代数问题构成了计算的最基本构建块。因此，有效解决线性代数问题的软件库（专用代码包）从根本上支持科学的持续创新。该项目旨在为该领域创建下一代软件库，并将这些库作为开源软件提供给科学界，这些软件可以很容易地移植到当前和未来的计算机体系结构中。这将直接或间接地影响学术界、国家实验室和工业界的发现。该项目还将通过开放式课件影响负担得起的教育，预计将覆盖广泛的受众。本科生和研究生的参与将加强受过支持科学计算培训的合格人才库。 该项目涉及传统上代表性不足的群体的研究人员和学生。BLAS（基本线性代数子程序）是众所周知的例程，为执行基本向量和矩阵运算提供标准构建块。1级BLAS执行标量、向量和向量-向量运算，2级BLAS执行矩阵-向量运算，3级BLAS执行矩阵-矩阵运算。由于BLAS是高效的、可移植的和广泛可用的，它们通常用于高质量线性代数软件的开发，例如众所周知的线性代数包（LAPACK）。然而，今天存在的BLAS库还没有发展到新的计算架构，因此没有表现得很好。因此，该项目的技术目标和范围是开发一个新的高性能密集线性代数库，具有广泛的功能，可以轻松地移植到当前和未来的多核和众核处理器。该项目建立在类似于BLAS的库实例化软件（BLIS）的基础上，该软件已经公开了促进BLAS高性能实现的低级原语。通过根据这些低级原语来实现高级密集线性代数功能，将为许多科学计算应用所需的高级功能实现可移植的高性能。贡献将包括将开发的技术，用于实施这样的软件，由此产生的开放源码软件，教学文物，将包括开放课程软件。