SHF: Small: Collaborative Research: Lighthouse: Resource-Aware Advisor for High-Performance Linear Algebra

本研究将研究如何简化高性能矩阵代数软件的生产和使用。矩阵代数计算构成了各种领域仿真中最耗时的部分，降低这些计算的运行时间会对整体应用程序性能产生重大影响。然而，将矩阵代数从算法转换为高质量实现的过程是一个复杂的过程。在每一步中，代码开发人员都面临着无数的可能性，其中许多需要数值计算、数学软件、编译器和计算机体系结构方面的专业知识。为了应对这些困难，pisa开发了一个名为Lighthouse的原型分类法实现，它是软件包LAPACK中线性系统求解器例程的指南。它是第一个将矩阵代数软件本体与代码生成和调优功能相结合的框架。它的界面是为不同学科、职业水平和编程经验的用户设计的。pi将在一些新的方向上极大地扩展灯塔框架。首先，他们将构建一个可用于构建高度优化的数学应用程序的软件的一般分类。该分类法最初将为高性能矩阵代数和后来来自各种问题领域的其他数值软件提供有组织的软件组件本体。它将作为实践者的指南，帮助他们学习什么是可用于他们的数学规划任务的，如何使用它，以及各个部分如何组合在一起。其次，pisi将应用源代码分析和机器学习技术的组合，以完全自动化参数化模型的生成，给定代表性输入和简单的体系结构描述，可以评估以确定各种库中最能反映用户资源和性能需求的方法。这种自动化对于确保分类法足够全面而有用，并且准确地反映最新版本的数字库的特性和性能至关重要。最后，pi将通过改进分类法中包含的一些工具，扩大它们在问题领域和语言方面的功能范围，来推进最先进的调优工具。该项目将产生以下影响：提高应用程序的性能，使可用的计算资源得到更多的发现，并提高应用程序程序员的生产力；更好地理解架构和算法之间的相互作用；也是未来计算科学家的教育工具。

This research will study ways to ease the production anduse of high-performance matrix algebra software. Matrix algebracalculations constitute the most time-consuming part of simulationsin diverse fields, and lowering the runtimes of those computationscan have a significant impact on overall application performance. Theprocess of converting matrix algebra from algorithm to high-qualityimplementation is, however, a complex one. At each step, the codedeveloper is confronted with a myriad of possibilities, many requiringexpertise in numerical computation, mathematical software, compilers,and computer architecture. In response to these difficulties, the PIshave developed a prototype taxonomy implementation entitled Lighthouse,which is a guide to the linear system solver routines from the softwarepackage LAPACK. It is the first framework that combines a matrix algebrasoftware ontology with code generation and tuning capabilities. Itsinterface is designed for users across a spectrum of disciplines, careerlevels, and programming experience.The PIs will dramatically extend theLighthouse framework in a number of new directions. First, they willconstruct a general taxonomy of software that can be used to buildhighly-optimized mathematical applications. The taxonomy will initiallyprovide an organized ontology of software components for high-performancematrix algebra and later other numerical software from a variety ofproblem domains. It will serve as a guide to practitioners seekingto learn what is available for their mathematical programming tasks,how to use it, and how the various parts fit together. Second, the PIswill apply a combination of source code analysis and machine learningtechniques to fully automate the generation of parameterized models that,given representative inputs and a simple architecture description, can beevaluated to identify methods from various libraries that best reflectthe user's resource and performance requirements. This automation iscritical for ensuring that the taxonomy is comprehensive enough to beuseful and that it accurately reflects the features and performanceof the latest versions of numerical libraries. Finally, the PIs willadvance the state-of-the-art in tuning tools by improving some of thetools included in the taxonomy, broadening their ranges of functionalityin terms of problem domains and languages. This project will producethe following impacts: greater performance by applications, enablingboth more discovery with available computing resources and greaterproductivity of application programmers; greater understanding of theinteraction between architecture and algorithms; and an educational toolfor future computational scientists.