Collaborative Research: Frameworks: Production quality Ecosystem for Programming and Executing eXtreme-scale Applications (EPEXA)

合作研究：框架：用于编程和执行超大规模应用程序的生产质量生态系统 (EPEXA)

• 批准号: 1931384
• 负责人: George Bosilca
• 金额: $ 199.57万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931384&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Production; quality

A team of researchers from three institutions will work collaboratively to design and develop a software framework that implements high-performance methods for irregular and dynamic computations that are poorly supported by current programming paradigms. The framework, titled EPEXA (Ecosystem for Programming and Executing eXtreme Applications), will create a production-quality, general-purpose, community-supported, open-source software ecosystem that attacks the twin challenges of programmer productivity and portable performance for advanced scientific applications on modern high-performance computers. Employing science-driven co-design, the team will transition into production a successful research prototype of a new programming model and accelerate the growth of the community of computer scientists and domain scientists employing these tools for their research. The project bridges the so-called "valley of death" between successful proofs of principle to an implementation with enough quality, performance, and community support to motivate application scientists and other researchers to adopt the tools and invest their own effort into the community. In addition to work on the framework development, the project includes training of postdoctoral scholars, graduate and undergraduate students as well as education, outreach and scientific community engagement activities.Specifically, the new powerful data-flow programming model and associated parallel runtime directly address multiple challenges faced by scientists as they attempt to employ rapidly changing computer technologies including current massively-parallel, hybrid, and many-core systems. Both data-intensive and compute-intensive applications are enabled in part by the general programming model and through the ability to target multiple backends or runtime systems. Also enabled is the creation by domain scientists of new domain-specific languages (DSLs) for both shared and distributed-memory computers. EPEXA contributes to the design and development of state-of-the-art software environments that leverage the National Science Foundation's investments in cyberinfrastructure to enable scientific discovery across all disciplines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

来自三个机构的研究人员团队将合作设计和开发一个软件框架，该框架将为当前编程范式支持不足的不规则和动态计算实现高性能方法。该框架名为EPEXA（Ecosystem for Programming and Executing eXtreme Applications），将创建一个生产质量，通用，社区支持的开源软件生态系统，解决程序员生产力和现代高性能计算机上先进科学应用程序的便携性能的双重挑战。 通过采用科学驱动的协同设计，该团队将把一个新编程模型的成功研究原型转化为生产，并加速计算机科学家和领域科学家社区的发展，他们将这些工具用于他们的研究。 该项目在成功的原则证明与具有足够质量、性能和社区支持的实施之间架起了所谓的“死亡之谷”的桥梁，以激励应用科学家和其他研究人员采用这些工具并将自己的努力投入到社区中。除了框架开发工作外，该项目还包括博士后学者、研究生和本科生的培训，以及教育、推广和科学界参与活动。具体而言，新的强大的数据流编程模型和相关的并行运行时直接解决了科学家在尝试使用快速变化的计算机技术时面临的多重挑战，包括当前的并行、混合、和众核系统。数据密集型应用程序和计算密集型应用程序都可以部分地通过通用编程模型以及针对多个后端或运行时系统的能力来实现。领域科学家还可以为共享和分布式内存计算机创建新的特定于领域的语言（DSL）。EPEXA致力于设计和开发最先进的软件环境，利用美国国家科学基金会在网络基础设施方面的投资，使所有学科的科学发现成为可能。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Distributed-memory multi-GPU block-sparse tensor contraction for electronic structure

• DOI: 10.1109/ipdps49936.2021.00062
• 发表时间: 2020-06
• 期刊: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: T. Hérault;Y. Robert;G. Bosilca;R. Harrison;C. Lewis;Edward F. Valeev;J. Dongarra

Pushing the Boundaries of Small Tasks: Scalable Low-Overhead Data-Flow Programming in TTG

突破小任务的界限：TTG 中的可扩展低开销数据流编程

• DOI: 10.1109/cluster51413.2022.00026
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Cluster Computing (CLUSTER
• 作者: Schuchart, Joseph;Nookala, Poornima;Herault, Thomas;Valeev, Edward F.;Bosilca, George
• 通讯作者: Bosilca, George

Understanding Recursive Divide-and-Conquer Dynamic Programs in Fork-Join and Data-Flow Execution Models

了解 Fork-Join 和数据流执行模型中的递归分治动态程序

• DOI: 10.1109/ipdpsw52791.2021.00069
• 发表时间: 2021
• 期刊: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Nookala, Poornima;Ahmad, Zafar;Javanmard, Mohammad Mahdi;Kong, Martin;Chowdhury, Rezaul;Harrison, Robert
• 通讯作者: Harrison, Robert

Comparing Distributed Termination Detection Algorithms for Modern HPC Platforms

比较现代 HPC 平台的分布式终止检测算法

• DOI: 10.15803/ijnc.12.1_26
• 发表时间: 2022
• 期刊: International Journal of Networking and Computing
• 作者: Bosilca, George;Bouteiller, Aurélien;Herault, Thomas;Fèvre, Valentin Le;Robert, Yves;Dongarra, Jack
• 通讯作者: Dongarra, Jack

Revisiting Credit Distribution Algorithms for Distributed Termination Detection

重新审视分布式终止检测的信用分配算法

• DOI: 10.1109/ipdpsw52791.2021.00095
• 发表时间: 2021
• 期刊: IEEE
• 作者: Bosilca, George;Bouteiller, Aurelien;Herault, Thomas;Le Fevre, Valentin;Robert, Yves;Dongarra, Jack
• 通讯作者: Dongarra, Jack