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: 1725611
• 负责人: Louis-Noel Pouchet
• 金额: $ 20万
• 依托单位: Colorado State 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 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1725611&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.

动态和不规则的应用程序，如多分辨率自适应数值环境科学仿真框架，是众所周知的难以有效地实现，特别是在新兴的复杂和异构的高性能计算平台。由于缺乏能够表达这些类型的应用程序的合适的编程模型，同时允许工具在各种硬件上有效地映射应用程序，这进一步加剧了问题。这个项目的智力价值在于推进了基于依赖的编程模型、编译器技术和解决这些问题的运行时技术的最新发展。 该项目更广泛的意义和重要性在于为不规则可扩展算法的组成和优化奠定了知识基础，重点是具有挑战性和高度重要性的空间树算法。 该项目使设计和实现高性能，可移植的非常规应用程序，以及培训未来的员工的公司和政府谁在这些领域的工作。该项目重新定义了流行的抽象统一和扩展的并发集合（CnC）依赖为基础的编程模型与新的编译器和运行时技术，并将这些应用到一个非常重要的类的动态，不规则的数值计算，例如在上述模拟框架中发现的数值计算。编程模型中的创新允许程序员将算法的规范与如何在具有各种不同调优目标的各种不同平台上有效地映射应用程序的规范分开。更快的创新使以前难以实现的不规则应用程序的优化成为可能，而运行时技术则使现代、异构和分布式机器上的高效执行成为可能。

项目成果

Efficient Execution of Dynamic Programming Algorithms on Apache Spark

• DOI: 10.1109/cluster49012.2020.00044
• 发表时间: 2020-09
• 期刊: 2020 IEEE International Conference on Cluster Computing (CLUSTER)
• 作者: M. Javanmard;Zafar Ahmad;J. Zola;L. Pouchet;R. Chowdhury;R. Harrison

Deriving parametric multi-way recursive divide-and-conquer dynamic programming algorithms using polyhedral compilers

使用多面体编译器导出参数多路递归分治动态规划算法

• DOI: 10.1145/3368826.3377916
• 发表时间: 2020
• 期刊: CGO 2020: Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization
• 作者: Javanmard, Mohammad Mahdi;Ahmad, Zafar;Kong, Martin;Pouchet, Louis-Noël;Chowdhury, Rezaul;Harrison, Robert
• 通讯作者: Harrison, Robert

Optimizing Coherence Traffic in Manycore Processors Using Closed-Form Caching/Home Agent Mappings

• DOI: 10.1109/access.2021.3058280
• 发表时间: 2021
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Steve Kommrusch;Marcos Horro;L. Pouchet;Gabriel Rodríguez;J. Touriño