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：小型：协作研究：矩阵代数软件自动调整的分类法PI Jessup，Elizabeth R. 科罗拉多大学博尔德分校CCF？ 0916474PI Norris，芝加哥博亚纳大学摘要：为了满足对高性能科学软件的需求，我们建议研究简化优化矩阵代数软件生成的方法。目前，代码开发过程的每个步骤都涉及许多选择，最需要数值计算、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过程。当利用现有的高性能数值库时，应用程序开发人员必须选择适当的数值例程，然后设计使这些例程在现有架构上高效运行的方法。一旦确定了数值例程，将其包含到更大的应用程序中的过程通常会很乏味或困难。然后，应用程序本身的调整呈现出无数的选项，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；使用关键数值算法的调优库；并且，不太频繁地使用基于编译器的源转换工具进行循环级优化。拟议研究的目标有三个。首先，我们将构建可用软件的分类，可用于构建高度优化的矩阵代数计算。该分类法将提供该任务所需的软件组件和编程工具的有组织的选集。该分类法将为从业者提供指南，帮助他们了解哪些内容可用于其编程任务、如何使用它以及各个部分如何组合在一起。它将建立并改进现有的数值软件集合，添加用于调整矩阵代数计算的工具。其次，我们将开发一套与该分类法结合使用的初始工具。特别是，我们将提供一个界面，该界面采用矩阵代数计算的高级描述，并使用分类法中的软件生成可定制的代码模板。该模板将在整个过程的所有步骤中为开发人员提供帮助，从基于基本线性代数子程序 (BLAS) 的代码的初始构建到该代码的全面优化。最初，这些工具将接受 MATLAB 原型并生成优化的 Fortran 或 C。最后，我们将通过改进分类中包含的一些工具来推进最先进的调优工具，从而扩大其功能范围问题领域和语言。