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: 1931387
• 负责人: Robert Harrison
• 金额: $ 109.93万
• 依托单位: SUNY at Stony Brook
• 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=1931387&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协作设计和开发一个软件框架，该框架实现了不规则和动态计算的高性能方法，而当前编程范式支持的不规则和动态计算很差。该框架标题为EPEXA（用于编程和执行极端应用程序的生态系统），将创建生产质量的，通用的，社区支持的，由社区支持的，开源的软件生态系统，攻击程序员生产力的双重挑战，以及针对现代高级高级计算机上的高级科学应用程序的便携式性能。 该团队采用科学驱动的共同设计，将过渡到生产新的编程模型的成功研究原型，并加快使用这些工具进行研究的计算机科学家和领域科学家社区的成长。 该项目将成功的原则证明之间的所谓“死亡谷”桥接到具有足够质量，绩效和社区支持的实施之间，以激发应用程序科学家和其他研究人员采用工具并将自己的努力投入到社区中。除了在框架开发方面的工作外，该项目还包括对博士后学者，毕业生和本科生以及教育，外展和科学社区参与活动的培训。特别是，特别是，新型强大的数据流程编程模型和相关的并行运行时直接解决了科学家在包括当前计算机系统的快速变化时，包括科学家面临的多个挑战，包括当前的计算机系统，包括当前的计算机系统。数据密集型和计算密集型应用程序均由一般编程模型以及针对多个后端或运行时系统的能力启用。还启用了共享和分布式内存计算机的新域特异性语言（DSL）的域科学家的创建。 EPEXA为最先进的软件环境的设计和开发做出了贡献，这些软件环境利用国家科学基金会在网络基础架构上的投资，以在所有学科中进行科学发现。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。

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

The Template Task Graph (TTG) - an emerging practical dataflow programming paradigm for scientific simulation at extreme scale

模板任务图（TTG）——一种新兴的实用数据流编程范例，用于极端规模的科学模拟

• 发表时间: 2020
• 期刊: 2020 IEEE/ACM 5th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2
• 作者: Bosilca, G.;Harrison, R.J.;Herault, T.;Javanmard, M.M.;Nookala, P.;Valeev, E.F.
• 通讯作者: Valeev, E.F.

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