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: 1931347
• 负责人: Edward Valeev
• 金额: $ 100万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=1931347&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，为当前编程范例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(编程和执行极端应用程序的生态系统)，将创建一个生产质量、通用、社区支持的开源软件生态系统，以应对现代高性能计算机上高级科学应用程序的程序员生产力和可移植性能的双重挑战。利用科学驱动的联合设计，该团队将把一种新编程模型的成功研究原型转化为生产，并加速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增长。该项目在成功的原则证明和具有足够质量、性能和社区支持的实现之间架起了所谓的“死亡之谷”，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资于社区。除了框架开发工作外，该项目还包括对博士后学者、研究生和本科生的培训以及教育、推广和科学界的参与活动。具体地说，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(包括当前的大规模并行、混合和多核系统)时面临的多重挑战。数据密集型和计算密集型应用程序在一定程度上都是通过通用编程模型和面向多个后端或运行时系统的能力来实现的。还允许领域科学家为共享和分布式内存计算机创建新的领域特定语言(DSL)。EPEXA致力于设计和开发最先进的软件环境，利用国家科学基金会在网络基础设施方面的投资，实现跨所有学科的科学发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Composition of Algorithmic Building Blocks in Template Task Graphs

模板任务图中算法构建块的组成

• DOI: 10.1109/paw-atm56565.2022.00008
• 发表时间: 2022
• 期刊: 2022 IEEE/ACM Parallel Applications Workshop: Alternatives To MPI+X (PAW-ATM
• 作者: Herault, Thomas;Schuchart, Joseph;Valeev, Edward F.;Bosilca, George
• 通讯作者: Bosilca, George

Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment

• DOI: 10.1109/ipdps53621.2022.00086
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: J. Schuchart;Poornima Nookala;M. Javanmard;T. Hérault;Edward F. Valeev;G. Bosilca;R. Harrison

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.