SHF: Small: Collaborative Research: Taxonomy for the Automated Tuning of Matrix Algebra Software

SHF：小型：协作研究：矩阵代数软件自动调整的分类法

• 批准号: 0916474
• 负责人: Boyana Norris
• 金额: --
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916474&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Taxonomy

CCF - 0917324 SHF: Small: Collaborative Research: Taxonomy for the Automated Tuning of Matrix Algebra SoftwarePI Jessup, Elizabeth R. University of Colorado at BoulderCCF ? 0916474PI Norris, Boyana University of ChicagoAbstract:In response to the need for high-performance scientific software, we propose to study ways to ease the production of optimized matrix algebra software. Each step of the code development process presently involves many choices, most requiring expertise in numerical computation, mathematical software, compilers, or computer architecture. The process of converting matrix algebra from abstract algorithms to high-quality implementations is a complex one. When leveraging existing high-performance numerical libraries, the application developer must select the appropriate numerical routines and then devise ways to make these routines run efficiently on the architecture at hand. Once the numerical routine has been identified, the process of including it into a larger application can often be tedious or difficult. The tuning of the application itself then presents a myriad of options generally centered around one or more of the following three approaches: manually optimizing code fragments; using tuned libraries for key numerical algorithms; and, less frequently, using compiler-based source transformation tools for loop-level optimizations. The goals of the proposed research are three-fold. First, we will construct a taxonomy of available software that can be used to build highly-optimized matrix algebra computations. The taxonomy will provide an organized anthology of software components and programming tools needed for that task. The taxonomy will serve as a guide to practitioners seeking to learn what is available for their programming tasks, how to use it, and how the various parts fit together. It will build upon and improve existing collections of numerical software, adding tools for the tuning of matrix algebra computations. Second, we will develop an initial set of tools that operate in conjunction with this taxonomy. In particular, we will provide an interface that takes a high-level description of a matrix algebra computation and produces a customizable code template using the software in the taxonomy. The template will aid the developer at all steps of the process from the initial construction of Basic Linear Algebra Subprogram (BLAS)-based codes through the full optimization of that code. Initially, the tools will accept a MATLAB prototype and produce optimized Fortran or C. Finally, we will advance the state-of-the-art in tuning tools by improving some of the tools included in the taxonomy, broadening their ranges of functionality in terms of problem domains and languages.

CCF -0917324 SHF：小：合作研究：用于自动调整的分类学矩阵代数软毛皮杰西普，伊丽莎白·R·科罗拉多大学的伊丽莎白·R·科罗拉多大学的分类学？ 0916474PI Norris，芝加哥大学Boyana University：响应对高性能Scienti＆＃64257; C软件的需求，我们建议研究减轻优化基质代数软件的生产方法。代码开发过程的每个步骤目前都涉及许多选择，大多数需要数字计算，数学软件，编译器或计算机体系结构方面的专业知识。将矩阵代数从抽象算法转换为高质量实现的过程是一个复杂的实现。当利用现有的高性能数字库时，应用程序开发人员必须选择适当的数值例程，然后设计方法以使这些例程运行E＆＃64259;在手头的体系结构上非常有效。一旦识别了数值例程，将其包含在较大应用程序中的过程通常可能是乏味的或di＆＃64259;邪教。然后，对应用程序本身的调整提出了多种选择，通常围绕以下三种方法中的一种或多种：手动优化代码片段；使用调谐的库作为密钥数值算法；而且，使用基于编译器的源转换工具进行循环级优化的频率较低。拟议研究的目标是三倍。首先，我们将构建可用软件的分类法，可用于构建高度优化的矩阵代数计算。分类法将提供该任务所需的软件组件和编程工具的组织选集。分类法将作为寻求学习其编程任务，如何使用它以及如何在一起的各种零件的实践者指南。它将建立并改善现有的数值软件集合，并添加用于调整矩阵代数计算的工具。其次，我们将开发一组与此分类法一起运行的初始工具。特别是，我们将提供一个接口，对矩阵代数计算进行高级描述，并使用分类法中的软件生成可自定义的代码模板。该模板将通过基于基本的线性代数子程序（BLAS）基于该代码的完整优化的基本线性代数子程序（BLA）代码的初始构建，从而帮助开发人员。最初，工具将接受MATLAB原型，并生产优化的Fortran或C。最后，我们将通过改善分类法中包含的一些工具来提高调整工具的最新调整工具，从而扩大其功能范围，以问题域和语言和语言。