SPX: Collaborative Research: Dependence Programming and Optimization of Scalable Irregular Numerical Applications

SPX：协作研究：可扩展不规则数值应用的依赖编程和优化

• 批准号: 1725728
• 负责人: Zoran Budimlic
• 金额: $ 40万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725728&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Dependence; Programming

Dynamic and irregular applications, such as in the Multiresolution Adaptive Numerical Environment for Scientific Simulation framework, are notoriously hard to implement efficiently, especially on emerging complex and heterogeneous high-performance computing platforms. This is further compounded by the lack of suitable programming models capable of expressing these kinds of applications, while at the same time allowing the tools to efficiently map the applications on a variety of hardware. The intellectual merits of this project are in advancing the state of the art in dependence-based programming models, compiler technologies and runtime techniques that address these issues. The project's broader significance and importance are in laying down the intellectual foundations for the composition and optimization of irregular scalable algorithms, focusing on challenging and highly-significant spatial-tree algorithms. This project enables design and implementation of high-performance, portable irregular applications, as well as training of the future employees of companies and government who work in these domains.This project redefines the prevailing abstractions by unifying and extending the Concurrent Collections (CnC) dependence-based programming model with novel compiler and runtime techniques, and applying these to a very important class of dynamic, irregular numerical computations such as the ones found in the above simulation framework. Innovations in the programming model allow the programmers to separate the specification of the algorithm from a specification of how to efficiently map the application on a variety of different platforms with a variety of different tuning goals. Compiler innovations enable previously elusive optimizations of irregular applications, while runtime techniques enable efficient execution on modern, heterogeneous and distributed machines.

众所周知，动态和不规则的应用程序，例如在多分辨率的自适应数值环境中，很难有效地实施，尤其是在新兴的复杂和异质的高性能计算平台上。缺乏能够表达这些应用程序的合适编程模型，同时允许工具有效地绘制各种硬件上的应用程序，这进一步加剧了这一点。该项目的智力优点在于在基于依赖的编程模型，编译器技术和运行时技术方面提高最新技术的状态，以解决这些问题。 该项目的更广泛的意义和重要性在于为不规则可伸缩算法的组成和优化奠定知识基础，重点是具有挑战性且高度重要的空间树木算法。 该项目可以设计和实施高性能，便携式不规则应用，以及在这些领域工作的公司和政府的未来员工的培训。该项目通过统一和扩展并发收集（CNC）依赖性模型，并将这些依赖性的依赖性统一和运行型号的类别纳入这些依赖性，并将其基于irnig andigrignignignignignignignignignignignignignignignigrignignignignigrignignignignignignignignignignignignignignignignignignignigniq a Dynameriqual andirenignignired，该项目可以重新定义盛行的抽象。以上模拟框架。编程模型中的创新使程序员可以将算法的规范与如何在具有各种不同调谐目标的各种不同平台上有效映射应用程序的规范。编译器创新能够对不规则应用程序进行以前难以捉摸的优化，而运行时技术可以对现代，异质和分布式机器有效执行。