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

CCF - 0917324 SHF：小：合作研究：矩阵代数软件自动调优的分类学PI杰瑟普，伊丽莎白R。 科罗拉多大学博尔德分校CCF？0916474PI Norris，Boyana 芝加哥大学摘要：为了满足高性能scienti #64257;c软件的需求，我们建议研究如何简化优化矩阵代数软件的生产。目前，代码开发过程的每一步都涉及许多选择，大多数需要数值计算、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手头的架构上正确运行的方法。一旦数字例程被识别，将其包含到更大的应用程序中的过程通常是乏味或困难的。然后，应用程序本身的调优提供了大量的选项，这些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;使用调优的库进行关键数值算法;以及使用基于编译器的源代码转换工具进行循环级优化。拟议研究的目标有三个方面。首先，我们将构建一个可用软件的分类，这些软件可用于构建高度优化的矩阵代数计算。该分类法将提供一个有组织的选集的软件组件和编程工具所需的任务。该分类法将作为从业者的指南，帮助他们学习什么可用于他们的编程任务，如何使用它，以及如何将各个部分结合在一起。它将建立和改进现有的数值软件集，增加调整矩阵代数计算的工具。第二，我们将开发一组初始工具，与此分类法一起操作。特别是，我们将提供一个接口，它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。该模板将帮助开发人员完成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的过程的所有步骤。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法中包含的一些工具，扩大它们在问题域和语言方面的功能范围，来推进调优工具的最新发展。

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.